This gap between what happened and why it happened is a common challenge in analytics. Standard dashboards surface general patterns but struggle to explain their context.

Custom dimensions offer a way to capture that missing context by attaching meaningful attributes to visits and actions. Details, such as user roles, content categories or subscription tiers, can transform raw activity into insight. 

This article explores what custom dimensions are, how they work in Matomo and how to set them up for clearer, more relevant reporting.

What are custom dimensions?

Custom dimensions are extra pieces of information attached to visits or actions in an analytics tool. Instead of relying only on default fields, such as page URL or traffic source, an analyst can store tailored attributes that matter to the business, then use them in reports for deeper insight.

Each custom dimension holds a name and a value. During tracking, the value is sent with the hit, and Matomo stores it alongside standard metrics. When reports run, Matomo groups and filters data by these values, which keeps the analysis accurate and consistent.

For example, a “subscription tier” custom dimension can record whether a visitor is on a Free, Pro or Enterprise plan. Another might capture “Content type,” such as article, video or product page. 

Custom dimensions can be set up to avoid personal data, which helps teams measure behaviour without tracking names or contact details. They also give analysts more say in how results are grouped in reports.

Common use cases

Custom dimensions are most useful when they add context that standard metrics miss. The examples below show how a few extra fields can turn log data into clear, practical findings.

Content performance tracking

Editors can tag visits with content author, category or content type. Reports then reveal which authors keep visitors engaged, which categories attract new audiences and whether articles, videos or product pages drive the most conversions.

User segmentation

Marketers often track subscription tier, user type or acquisition channel as custom dimensions. A tier such as Free, Pro or Enterprise can be followed through funnels to compare feature usage, upgrade rates and campaign performance with clear, transparent splits.

Ecommerce insights 

Stores can attach product attributes, such as brand or collection, along with the customer lifetime value band. That makes it easier to compare groups that spend more or stay longer, without storing personal data.

Technical tracking 

Teams can record a page load time band or an error type. Lining those values up against clicks and conversions shows where slow pages or repeating errors cause visitors to drop off.

Implementing and managing custom dimensions

Implementing custom dimensions in Matomo follows two stages: define the dimension, then send values with each relevant hit. A little planning at this point protects accuracy, performance and privacy later.

Step 1: Plan and create the dimension

Before creating a new dimension, teams decide whether it should describe an entire visit (visit-scoped) or a single interaction, such as a page view or event (action-scoped). 

In Matomo, administrators click: 

• The Administration page (cog icon)
• Measurables or Websites (depending on setup) in the left-side menu
• Custom Dimensions

They can then add a name, choose the scope and set the dimension as active. 

Because each site has a limited number of slots per scope and dimensions usually can’t be deleted, only deactivated, most teams reserve them for stable concepts, such as subscription tier or content group, rather than volatile labels.

Step 2: Track values from the site or app

For sites that use the JavaScript tracker, custom dimensions are attached to hits through the _paq queue.

This simple example records a visitor’s plan:

This call runs before the relevant trackPageView or event, and Matomo stores the value alongside the standard metrics for that visit or action.

Matomo Tag Manager offers another route and keeps tracking logic in one place. A variable first captures the value, like a data layer field that holds userRole. In the Matomo Configuration Variable, the Custom Dimensions section maps a dimension index to that variable. When a tag that uses this configuration fires, it sends the custom dimension value with the hit. In preview mode, teams can check the container and see those values in the request before publishing any changes.

Server-side systems, background jobs or mobile apps that call the HTTP Tracking API add custom dimensions with dimension{id} parameters, such as dimension2=Enterprise. Separate ranges support visit and action scopes, which help keep imports structured and efficient.

Step 3: Maintain and validate

After tracking is live, teams should watch reports and logs for empty rows or odd values.

Action dimensions can also take values from URLs or page titles through extraction rules. That approach cuts down on code edits and makes it clear where each value comes from.

Periodic reviews of active dimensions, along with consent and data minimisation settings, help ensure the implementation remains accurate, privacy-friendly and easy to extend.

How custom dimensions affect analytics and reporting

Once custom dimensions begin collecting data, they become part of Matomo’s standard reporting flow. 

Each dimension appears in dedicated reports where metrics are grouped by the stored values. It keeps analysis consistent and makes it clear how attributes (like subscription tier or content type) relate to behaviour and results.

Matomo processes visit-scoped and action-scoped dimensions differently:

• Visit-level dimensions describe the whole session, so reports summarise complete visits and conversions by each value. 
• Action-level dimensions attach to individual events, page views or downloads. In these reports, a single visit can contribute multiple rows, which helps expose detailed patterns, like which content category generated the most downloads or form submissions.

Custom dimensions can also feed Custom Reports. Analysts can add a dimension as a row or column, then filter by action type to focus on events, downloads or other specific interactions. This level of control, combined with clear scopes, supports accurate reporting and efficient workflows without obscuring how Matomo stores and processes the underlying data.

Privacy and compliance considerations

Custom dimensions can touch personal data, depending on implementation, so they form an important part of privacy and compliance work.

Under GDPR and similar laws, any field that can identify or single out a person needs a lawful basis, a clear purpose and suitable safeguards. In practice, this means planning dimensions with legal and privacy teams, as well as analysts.

Data minimisation, careful consent management and anonymisation are at the core of a privacy-forward and compliant implementation.

For custom dimensions, that often means recording stable, non-identifying values, such as subscription tier or an internal segment label, instead of names or email addresses. It also means linking records with a pseudonymous ID.

Data minimisation keeps each dimension tied to a single purpose. Retention rules and deletion processes then clear out values once they are no longer needed. Anonymisation and aggregation features in Matomo, including IP masking and optional cookieless tracking, help reduce risk further when combined with explicit consent where required. 

Planned this way, custom dimensions support accurate analysis while maintaining transparency, user control and respect for local privacy requirements.

Advanced tips and best practices

Reserve slots for stable attributes

Custom dimension slots are limited and difficult to restructure later, so teams should stick to stable ideas and stay away from ultra-granular values that will bloat tables.

Planning ahead of time and consulting the Matomo Measurement Plan can prevent performance issues or dimension limit frustrations down the road.

Avoid high-cardinality values

High-cardinality dimensions, meaning those that have a large or infinite number of unique values, increase archive time and slow down reporting. Teams should avoid using dynamic values for their dimensions, like time stamps or full URLs with parameters.

Keep names simple and consistent

Naming matters. Simple labels such as “Subscription tier” or “Content category” make reports easier to scan and make future changes less painful. 

A shared naming convention for events, custom dimensions and variables helps everyone understand what each field stores and how it shows up in dashboards and exports.

Troubleshooting common issues

Data not appearing in reports

The most common cause of missing data is scope or timing. The dimension must be: 

• Active
• Attached to the correct site
• Sent before trackPageView or the relevant event

Reports only show data for the selected date range, so very recent hits may appear first in real-time or visit logs before they reach aggregated reports.

“undefined” or “Value not defined” dimension values

Reports may display “undefined” or “Value not defined” as a dimension value.

This has two causes:

• The tracker tried to use a variable that wasn’t defined when setCustomDimension was called, so it’s received as “undefined”
• The dimension was sent with an empty string, so it displays as “Value not defined”

To fix this, teams should set the dimension before the pageview or event is tracked and confirm that the variable returns a real value (unless intentionally left empty).

Inconsistent formatting

Inconsistent formats fragment results. For example, recording “pro”, “Pro” and “PRO” as separate values inflates the number of rows and makes comparisons harder. 

Shared naming conventions and validation on the data layer keep values accurate and readable.

Implementation validation

Tag Manager preview mode and browser Network tabs can confirm that dimension{id} is included in a tracking request. Teams can verify values in the Visitor Log before relying on aggregated reports.

Teams should also review dimension values to make sure no personal data is sent and the consent setup blocks tracking where required.

Using custom dimensions in Matomo

Custom dimensions fit neatly into Matomo’s privacy-first approach. The platform combines 100% data ownership with options such as IP anonymisation, cookieless tracking and no data sampling, so added context does not come at the expense of privacy or accuracy.

Matomo treats custom dimensions as first-class fields in many features. They appear in dedicated reports, can act as rows or columns in Custom Reports and can filter or group Goals, Funnels and E-commerce reports. A “Subscription tier” dimension, for example, can break down goal completions by Free, Pro and Enterprise across landing pages, events and revenue, which gives teams a clear view of how each tier behaves.

Business Matomo Cloud plans come with 15 visit-scope and 15 action-scope dimensions, but Enterprise’s total amount is customisable. On Matomo On-Premise, administrators can extend the default five per scope to around 50 per scope through a console command with SSH access.

Custom dimensions as a foundation for trusted analytics

Custom dimensions close the gap between raw metrics and meaningful insight by restoring context to every visit and action. 

Instead of isolated page views and bounce rates, teams gain a structured view of how real audiences behave across content, products and technical experiences. 

In Matomo, this richer picture rests on a trusted base: accurate data with no sampling, an open-source platform used by more than 1 million websites and features that can be configured for GDPR compliance. 

For organisations that value privacy and control, Matomo’s custom dimensions provide a practical path to clearer, more confident decisions.

Download Matomo and run it for free on your own server or start your free Matomo Cloud trial today — no credit card required.

FAQ

What is a custom dimension?

A custom dimension is a field that stores extra context for a visit or action, such as user role or content category. It appears in dedicated Matomo reports.

When should I use custom dimensions vs. custom variables?

Custom dimensions are the modern way to track extra metadata in Matomo. Custom variables are deprecated and mainly kept for legacy installations.

What’s the maximum number of custom dimensions allowed in Matomo?

Matomo Cloud Business Plan supports 15 custom dimensions per scope (visit and action), so 30 in total. The Enterprise plan has customisable limits. 

On-Premise starts at 5 per scope and can be extended to at least 50 per scope using console tools with SSH access. 

Can you add custom dimensions retroactively in Matomo?

Custom dimensions record values from the time tracking is implemented. Earlier visits without that value remain empty in reports.

How do custom dimensions differ from segments in Matomo?

A custom dimension adds a new field to the dataset, like a membership tier. A segment filters existing data, such as visits from a specific region.

Are Matomo custom dimensions GDPR-compliant?

Custom dimensions in Matomo can be made GDPR-compliant when configured and governed correctly, following consent, data minimisation, limited retention and anonymisation of personal data where possible. You can learn more in our handy GDPR guide.

Can I use custom dimensions in Matomo’s mobile app analytics?

Yes. Matomo’s mobile SDKs for Android and iOS support tracking custom dimensions alongside events, screens and ecommerce actions.

With privacy-centric methods like server‑side tagging and consent-based event measurement, marketing teams can still capture the contextual and behavioural signals they need to connect with target audiences and personalise content.

This guide explores first-party cookies and their use in marketing. We’ll discuss their benefits, how they differ from third-party cookies and their value in web analytics workflows, especially in marketing attribution. Finally, we’ll highlight potential risks to keep in mind and best practices to implement first-party cookies while promoting data minimisation, transparency and trust.

What are first-party cookies?

First-party cookies are a type of tracking code that helps a site remember visitor preferences. They keep people signed in, preserve baskets between pages, recall language and region choices and connect page views so analytics data can count user sessions and attribute conversions. 

They also give marketing teams direct customer behaviour signals without third-party intermediaries, which improves reporting accuracy and aligns with GDPR and other privacy requirements. 

Unlike Google Analytics and most legacy solutions that were initially designed around cross-site tracking, privacy-first tools are built around direct user interactions. These ethical analytics platforms focus on extracting insights while still respecting user privacy.

How do first-party cookies work?

When someone visits your website, your domain creates a small text file (the “cookie”) through your site’s script or web server and stores it in their browser to remember them.

Then on future visits or pageviews, the browser returns the same value to your domain, allowing you to link actions throughout a user session or over a short time frame.

First-party vs third-party 

First-party cookies are set and read by the site a person visits. Third-party cookies originate from embedded domains and are used for advertising purposes. Here’s a breakdown of their characteristics: 

While first-party cookies raise fewer ethical and privacy concerns, they still handle personal data and must be managed carefully. If responsibly implemented, with a clear purpose and transparency, they can provide significant benefits.

Benefits of first-party cookies

First-party cookies provide marketing teams with the necessary signals while keeping data within the bounds a visitor has chosen. The result is better measurement, clearer choices and a stronger foundation for privacy.

Clear ownership

Unlike tracking cookies used by advertisers and other third parties, first-party cookies are created and set by the website owner. Since tracking stays on your site and is limited to the purposes you declare, it’s much easier to explain to users. Visitors know exactly who is collecting their data and why, which builds trust.

Consistent data quality

Because first-party cookies travel between a browser and the site a person is on, they work consistently across your own pages. 

Teams get steadier session counts, cleaner attribution within a domain and fewer gaps caused by blocked third-party requests. 

You can also define sensible expiries to keep user data fresh, which improves the quality of conversion and cohort analysis.

Transparency and control

First-party setups are easier to explain and manage. You can show plain-language descriptions and provide a preference centre that lets people opt in or out later. 

It is straightforward to rotate identifiers, shorten lifetimes and minimise what you store. Clear naming and documentation create an audit trail that your legal and security teams can review.

Compliance support

Regulators emphasise transparency, purpose limitation and choice. Under the GDPR, CCPA and similar frameworks, data shouldn’t be kept any longer than necessary for the purpose it was collected. What’s considered a “reasonable” cookie expiry period varies by jurisdiction and industry.

First-party setups can be configured to support GDPR and similar rules by defining specific purposes, collecting only the minimum data, honouring consent, and setting sensible expiries. 

Teams should:

• Document expiry decisions and align them with local regulator guidance.
• Review expiries regularly as part of compliance checklists and audits.
• Adjust retention periods when business needs or regulatory expectations change.

Data privacy considerations with first-party cookies

First-party strategies avoid the broad cross-site profiling that made third-party cookies contentious. But they still involve personal data, so they require careful handling and safeguarding. Reusing identifiers or failing to obtain consent can increase data privacy risks.

Consent management issues

Under GDPR and similar laws, non-essential cookies need a lawful basis. So analytics and personalisation require consent. As an organisation using first-party cookies, make sure to stick to the following best practices: 

• Describe purposes in plain language.
• Honour preferences on every page load.
• Ensure settings sync across subdomains.
• Use a consent management platform.

Data storage and security considerations

Limit what a cookie stores. Keep values short, avoid storing sensitive data in the browser and set sensible expiration times. 

Secure attributes such as HttpOnly and SameSite help reduce exposure. In your systems, restrict access, log reads and changes and retain data only as long as needed for the declared purpose.

Cross-device tracking limitations

First-party cookies are browser-bound. They don’t link phones, tablets and laptops without an account or server-side logic. You can either accept these limits or consider explicit, consent-based methods such as signed-in measurement.

Balancing personalisation with privacy

Considering data privacy when using first-party cookies also means: Start with data minimisation. Use the least intrusive signal that achieves the goal. Prefer session-level metrics when possible. 

And always keep in mind to provide value in return for consent and make controls easy to find. The aim is to create more positive user experiences that respect data subjects’ choices and privacy.

Potential for misuse despite being “first-party”

Without proper implementation, first-party strategies can still have privacy risks. Watch out for common pitfalls to avoid. These include:

• Overly long lifetimes: Don’t keep identifiers longer than necessary, it can feel invasive and increase risk. Many tools default to 30‑day lifetimes, but privacy‑focused teams usually adopt shorter, purpose‑bound limits in the 7 to 14 day range.
• Fingerprint‑like IDs: Avoid using highly specific or persistent identifiers that resemble device fingerprinting
• Undisclosed reuse or repurposing: Be transparent if you reuse cookie data across contexts or for new purposes. 
• Sensitive data combinations: Be cautious when combining cookie data with sensitive information or using it for profiling or targeting.
• Rights handling: Users have the right to access or delete, or object to how their data is used. Make sure these options are easy for them to find and act on.

To avoid these pitfalls and make sure your first-party strategy is effective, start with the best practices below.

First-party cookie implementation best practices 

Done well, first-party cookies can support useful analytics and respectful personalisation. Follow the steps below to maintain a clear, auditable and user-centric setup.

Consent mechanisms

To meet the GDPR’s lawful basis, make sure to implement user-friendly consent mechanisms. Keep in mind to:

• Group cookies by purpose.
• Make it easy to change or withdraw consent.
• Obtain consent before setting non-essential cookies.

Value exchange

Help visitors understand how their choices shape their experience. You can add explanatory text to your cookie banners, for example:

• “Analytics cookies help us improve site performance and page loading times.”
• “Session cookies keep you signed in and save the items in your shopping cart.”
• “Preference cookies load the site with your preferred language and display settings.”
• “Personalisation cookies tailor content and product recommendations to your interests and region.”

Data minimisation 

To minimise privacy risk and support compliance, make data minimisation a top priority. Its core principles include the following:

• Store only what is necessary.
• Default to short randomised user IDs.
• Align expiries with purpose.
• Use session cookies where possible. 
• Scope strictly necessary cookies to the smallest path or subdomain that still works.

Audits & cookie lifecycle management

To encourage accountability and avoid unchecked cookie growth, conduct regular cookie audits and follow the following approaches:

• Maintain a cookie inventory that includes the name, purpose, domain, expiry date and owner.
• Regularly review inventory and remove legacy entries.
• Apply Secure, HttpOnly and SameSite attributes to strengthen browser protection.
• Enforce data retention limits
• Rotate identifiers regularly.

Privacy by design principles

To align internal privacy controls with regulator expectations, its crucial to understand privacy as a core principle of ethical marketing and embed it deep into your analytics approach:

• Conduct DPIAs for new feature releases or data uses.
• Opt for privacy-enhancing technology.
• Implement role-based access controls.
• Log all reads and changes, and document decisions for review and future reference.

When implemented with these safeguards, first‑party cookies can support ethical analytics and improve customer relationships.

From tracking to trust

First‑party cookies foster more respectful and transparent relationships with customers. When aligned with jurisdictional requirements and industry best practices, they’re effective and ethical analytics tools.

If your team needs a privacy-first approach to analytics, consider Matomo. It’s an open-source platform that lets you easily configure privacy settings to align with GDPR, CCPA and other privacy laws.

Whether you choose on-premises deployment or Matomo Cloud, you have full control over your customer data and everything you need to interpret user behaviour while still respecting their privacy.

Download Matomo On-Premise completely free, or start a 21-day free trial of Matomo Cloud.

Yet many analytics tools don’t take into account all the touchpoints that nurture customers. They still rely on first-touch or last-touch attribution — crediting only the starting point or final click. 

This oversimplifies reality, leading to poor sales and marketing decisions. 

Multi-touch attribution models offer a more balanced approach to valuing each touchpoint’s contribution. Tracking every meaningful interaction and the bigger picture helps you understand what truly drives conversions.

Let’s examine multi-touch attribution, how it works, and how Matomo can help you implement it. 

What is a multi-touch attribution model? 

A multi-touch attribution model (MTA) measures how every sales and marketing channel and touchpoint contributes to a conversion. Instead of crediting only the first-touch or last-touch attribution, MTA distributes the conversion credit across the entire customer journey.

Think of a shopper who first discovers a brand through a Google Ad. The next day, they read a blog via organic search, and a week later, they click on a Facebook ad. Finally, they buy after receiving an email promotion. 

A first-touch model credits the Google Ad, and a last-touch model credits the email promotion. But both ignore the role of search, ads and other content and their contribution to the eventual conversion. 

To fully understand the value of your sales and marketing efforts, you need to know how much each touchpoint contributes to the eventual conversion.

To solve this, multi-touch attribution distributes credit more fairly.

Offering a broader view, MTA reflects how real consumer journeys unfold. After all, people rarely buy after a single interaction. They move between search results, website visits, social media engagement and email interactions before deciding. 

With Matomo’s Multi Attribution feature, you can track each of these steps and assign conversion credit fairly, helping marketing teams see what truly drives results.

Check out our Acquisition and marketing channels user guide FAQ for a deeper dive into other attribution tracking models in Matomo.

Multi-touch attribution vs single-touch attribution: Why single-touch attribution isn’t enough 

Single-touch attribution models, like first-touch or last-touch, give all the conversion credit to just one interaction. These models are simple to apply and highlight either the entry point into the funnel or the final click before purchase.

Let’s look at an example.

Imagine a visitor first clicking a LinkedIn ad, later reading a product comparison blog, and then finally responding to a discount email. 

• With a first-touch model, the LinkedIn Ad gets 100% of the credit
• With a last-touch model, the discount email does

But here’s the kicker. Neither view tells you how the blog helped nurture the decision.

That’s the limitation of single-touch attribution. It oversimplifies performance, especially in longer sales cycles. 

As a result, companies often allocate a significant portion of their marketing budgets to the last-click channels that appear most profitable on paper.

In reality, marketing strategies depend on the combined effect of multiple touchpoints. 

Multi-touch attribution models recognise this combined value. They spread conversion credit more fairly, showing how each step contributes, from awareness to consideration to conversion. 

This gives marketing teams a more accurate picture of:

• What’s working
• Where to invest ad spend
• How to refine campaigns across channels

Types of multi-touch attribution models

While multi-touch attribution offers a more balanced view of marketing value, not all MTA models work the same way. 

Let’s walk through the most common types of MTA models and what they bring to the table.

This model treats every touchpoint as equally important. 

For example, say a B2B buyer spends weeks researching. They click a LinkedIn ad, read a product comparison blog, attend a webinar and finally respond to a retargeting email. 

Since each touchpoint contributed to their purchase, the linear model divides the credit evenly. 

A time-decay attribution model weights the conversion credit toward the most recent touchpoints. Earlier interactions still matter, but they carry less influence.

For instance, imagine a visitor spots a Facebook ad on Monday, forgets about it, but clicks a remarketing email on Friday and makes a purchase. 

The time-decay model recognises the stronger role of the email in driving the final action. 

Not every touchpoint is equal. Some push prospects forward in the funnel, while others simply build confidence or reinforce awareness. Position-based attribution models reflect this balance, focusing on milestone moments in the funnel.

• U-shaped attribution gives most of the credit to the first and last touchpoints.
• W-shaped attribution splits credit among three pivotal steps: the first interaction, a key mid-funnel event (like a form fill) and the last interaction.

Picture it like this. 

A prospect first discovers your company through organic search and downloads a whitepaper via LinkedIn. This is their first real signal of intent. 

Along the way, they read a press release and stumble on a blog about another product you offer. Those touches don’t directly relate to the purchase, but they act as trust signals, reinforcing credibility.

Later, they attend a webinar on a broader industry theme, which builds even more credibility. Finally, they respond to a sales email and convert.

In a U-shaped model, the organic search and the final sales email receive the most credit.

This model considers the two bookend touchpoints to be the most decisive. Discovery brings the buyer in, and the last interaction closes the deal. The whitepaper, press release, blog and webinar still count, but they receive minimal credit.

In a W-shaped model, the organic search, whitepaper download and sales email carry the most weight. These touchpoints mark the key milestones of discovery, active engagement and final conversion. The blog, press release and webinar still matter, but they’re treated as supporting steps. They’re valuable for trust-building but not decisive in the conversion.

Why multi-touch attribution leads to better marketing campaigns 

Nowadays, marketing produces more data than ever. But volume doesn’t equal clarity. 

Multi-touch attribution (MTA) does, though. Here’s how it helps show which interactions actually drive conversions.

Improves funnel analysis

Funnels aren’t linear. People don’t just click one ad and buy. Instead, they explore, compare, drop off and return. 

MTA maps these movements across awareness, consideration and conversion. By spreading credit across touchpoints, it highlights which steps push users forward and where they get stuck. 

Enhances attribution precision 

Last-click models undervalue early touches like blogs, social media, and nurture emails. MTA distributes credit more fairly, helping to prove how those interactions fuel long-term conversions. 

This is why MTA is so important. It helps you understand the merit of each marketing method, so your marketing efforts can support your sales team toward higher conversion rates.

Supports better budget decisions

Gut feelings and skewed metrics often drive marketing spending, but this is rarely the most efficient way to allocate budgets.

MTA replaces that guesswork with evidence. It shows which campaigns, channels and messages deserve more investment and which don’t.

Reveals cross-channel value

Search, social, email, referral, direct — customers rarely stick to one marketing channel. 

MTA connects all marketing channels as part of a single journey, revealing how channels amplify each other. By showing the patterns between the channels, campaign planning becomes more strategic and connected, as you can see which parts of the journey harmonise.

Gives stakeholders clearer insight

Multi-touch attribution breaks down the customer journey so you can see more than the final conversion. You get to understand the sequence of touchpoints that influenced it.

This makes it easier for marketing teams to justify strategy and communicate success across departments.

This leads to deeper funnel insights and more informed budget decisions. 

Common challenges with multi-touch attribution (and how Matomo solves them) 

Multi-touch attribution is powerful, but it still comes with hurdles. 

From fragmented data to privacy rules, these challenges often leave marketing teams with gaps in the story.

Here’s a quick overview:

Now let’s dive deeper into these blockers and look at how Matomo can iron them out.

Fragmented data across sessions, channels and devices

Many analytics tools struggle to stitch together user journeys. Cookies — the small browser files that track who people are and what they do on a site — are device-specific, and session data often resets after a short period.

That means if someone first visits a site on mobile, later returns on desktop and finally converts without logging in, the system may count them as three separate visitors.

The result? Attribution reports end up incomplete or even misleading. Instead of giving you the whole picture, your reports won’t recognise the value of mid-funnel marketing work. 

If a single customer’s three separate touchpoints look like three distinct buyers, it seems as though there are three different ways people are entering the funnel. When that happens, teams skew budget decisions toward the wrong touchpoints. 

Matomo solves this by using first-party cookies, UTM parameters and anonymous visitor IDs to connect sessions across devices. It allows you to track logged-in users more accurately with User ID tracking, giving a unified view of the whole customer journey and making sure every meaningful interaction counts.

Consent and privacy limitations

The GDPR, ePrivacy Directive, and many other global regulations require consent before tracking personal data. That’s why most websites show consent banners. But if users click “no,” all tracking stops. 

The problem here is that if you can’t track, you can’t run reliable attribution models. 

Matomo addresses this hurdle through a privacy-first approach, including its cookieless tracking mode, which doesn’t store personal data in cookies. In France, the data protection authority (CNIL) has officially recognised this method as compliant, so if you configure Matomo that way, you don’t even need a consent banner. 

In other countries, consent banners are still legally required. If visitors reject them, you can’t use cookies. 

But with Matomo’s cookieless mode, you can still capture anonymous, high-quality behavioural data where regulations permit, like page views and session length. That way, even if some users reject cookies, you don’t lose sight of their key interactions in your attribution reports.

Data loss from cookie rejection

Consent banners offer customers an option to reject tracking, which skews your attribution models, as reports only reflect the behaviour of a subset of users.

The more people reject cookies, the less data is available to make decisions.

But with Matomo’s cookieless tracking mode and anonymisation features (like IP masking), you can still capture compliant, high-quality behavioural data. This preserves accuracy without over-reliance on cookies.

Multi-touch attribution tracking in Matomo

Matomo makes it easier to see which touchpoints really drive conversions. 

Its Multi Channel Conversion Attribution plugin lets you assign conversion credit across every step of the journey — not just the first or last click.

Here’s how.

Built-in attribution reporting

Matomo lets you choose from multiple attribution models: linear, time-decay or position-based. This helps you see how credit is distributed across sessions, campaigns, keywords or referrers. 

Reports show exactly which interactions influence conversion and in what order. 

But unlike many other tools, Matomo doesn’t rely on data sampling. Insights are based on complete journeys, so you’re not extrapolating from partial datasets. This kind of high accuracy is essential when budget decisions and compliance are on the line.

Privacy-first by design

Matomo supports GDPR, CCPA and PECR when configured correctly. 

The default tracking code must be configured to obtain consent before tracking. In France only, if you run Matomo in cookieless mode with no personal data, CNIL allows tracking without a banner. Elsewhere, you need banners. 

In cookieless mode, Matomo uses a short-lived config_id (a 24-hour visit hash) rather than fingerprinting. That way, you can still capture anonymous behavioural signals while protecting users.

Flexible goal tracking

Matomo lets you define conversions that match your business logic, from purchases and downloads to newsletter signups. 

You can create multiple goals and track how each marketing channel contributes. This avoids the “one-size-fits-all” trap and shows how different funnel stages affect outcomes.

Real behavioural insights

Matomo goes beyond page views. With Tag Manager, session recordings and heatmaps, you can track on-page actions like scroll depth, CTA clicks and video plays. 

Matomo dashboard showing visits log, visitor map, search keywords, and real-time behavior panels

These behavioural signals feed directly into attribution reports, showing how on-page engagement connects to eventual conversions.

Cookieless, first-party tracking

The phasing out of cookies is problematic for companies that rely on third-party data.

With a privacy-centric platform like Matomo, you become more resilient to the phase-out of third-party cookies, as it’s easier to maintain accurate attribution while still supporting user privacy and compliance efforts.

Matomo uses first-party cookies (stored on your own domain) or its privacy-friendly config_id system, which is a limited-time, anonymised identifier that groups actions into visits without persistent fingerprinting.

Raw data access and API integration

With Matomo, you can export attribution data through the open Reporting API or connect directly to Looker Studio and Power BI. 

This allows you to blend web analytics with CRM or offline sales data to measure long-term impact, without being locked into rigid dashboards.

Implementing multi-touch attribution in 5 steps with Matomo

It’s straightforward to set up with multi-touch attribution in Matomo. 

1. Start with accurate tracking

Use Matomo Tag Manager to track the actions that matter, such as:

• Page views
• Clicks
• File downloads
• Form submissions 

You don’t need developer support to set this up, so you can begin collecting reliable data immediately.

2. Set up campaign parameters

UTM parameters let you trace visits from email campaigns, paid search ads and social media links. 

Matomo automatically analyses these and connects them to your attribution reports to make sure that every marketing source gets the recognition it deserves.

3. Create conversion goals

Define what success looks like. Whether it’s a purchase, a whitepaper download or a lead form submission, you can set up conversion goals in just a few clicks. This ties every report back to meaningful outcomes.

4. Choose your attribution model

Select from linear, time-decay or position-based models to understand how credit is distributed across the funnel. 

Matomo lets you compare models side by side, so you can test which best reflects your customer journey. 

5. Monitor and adapt

You can view results in Matomo’s dashboard, set up scheduled email reports, or export raw data via the Reporting API. 

With these insights, you can refine campaigns, optimise spending, and focus on what works.

Start modelling what matters

Multi-touch attribution helps you assign value across the full customer journey so that you can make smarter sales and marketing decisions based on the real levers.

With a keener eye on the role each touchpoint plays, you can more confidently make budget allocations and capitalise on the opportunities most likely to move the needle.

But don’t rely on outdated tools that only consider the first and last touchpoints.

Try Matomo. You can build privacy-friendly, multi-touch attribution reports without third-party tools or data risks. Start your 21-day free trial today to spot the sales signals you’re missing.

This playbook puts URL parameters to work, providing the key to precise campaign tracking and informed decisions. Learn how to use these simple yet effective tools with Matomo to gain crystal-clear insights, improve ROI, and stay fully compliant with data privacy regulations—all without needing to write any code.

What are URL parameters?

Parameters are short tags added to links that record where a visit came from and the context of the click. They turn a plain URL into a source of clear, comparable campaign data for analysis.

Decoding the URL: Query strings vs. URL parameters

The query string is the part of the URL after the question mark “?” and before any hash fragment “#” or the end of the URL. Inside that query string live the parameters, written as “key=value” pairs and separated by “&”. 

For example:

Here, “mtm_campaign”, “mtm_source” and their values are parameters created with Matomo’s URL builder. When someone clicks this link, Matomo records the campaign name, source, medium, and more, allowing comparison of channels and creatives without manual tagging.

Need extra fields? Install the free Marketing Campaigns Reporting plugin to track up to five campaign dimensions.

A note on special characters: URL encoding 

Some characters are reserved in URLs. Encoding replaces them with a safe code so nothing breaks. Spaces will often become “%20”. Do not worry about the details, as browsers and tools like Matomo handle encoding automatically.

Different types of parameters

Not every parameter behaves the same way. Two useful lenses help plan and troubleshoot:

1. Whether a parameter alters the page itself.
2. The function it performs for the visitor or the analytics it provides.

Active parameters

Active campaign parameters change what a user sees on the page. They modify the content or layout so that search engines can treat the resulting URL as a distinct page. Examples:

• “sort=price_asc” reorders products from low to high
• “category=boots&colour=black” filters a catalogue view
• “lang=fr” serves the French version of a page

Passive parameters

Passive parameters do not change the page content. They carry context for analytics or routing, and the page looks the same without them. Examples:

• “mtm_source=newsletter” and “mtm_campaign=summer_sale” for campaign tracking
• “utm_term=running+shoes” to capture the paid keyword
• “ref=partner123” to attribute an affiliate

Functional categories of parameters

Marketing use cases for using parameters

Parameters join the dots between clicks and outcomes across every channel. Tag once, maintain consistent naming, and then compare performance in Matomo without relying on manual spreadsheets. 

For quick ideas and patterns to try, here are some common campaign tracking use cases and examples.

Tracking email campaign performance

Add parameters to every newsletter link so Matomo records source, medium and campaign. For example, mtm_source=newsletter&mtm_medium=email&mtm_campaign=spring_launch. Email platform data can also be mapped to Matomo.

Using Mailchimp? Check out our guide to track Mailchimp email clicks in Matomo.

Measuring social media ROI

Social budgets spread across organic posts and paid placements. Parameters standardise the click data, providing a way to attribute visits, conversions and revenue back to each network and post type. 

Pair consistent tagging with the Multi Channel Conversion Attribution plugin to see first touch, last touch, or position-based credit. This helps you decide where to invest next and which creatives to scale.

Analysing paid advertising effectiveness

For search and social ads, apply parameters at the campaign or ad level. Capture keyword, ad group, audience or placement in “mtm_content” or “mtm_kwd” to compare cost against conversions by slice. This makes A/B tests faster to read and keeps reporting stable even when platforms rename fields.

Understanding affiliate marketing performance

Give each partner a unique parameter, such as “ref=partner123” or record the partner in “mtm_source”. Matomo helps create a simple affiliate system that will attribute clicks, leads and sales to the right affiliate, which supports accurate commission payments and shows which partners bring high-value customers.

Common parameter pitfalls to avoid 

Parameters are useful for measurement, yet unmanaged, they can negatively impact search performance. This can create a few issues for marketers and SEO teams.

Duplicate content

Small parameter changes can result in multiple URLs that display the same page. 

A product grid at “/shoes” might also load at “/shoes?sort=price_asc”, “/shoes?size=10”, and “/shoes?size=10&sort=price_asc”. 

Search engines treat these as separate pages, which weakens each’s SERP position and visibility. Ranking signals spread between them, pages compete with each other and the clean version may struggle to win. 

The result is poor performance and fewer organic visits. Relying on parameters for filters or tracking requires a clear plan to direct search engines to the preferred version and maintain consistent internal links.

Protecting your crawl budget

Crawl budget is the time and resources a search engine sets aside to explore a site. It is not unlimited. 

Parameterised URLs multiply quickly so that bots can spend their visit on many thin variations rather than key pages. Important content gets crawled less often, updates get discovered later, and visibility can drop. Large catalogues feel this most when every sort, filter, and view mode adds new URLs. 

Keep the most valuable pages easy to reach, reduce unnecessary variants, and guide bots toward the versions that matter.

Diluted link equity

Links help search engines judge which page deserves to rank. When shares and backlinks point at parameterised URLs such as “?mtm_campaign=spring_launch”, the authority those links pass can splinter across many addresses. A clean URL earns less credit, even though it is the page you want to rank. 

The same split happens when internal links sometimes include tracking parameters and sometimes do not. Aim to keep public and internal links pointing to the canonical URL, and ensure that any tracking tags are stripped where they are no longer needed to persist.

Best practices for parameters

When handled well, parameters give clean data without harming the search. The aim is simple: show search engines one preferred version of each page while keeping rich campaign details for reporting.

Canonicalisation

Use a canonical tag to point each set of parameterised URLs to a single, preferred address. 

In most cases, that is the clean version without tracking tags. Place a self-referencing canonical on the canonical page, then add the same target on common variants such as “?sort=price_asc” or “?mtm_campaign=summer_sale”. 

Keep rules consistent across templates so lists, filters and pagination are all resolved to the correct URL. If a parameter truly creates unique content worth indexing, let that page self-canonise and treat it as a distinct destination.

Guiding search engines through internal linking

Links teach crawlers what matters. 

Keep navigation, breadcrumbs and body links pointing at the canonical URL, not at parameterised versions. 

Avoid adding tracking parameters to internal links as they overwrite attribution and multiply crawl targets. 

Standardise trailing slashes, capitalisation and parameter order so that the same page is never linked in two different ways. 

For language targets, use dedicated paths or subdomains where possible rather than “?lang= parameters”, then connect them with hreflang.

Using robots.txt with caution

Blocking parameters via robots.txt can sometimes backfire: it prevents crawling but not indexing. It can also hide signals that help canonicalisation work.

Canonical tags consolidate ranking signals by pointing parameter variants to a single preferred URL while keeping the page crawlable. A metanoindex suits pages that serve users but should not appear in search, removing them from the index while still allowing link equity to flow.

By contrast, robots.txt stops crawling entirely, so search engines may keep a URL indexed from external links, but cannot see the canonical or noindex and cannot evaluate internal links. Reserve robots.txt for genuine crawl traps such as infinite calendars, session IDs, or faceted combinations that can explode into countless thin URLs.

A/B testing parameters

Experiment tags like “?variant=a” are useful during tests but should not live on in search engines. Canonicalise variants to the original or apply a meta noindex

After the assignment, store the variant in a cookie or session so visitors see the same experience while sharing clean URLs. Name tests clearly, keep them short, and remove test parameters at the end. For measurement, pair clean final URLs with Matomo’s A/B testing reports so results are easy to read.

Personalisation with parameters

Custom tags can enrich campaign data without exposing personal tracking information. Define a simple taxonomy for “mtm_campaign”, “mtm_source”, and “mtm_medium” and reuse “mtm_content” or another agreed-upon key for audience, creative or placement. 

Keep values short, lowercase and human-readable. Never pass personal data in URLs. If you need to include an internal identifier, use a non-reversible ID rather than an email. 

Strip tracking parameters from on-site navigation, log them once at entry, then use Matomo to carry the session forward. It preserves accurate attribution while keeping things clean.

Optimising URL parameters with Matomo

Matomo turns tagged links into clear, trustworthy reports without code. It uses its campaign tracking, flexible parameters and readable dashboards to move from guesswork to decisions.

Campaign tracking 

1. Set a simple naming convention for mtm_source, mtm_medium, mtm_campaign, mtm_content and mtm_kwd.
   
2. Build links with Matomo’s URL builder so tags are added correctly.
   
3. Add the tagged links to ads, emails and social posts before publishing.
   
4. After launch, check campaign reports to confirm clicks and conversions are recording as expected.
   
5. For Google Ads, follow Matomo’s guide to align auto-tagging, ValueTrack or manual tags so sessions are attributed correctly.

Tip: keep values short, consistent and free of personal data.

Custom parameters

Many sites already use parameter keys from other systems, such as utm_source, cid, src, or partner tags. Matomo can customise which keys it reads and map them to its campaign fields (for example, treat utm_source as Source and utm_term as Keyword), so historic links and third-party URLs report correctly without re-tagging. 

Matomo also supports mixing MTM and UTM parameters in the same URL. When duplicate keys are present (e.g., mtm_campaign=Summer%20Sale&utm_campaign=winter%20Sale), the first matching parameter is used. But if a user finds the website through a Google ad link, signs up for a newsletter and finally clicks an email link, Matomo can start a new visit to preserve accurate attribution for each campaign.

This keeps reporting consistent across platforms and teams while you move toward a single naming convention.

Custom reports and dashboards

Matomo’s reports make parameter data easy to act on. Watch traffic arrive in the Visits in Real-time widget, then open the Visits Log to inspect individual sessions and confirm tags. Use the Real Time Visitor World Map to spot geographic patterns.

(Image source: Matomo)

Pin these widgets to a campaign dashboard, add a segment for a specific mtm_campaign, then compare performance week over week to inform future spend decisions.

Get started

Mastering URL parameters is crucial for accurate campaign tracking and informed decision-making. This playbook outlined the different types, marketing use cases, SEO pitfalls to avoid, and best practices for parameter optimisation.

Put this playbook to work with Matomo. Use the free URL builder to tag links, then read results in Campaign reports and dashboards to see which sources, creatives, and keywords drive visits and conversions.

Ready to craft effective, SEO-friendly URLs and measure their impact? Start your 21-day free trial of Matomo today.

In the United States, consumer credit reporting agency Equifax agreed to pay $425 million to consumers affected by a 2017 data breach. Amazon is facing a class action lawsuit for harvesting personal user data without consent. Meanwhile, in the EU, regulators are issuing multi-million dollar fines for violations under the GDPR’s data minimisation principles.

This article explores why data minimisation is vital for businesses, strategies and techniques for minimising data collection, and how Matomo can help. 

What is data minimisation?

Data minimisation is the practice of collecting only the data that is truly necessary and ensuring it is securely deleted once it’s no longer needed. It’s a core data privacy and data protection principle that also governs how companies collect and use data. 

This doesn’t mean organisations stop collecting data altogether. Companies still gather essential data, including, for example, first-party cookies that improve the customer experience. However, they are highly selective about the data they collect, avoid unnecessary data collection and delete data once it no longer serves a purpose. 

To fully appreciate this cultural shift toward the principle of minimisation, it helps to contrast it with its predecessor: the data maximisation mindset.

Data minimisation principles are a core part of the EU’s General Data Protection Regulation (GDPR), which states that any personal data collection must be “adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed”. 

The concept also appears in other privacy laws and influential guidance or privacy frameworks, such as the Fair Information Practice Principles (FIPPs), applied by the US Department of Homeland Security and other federal agencies, including the Federal Trade Commission (FTC), when evaluating whether a company’s privacy practices are “unfair or deceptive.”

Implementing data minimisation principles helps companies protect their users’ privacy, prevent data misuse, and reduce the risks of data breaches and non-compliance. 

What data should businesses collect?

Data minimisation doesn’t mean businesses should avoid collecting data entirely. Companies should still collect customer data when implementing data minimisation practices. However, they should do so thoughtfully using the four principles of adequacy, relevance, limitedness and timeliness.

Let’s explore each principle in more detail:

• Adequate: Not all data collection is bad. Businesses should collect enough data to meet their stated objectives and deliver services to customers.
• Relevant: Only collect data pertinent to business objectives. If you later want to use the data for another purpose, make sure the new use is compatible with the original one or that you have a valid legal basis.
• Limited: Businesses should strip data of identifiable information they don’t need. If companies only need a zip code, for example, they should delete the rest of a user’s address.
• Timely: Review held data regularly and delete it when it no longer serves a purpose. Businesses should even delete backup data at the end of the retention period. 

Under Article 6.1 of the GDPR, businesses must establish a lawful basis for processing personal data. The six recognised bases are: 

1. Consent
2. Contractual obligations
3. Legal obligations
4. Vital interests
5. Public interests 
6. Legitimate interest

The legal bases for processing special categories of personal data are different, and they are set out in Article 9 of the GDPR.

The business case for data minimisation

Data minimisation offers significant benefits to businesses. It can lower the risk of leaks, reduce the costs if leaks occur, build trust with consumers and make data management easier. 

Here’s more information on each of these benefits.

Mitigate risk

Data minimisation reduces the risk of a cybersecurity incident by limiting the data available for bad actors to exploit. The less data a company holds, the smaller its attack surface. It also makes companies less tempting targets in the first place.

If the worst occurs, data minimisation makes the fallout less severe. In the event of a breach, strong data minimisation practices mean thieves can only steal a limited amount of data, preferably only anonymised and masked data. 

Regulatory fines should also be smaller because they correlate with the size and impact of a breach. Further, companies may only suffer minor reputational damage if they can prove thieves stole only a small amount of data. This is particularly important because almost half of U.S. businesses have suffered significant revenue loss due to a security breach. 

Build trust

Consumers care deeply about data privacy, with 70% of them taking steps to protect their identity. Data minimisation shows that you care, too. It’s an excellent way to deliver a more ethical and privacy-focused service and prove that you put customer privacy first.

This increases customer trust, reassures cautious users and helps retain existing customers who are becoming increasingly concerned with privacy. 

Reduce costs

Data storage is expensive. A recent survey found that UK companies spend £213,000 to store and manage data. Many respondents said they had to choose data management spending over employee welfare and training. 

Data minimisation reduces these operational costs by decreasing the amount of data companies need to store. A small data footprint means lower infrastructure investments and more efficient resource allocation. Data backups are also cheaper to run.

Ensure regulatory compliance 

Data minimisation is essential for businesses complying with most privacy laws, including the GDPR. It’s one of the seven principles for personal data and privacy protection laid out in Article 5 of the GDPR, which states: 

Companies operating beyond the EU may also need to practice data minimisation to comply with local data privacy laws, including the following:

• California Privacy Rights Act (CPRA)
• Colorado Privacy Act (CPA)
• Florida Digital Bill of Rights (FDBR)
• Utah Consumer Data Privacy Act (UCDPA)
• Connecticut Data Privacy Act (CTDPA)
• Virginia Consumer Data Protection Act (VCDPA)

Even outside of regulated regions, many companies proactively adopt these privacy principles to build trust, ensure scalability and stay ahead of their competitors.

Fines for non-compliance can be significant. Businesses that fail to comply with GDPR can face fines of up to €20 million or 4% of their total global annual turnover, whichever is higher. Meta received the largest GDPR-related fine so far, €1.2 billion, issued by the Irish Data Protection Commission in May 2023. 

Minimise noise

Collecting as much data as possible isn’t helpful. In addition to raising storage costs, this increases noise and makes it harder for analysts to use the data. 

As Timo Dechau explains in his recent webinar on running lean analytics in a privacy-first environment:

“Digital marketers, analysts, and business leaders now try to navigate vast amounts of information that create more confusion than insight, especially when the data is incomplete due to privacy regulations.”

Lean data means fewer variables to process. With Matomo’s Custom Reports, analysts can get the information they need more efficiently, speeding up decision-making and reducing time spent cleaning or interpreting irrelevant data.

Four data minimisation techniques 

Businesses can implement several techniques to anonymise data, reduce the amount they hold and shorten data retention times.

1. Understand what data to collect and set up data collection policies 

The first step is to understand what data is adequate, relevant, and limited to what is reasonably necessary for the purpose. This should be documented in a data collection policy.

The policy explains how your organisation handles personal data. It’s a framework marketers and other teams can reference when building landing pages, forms, and campaigns. It also clarifies for customers who are wary about how organisations use their data. 

Your policy should include:

• Data collected
• Collection methods
• Processing activities
• Purpose of collection
• How data is used
• Who has access
• How it’s stored
• How it’s shared

Don’t just write the policy, though. Spend time training employees on your policy and the importance of handling personal data with care. 

2. Pseudonymise or Anonymise data

Companies can protect personal data by removing identifiers. Anonymisation transforms data so that it can no longer be linked to an individual at all (and therefore falls outside GDPR). Pseudonymisation, on the other hand, replaces identifiers with artificial values but can still be re-identified if additional information is available — so it remains personal data under GDPR.

Common techniques include:

• Data masking: replacing sensitive data with altered or fictional values so the original information is hidden, but the dataset remains usable
• Data substitutions: replacing original characters with alternatives using pre-established rules.
• Data shuffling: rearranging data in a dataset.
• Tokenisation: replacing identifiable data with randomly generated tokens.
• Pseudonymisation: replacing identifiable data with pseudonyms or fake data. 

For example, merchants rarely store full credit card numbers; they use tokenisation to mask sensitive details. 

3. Limit data access

While deidentified data allows companies to share data freely across their organisations, businesses should limit data access as much as possible. 

One of the best ways to do this is through role-based access control (RBAC). This security method restricts system access to authorised users based on their job role and seniority. In other words, only people who need the data for their jobs can access it. 

4. Create data retention policies 

A data retention policy defines how long companies keep data and how they delete it when it is no longer required. It also outlines data storage and access methods. 

Data retention policies are essential for companies to comply with data protection laws like GDPR. They also offer guidance and reassurance to employees. Deleting corporate data is a big decision, and employees will feel more inclined to follow through if a policy supports their actions.

How Matomo can minimise your data

The web and app analytics data you collect is a great place to start minimising data collection. While some of this data is essential for attributing sales and improving the customer experience, many businesses tend to collect far more than they need to, especially if they use Google Analytics. 

Matomo—the world’s leading privacy-friendly web analytics solution— includes a range of built-in features designed to help you minimise data collection while delivering incredible analytics. 

1. Automatically Mask or Anonymise data

Matomo lets marketers implement data masking or anonymisation techniques so the data they collect cannot be linked to individual users. 

IP address: The first method is to mask or anonymise a visitor’s IP address and geo-location information in your privacy configuration settings. By default, IP masking is enabled in Matomo. You can choose to mask varying amounts of the IP address. 

An optional setting allows you to select whether to use the full IP address to find the user’s location and immediately mask the IP before storing it or the hashed address for the geolocation lookup. 

Anonymise referrer information: To enhance privacy and comply with data protection laws, Matomo allows users to anonymise referrer information, which can sometimes contain personal data like user IDs. You can choose from several levels of anonymisation, including removing query parameters, keeping only the domain, or fully stripping the referrer URL while still identifying its source type.

No default UserID tracking: To protect your visitors’ or users’ privacy, Matomo does not track UserID by default. While Matomo automatically tracks various data, such as IP address, page views, and browser details, UserID tracking is optional and must be explicitly configured.

Anonymise previously tracked data: Matomo also lets you anonymise data you’ve already collected. In the Anonymise data section of your Privacy settings, configure a one-off data anonymisation process to run on data you have tracked in the past. You can anonymise:

• Visitor IP
• Location
• User ID
• Visit columns
• Action columns

2. Let visitors opt out of tracking

The most effective way to minimise data is not to collect it in the first place.

In regulated jurisdictions, such as those governed by the EU ePrivacy Directive and the GDPR, prior consent is mandatory before tracking begins. Unless they are required to provide a service requested by the user, analytics cookies always require active opt-in consent.

Opt-out mechanisms are only appropriate in specific non-EU contexts or narrowly defined legitimate interest use cases where consent isn’t legally required

Matomo supports jurisdiction-specific tracking and opt-out forms (where consent is not required):

Matomo supports jurisdiction-specific tracking and opt-out forms (where consent is not required):

• Consent-first tracking
  If you need to obtain user consent before tracking their data, you can integrate Matomo with your Consent Management Platform (CMP) to capture and respect user preferences in accordance with local laws.
  
  
• Opt-out form (where consent is not required)
  In regions without mandatory consent, you can embed Matomo’s opt-out form to allow visitors to exclude themselves from tracking.
  • Paste the form’s HTML directly into your website’s code; or
  • Use Matomo for WordPress to automatically match your consent form’s design to your page.

This flexibility ensures that you can configure tracking to meet your legal obligations and your visitors’ privacy expectations.

Opt-out form in Matomo

Related FAQ

• Do I need consent to use web analytics on my website?
• How do I ask for user consent before tracking visitors or measuring user analytics?
• Can I track a visitor without cookies when they have not given consent for tracking cookies?
• How do I let visitors opt-out of tracking?

3. Shorten cookie lengths and delete old data

The cookies Matomo creates have a pre-specified expiry time. But Matomo lets you shorten cookie lengths to minimise data and free up database space. 

Matomo allows you to configure data retention for both raw data and reports. You can program Matomo to delete historical logs automatically. You can do this in the Anonymise Data section of the Privacy settings, configuring Matomo to disable the visits log or delete logs older than a set number of days. You can also set up purging to happen automatically every day, week or month. 

4. Use cookieless tracking

To minimise the analytics data you store about users, consider using cookieless tracking. 

Cookieless tracking is an alternative form of tracking in which Matomo uses the visitor config_id (a randomly seeded, privacy-enabled, time-limited hash of a limited set of the visitor’s settings and attributes) to track users. 

In some jurisdictions, cookieless tracking, if combined with collecting no personal data or unique identifiers, may remove tracking consent requirements. But in countries with stricter ePrivacy laws, cookieless tracking will still require prior consent.

5. Use consent-exempt configurations of Matomo

Some EU countries (France, Italy, the Netherlands, Spain, and most recently the UK) provide express consent exemptions for privacy-preserving aggregated analytics that minimise the range of data processed. Matomo is highly configurable and can be set up to comply with applicable consent-exempt conditions (see CNIL consent-exemption).

Protect your users’ privacy with Matomo

Data minimisation protects your businesses, reduces costs and helps you comply with data protection regulations. It’s non-negotiable if you care about using and storing data ethically. 

Rather than just another compliance requirement, many forward-thinking companies are treating ethical analytics and data minimisation as strategic brand differentiators. By putting privacy first, brands can build trust, grow customer loyalty and gain a competitive edge.

To take the first step on your data minimisation journey, consider switching to Matomo. With Matomo, you get: 

• Complete control over your data
• Built-in data minimisation methods
• A straightforward, easy-to-use analytics interface
• A built-in GDPR manager
• Compliance support for other strict privacy regulations

See why more than one million websites trust Matomo to ethically track and improve website performance. Start your 21-day free trial today — no credit card required.

These tools collect and analyse quantitative and qualitative data, helping you to reduce friction and increase conversions through A/B testing and conversion rate optimisation. 

Finding a tool that can do all this while still protecting your users’ privacy can be tricky. But we’re here to help. 

In this article, you’ll learn what customer journey analytics is, how you can use these tools and what to look for when choosing your own. You’ll even find detailed reviews of the best customer journey analytics tools available today. 

What are customer journey analytics tools?

Customer journey analytics tools collect, analyse and visualise buyer interactions across multiple touchpoints and channels.

Through customer journey analysis, marketers can better understand where users come from, how they navigate websites, where they get stuck and what makes them convert.

Unlike traditional analytics tools that focus on isolated metrics or channels, customer journey analytics tools provide a cohesive view of the entire experience. They help marketers create a rich customer journey map that reflects thousands of different user experiences.

To do this, tools use several data collection methods, including:

• Web analytics
• Behavioural tracking
• Session recordings
• Heatmaps
• Funnel analysis

By integrating quantitative data — what customers do — with qualitative insights — why they do it — these tools help businesses understand customer behaviour on a much deeper level. 

Imagine a SaaS brand finds that its checkout page has a really high bounce rate. Using session recordings, they see that one of the buttons breaks on mobile devices, preventing people from making purchases. 

It’s an easy problem to fix, but they only discovered it because of their customer journey analytics tool’s session recording capabilities. 

Seven of the best customer journey analytics tools 

With so many analytics tools to choose from, it can be hard to know where to begin your search. 

We’ve made things easier by reviewing seven of the top tools on the market and highlighting their features, use cases, and pricing. 

1. Matomo

Matomo app analytics is a powerful, open-source platform that provides a range of behavioural analysis, segmentation and marketing attribution features while still putting data privacy first.

(Image Source)

Privacy is a huge concern for businesses these days. Unlike other tools, Matomo helps you comply with data privacy laws like GDPR and CCPA with ease. It’s completely open-source (so you can see how the platform collects data) and even offers a self-hosted option, so only you have access to your data. 

Privacy shouldn’t come at the expense of customer insights, though. Matomo offers marketers everything they could need from a customer journey analytics tool, including:

• Session recordings, heatmaps and visitor logs that trace the entire customer journey, identify friction and help you improve the user experience
• Customer segmentation tools to analyse how different groups interact with your site
• Retroactive analysis and near-real-time monitoring of website interactions, giving you insights into how recent changes affect behaviour and conversions
• A clean, user-friendly interface that even non-technical employees can start using immediately

Pricing starts from $26 per month for cloud hosting. On-premise hosting is free. 

2. Glassbox

Glassbox is a digital customer experience analytics platform that delivers powerful insights through its Augmented Journey Map™ feature.

Glassbox journey map
(Image Source)

Glassbox’s platform captures every user session across your digital channels without you having to tag events manually. This helps it create a comprehensive map of every customer journey, which marketers can use to:

• Identify struggle points, conversion blockers and opportunities for optimisation across websites and mobile apps.
• Understand the why behind user behaviour thanks to session replay capabilities and heatmaps.
• Understand the potential lost revenue of friction and prioritise improvements accordingly.

Glassbox also offers AI-powered insights that automatically identify anomalies and potential optimisation opportunities. The platform’s real-time alerting capabilities help businesses respond quickly to emerging issues before they significantly impact conversion rates.

Pricing is available upon request. 

3. Fullstory

Fullstory is a digital experience analytics platform that helps businesses to understand, optimise and personalise their customers’ journeys across web and mobile applications. 

Fullstory’s new user activation dashboard
(Image Source)

The tool has everything you’d expect from an analytics platform. It uses auto-capture technology to record every user interaction automatically, identifying friction points and conversion drop-offs.

Marketers can pair the journey maps the tool creates with funnels, conversion tracking and segmentation to see why specific users succeed or fail. 

Other key features include:

• Sentiment signals to give marketers automatic alerts when users show signs of frustration. It lets them tackle friction before it leads to an abandoned cart. 
• Session replays that bridge quantitative and qualitative analytics, letting teams watch real user sessions to understand the “why” behind the “what”.
• AI-driven insights, real-time alerts and retroactive data analysis help teams quickly spot trends, identify pain points and validate improvements.

Pricing is available upon request. There is a limited free plan.

4. Hotjar

Hotjar is a behaviour analytics tool that helps businesses understand how users interact with their websites and products. It specialises in heatmaps and session recordings that provide qualitative insights into buyer behaviour.

Hotjar heatmaps feature
(Image Source)

Hotjar’s intuitive visual analytics tools provide a clear view of exactly how visitors interact with websites. Heatmaps, for example, provide visual representations of how users interact with website pages, showing where they click, move and scroll. This visual approach means you don’t need to be an analytics expert to understand user behaviour and identify optimisation opportunities. 

Other features include:

• Session recordings, which let you watch user sessions to see frustration events in person
• User feedback tools, including on-site surveys and pop-ups that collect qualitative data about buyer motivations and intentions
• User tests, which let marketers run optimisation experiments at scale from within the platform

Pricing starts from $49 per month. There’s a free forever plan that includes unlimited heatmaps.

5. Mixpanel

Mixpanel is a product and customer analytics platform that tracks and analyses user interactions across web and mobile apps. Its main strength lies in providing insights into how users engage with your product. 

Mixpanel product metrics
(Image Source)

Event-based analytics let businesses understand exactly how people progress through their digital experience. Mixpanel provides a wealth of qualitative data in the form of product analytics. But you can support that with qualitative findings from session recordings, too. 

Other features include:

• Cohort analysis enables you to observe how different user segments progress through each journey stage
• Retention analysis helps you understand which features and experiences drive long-term engagement
• Multi-touch attribution lets you measure the true result of your marketing activities and find channels with the most impact.
• A/B testing for comparing and analysing the performance of different webpage variants

Mixpanel is free to use initially. Enterprise customers will need to request more information. 

6. Heap

Heap Analytics is a product analytics platform that automatically captures and analyses every user interaction on your website and app. It provides a complete view of the customer journey.

Heap marketing KPI dashboard
(Image Source)

Unlike traditional analytics platforms that require manual tagging, Heap automatically captures all user interactions, allowing businesses to analyse behaviour retroactively. 

This makes Heap particularly good for comparing and contrasting behaviour as your app and website develop. You can get a feel of which updates immediately improved the user experience and which took time to get used to. 

Other customer journey analytics features include:

• Session replays let you go in and see precisely why a specific user didn’t convert. 
• Effort analysis helps you find, quantify and remove friction from your funnel.
• Rage click maps and alerts highlight when users grow frustrated and click repeatedly on your site. 
• Segmentation analysis lets you compare the journeys of behaviour-driven user cohorts.

A limited free plan is available. Pricing is available on request. 

7. Appier AIRIS (formerly Woopra)

Appier AIRIS, formerly Woopra, is a marketing analytics platform that provides a holistic view of everyone who visits your site. Near-real-time analytics let you see who’s making a payment, browsing your product pages or opening your emails. 

Appier AIRIS journey map
(Image Source)

Appier AIRIS helps you build detailed customer profiles and use them to personalise the user experiences. The platform’s integration with AIQUA’s personalisation platform means you can create highly personalised experiences. 

Other features include:

• Optional journey reports to visualise the impact of touchpoints on customer success 
• Multi-touch attribution to understand the impact your campaigns had on conversions
• Cohort analysis to segment by date, geography, device, source, job title and more

Pricing starts from $49 per month. A limited free plan is available. 

What can you do with customer journey analytics?

Customer journey analytics tools are incredibly versatile. They can pinpoint what stops users from converting or track how they behave after they purchase. 

Eventually, marketers should use them to track the entire customer journey. But if you’re new to customer journey analytics, consider starting with one of the following use cases. 

Personalise the customer experience

Use customer journey analytics to understand what your customers need and want — then provide them with the perfect, personalised experience. 

Cohort analysis — a form of behavioural analytics where you analyse the behaviour of users who share similar traits — makes this possible. 

An analytics tool with cohort analysis functionality lets you group users by the actions they take (downloading a gated asset, making a purchase or leaving a page, etc.) or demographics (where they’re from, which device they use, etc.)

Different cohorts experience your product or service differently. So mapping and analysing cohort-specific journeys lets you optimise the experience for each group at every touchpoint.

Take smartphone users. Their browsing experience will be very different from that of desktop users. Understanding the specific differences will let you personalise your mobile site to improve the experience and increase conversions. 

Reduce churn

Churning is a fact of life for SaaS brands and other subscription businesses. While it isn’t possible to eliminate churn, you can take action to reduce it with customer journey analytics. 

By analysing the journeys of people who churned compared to those who remained loyal, businesses can identify specific points of friction or problems that result in an unsubscribe.

Once you know why customers churn, you can take a proactive approach to retaining the ones you have. 

Suppose the results show that decreased login frequency and increased support ticket submissions are predictors of churn, for instance. Customer success teams can then email users who haven’t logged in for several months to ask them what’s wrong.

Conversely, you can identify the actions that predict strong customer loyalty, such as completing onboarding experiences and utilising multiple features, and take steps to encourage existing customers to exhibit similar behaviour. 

Reducing churn by even a small percentage can significantly impact revenue and profitability, making it one of the most valuable applications of journey analytics.

Find and eliminate friction

Customer journey analytics excels at identifying friction points that prevent customers from completing desired actions. By analysing where customers struggle, hesitate or abandon actions, businesses can identify and prioritise improvements that have the most significant impact on conversion rates.

It will be much more critical (and profitable) to fix any friction customers experience during the checkout process compared to your homepage, for example. 

You’ll want to combine quantitative metrics (analytics KPIs like time on site and bounce rate) with qualitative insights captured through session recordings and heatmaps to paint a complete picture of user frustration. While web analytics reveal where problems occur, session recordings show exactly how customers experience these issues — and, most importantly, how to fix them. 

Attribute and increase conversions

Understanding which marketing channels and touchpoints contribute most effectively to conversions is essential for optimising marketing spend. Use the multi-touch attribution models in your customer journey analytics tools to understand how different interactions influence purchase decisions.

While traditional last-click attribution models give all credit to the final touchpoint, multi-touch attribution offers more sophisticated results that distribute credit across two or more interactions. They uncover the early-stage touchpoints that play a crucial role in initiating customer journeys that ultimately lead to conversion.

When you know every channel that contributes to a conversion, you can make better decisions and allocate your marketing budget more effectively. If SEO significantly outperforms social, for example, you can choose to spend your social budget on content marketing efforts.

What to look for in a customer journey analytics tool

The number of customer journey analytics tools extends far beyond our shortlist above. Narrow down your search by looking for a tool with all of the following features: 

Customer journey mapping

Customer journey mapping is essential for understanding complex customer paths. 

Matomo lets you connect different customer journey paths to clear goals — like purchases, signups, or downloads. This allows teams to:

• Monitor how users progress toward key outcomes
• Segment journeys by goal completion
• Compare paths across personas

By segmenting journeys based on whether users completed a goal or conversion, it’s easier to see what’s working well and repeat those patterns.

Choose tools that provide visual representations of every customer journey and let you break down journeys by behavioural or demographic cohorts. 

Pay particular attention to tools that generate journey maps based on actual behaviour data rather than hypothetical journeys. Google Analytics, for example, is notorious for using data sampling, whereas Matomo gives you 100% accurate data.

Behavioural analysis

Behavioural analysis tools help businesses understand what customers do and why they do it. Choose a platform that combines quantitative and qualitative tools, like: 

• Session recordings
• Heatmaps
• User feedback collection

For more detailed analysis, Matomo Form Analytics goes deeper into how users interact with forms, breaking down: 

• Where they hesitate
• What they skip
• When they drop off 

The more qualitative and quantitative feedback you can gather, the better. 

Marketing attribution

Attribution modelling helps marketers understand which channels contribute most significantly to conversions.

Finding a tool that offers multiple attribution models is key. Matomo, for example, provides the following models:

• First interaction
• Last interaction
• Linear
• Position-based
• Time decay

As a result, you can shift marketing budgets to channels with the most significant impact and track changes over time. 

Segmentation

Segmentation tools let you break users up into cohorts that share characteristics.

Segmentation should extend beyond simple demographic or acquisition-based groups to include behavioural and engagement-based segments. This more sophisticated approach helps businesses understand the relationship between customer behaviours and journey outcomes.

In Matomo, for example, you can segment visitors by the date they convert, the first time they visited your site and several behavioural metrics, like their browser or device. 

Custom reporting

Customisable reports ensure insights reach the right stakeholders in the most helpful format. Choose a tool that lets you create tailored reports highlighting the metrics most relevant to specific teams or business objectives.

While Matomo’s standard reports are great for most use cases, we know every business has unique needs. That’s why you can choose from over 200 dimensions and metrics to build the specific reports your stakeholders require. 

Data privacy

Data privacy matters when you’re collecting customer journey data. 

GDPR and CCPA compliance features help businesses meet their legal obligations while collecting valuable journey insights. The best tools include features for protecting PII, managing consent and handling data subject requests.

Bonus points if the tool gives you complete control over customer data, preferably storing it on your own servers rather than third-party cloud services

You can configure Matomo to follow even the strictest privacy laws, such as GDPR, HIPAA, CCPA, LGPD and PECR. 

Your users will never feel like they’re being tracked, either. The platform has an opt-out mechanism, the ability to anonymise IP addresses and can be configured to avoid processing any personal data. 

Optimise your customer journeys with Matomo

Customer journey analytics tools are essential for marketers who want to understand and improve the customer experience. 

With Matomo, you can:

• Credit conversions across channels with Multi-Touch Marketing Attribution
• Understand which journeys lead to conversions
• Segment journeys by persona or traffic source
• Use Form Analytics to identify drop-off points 
• Optimise UX with Session Recordings and Heatmaps

Whether you’re optimising a signup form or scaling a campaign, you have what you need to take action.

Start your 21-day free trial today to explore the platform’s full customer journey analytics features — no credit card required. 

You must also consider how a tool analyses data and helps you turn insights into action. There’s the customer-facing aspect, too. Shoppers shouldn’t be bombarded with cookie requests because you’ve chosen a tool that isn’t privacy-focused.

Finding the right analytics platform can be more complicated than some store owners first think. 

Don’t worry, though. We’ve got you covered. This article reviews five of the top ecommerce analytics tools and explains key factors to consider when choosing a platform.

What are ecommerce analytics tools?

Ecommerce analytics tools are software platforms that capture, measure and analyse data at every stage of the customer experience.

The right data analytics tool can help store owners and marketers to understand how shoppers behave on their site, which marketing channels are most profitable, and why customers abandon their carts. 

Most tools boast a wide range of features to achieve those goals, including:

• Customer journey tracking to understand shopper behaviour
• Conversion funnel analysis to see why customers don’t convert
• Customer segmentation and cohort analysis for targeted marketing
• Heatmaps and session recordings to pinpoint UX issues
• A/B testing to improve conversion rates
• Marketing attribution to identify high-value acquisition channels

Ultimately, a good analytics tool will reduce friction in the customer journey, boost conversion rates and personalise shopping experiences. 

Top five ecommerce analytics tools

Below, you’ll find a roundup of the five top ecommerce analytics tools based on their features, pricing and suitability for different types of stores.

Whether you’re looking for a free option, a comprehensive tool or a platform that respects your users’ privacy, you’ll find a suitable solution.

1. Matomo 

Matomo is an open-source ecommerce analytics platform offering a comprehensive, privacy-first solution.

Matomo’s ecommerce analytics solution goes beyond simple web tracking to give you all the customer data and tools you need. Track every metric using heatmaps and session recordings to understand how shoppers use your site. Then take action with built-in A/B testing tools. 

The platform’s open-source and privacy-focused nature makes it a solid choice for store owners who care about protecting their customers’ privacy.

Standout features

• Heatmaps and session recordings to visualise usability issues and frustration points
• A/B testing tools to optimise product pages or checkout flows
• Event tracking and goal funnels for conversion rate optimisation
• Custom reports and dashboards to turn data into insights
• No data sampling 
• Ethical and privacy‑first
• No cookie banners required in many cases (if configured correctly)

Pricing: Free self‑hosted core version, or $29/month for cloud-based option.

2. Google Analytics 4

Google Analytics 4 is the most popular web analytics platform on the planet, with a broad range of features to support ecommerce stores. 

(Image Source)

Google Analytics excels at showing customer engagement and website traffic metrics. Clear dashboards and reports make it easy to determine who landed on your site, what they did, where they came from, and how they converted. 

It’s a solid choice for first-time store owners looking for a free solution integrated with Google’s other products. However, be careful of the lack of data privacy and data quality. Google samples data when generating results, meaning your metrics may not be 100% accurate. 

Standout features

• Purchasing tracking, including transaction IDs, revenue, shipping costs and taxes
• Product performance analysis to see which products are popular and how they are performing
• Conversion analysis to find drop-off points and areas for optimisation 
• Dashboards that make it easy to track basic ecommerce data
• Native integration with Google Ads, Search Console, BigQuery, and Data Studio
• Free to use 

Pricing: Free. (Enterprises that want more advanced features can use Google Analytics 360, where pricing is available on request.)

3. Adobe Analytics

Adobe Analytics is an enterprise-level platform that combines web, product, and predictive analytics to deliver real-time insights across channels. 

(Image Source)

For in-depth ecommerce metrics tracking combined with predictive analytics and advanced customer segmentation, Adobe Analytics is a solid choice.

It gives a comprehensive view of real-time customer behaviour thanks to customer journey analysis tools, marketing attribution and ecommerce performance tracking.

It could be a sensible choice if you already use Adobe Experience Cloud or other products in the Adobe ecosystem. However, this platform may be too complicated and cost-prohibitive for everyone else. 

Standout features

• AI-powered prediction analysis identifying likely buyers, churn signals and customer lifetime value
• Attribution analysis across channels and multi-touch funnel analysis
• Detailed customer journey analytics and real-time data
• Predictive analytics come standard 
• Native integration with Adobe Commerce for a holistic view of customer interactions
• Advanced cohort analysis lets retailers create detailed, targeted audiences

Pricing: Available on request

4. Mixpanel

Mixpanel is a product analytics platform focused on user engagement, event tracking and cohort analysis. It has a dedicated ecommerce analytics solution for store owners looking to find conversion bottlenecks and drive more sales.

(Image Source)

Mixpanel is an intuitive ecommerce analytics platform that allows store owners to track user behaviour. It comes with a range of behavioural analysis tools, such as session recording and cohort analysis, to help identify and solve cart abandonment issues. 

While Mixpanel offers a free plan, it’s limited in scope. Enterprise plans start at $20,000 per year.

Standout features

• Event-based tracking with granular control over how user behaviour is recorded
• Funnel and cohort analysis to measure retention, repeat buyers and conversion paths
• Visual dashboards and real-time behavioural insights
• Multi-touch attribution to measure marketing effectiveness
• Streamlined interface and intuitive dashboards make data analysis accessible
• Built to handle high data volumes and process billions of events monthly
• Seamless integration with other software platforms

Pricing: Free to use, capped at one million monthly events.

5. Hotjar

Hotjar is a behaviour analytics tool that allows store owners to track shoppers’ interactions with their sites and experiment with ways to increase conversion rates. 

(Image Source)

Hotjar’s visualisation tools are one of the best to learn how shoppers browse your site. The platform has more types of heatmaps than almost any other provider, including:

• Click maps
• Heat maps
• Scroll maps
• Rage click maps

It’s an excellent tool for store owners who want to visualise how customers click, scroll, and interact. However, it doesn’t collect ecommerce data to the same degree as other platforms. 

Standout features

• Heatmaps and session recordings to understand user behaviour
• Conversion funnels that track where users drop off during checkout
• Feedback polls and surveys to track shopper satisfaction
• User-friendly interface and intuitive design
• Highlights feature lets teams share key user insights
• Native integrations with Shopify and other ecommerce platforms

Pricing: starts from $39 per month. A limited free plan is available

What to look for in an ecommerce analytics tool

Whether you use our shortlist above or create your own, you’ll want to ensure your tool has all the necessary features. 

Here are the most important ones.

Core ecommerce metrics tracking

First, your analytics tool needs a dedicated ecommerce solution that tracks key shopping metrics. The following are particularly important:

• Orders
• Total revenue
• Taxes
• Shipping costs
• Average order value (AOV) 
• Abandoned carts

You can track all these and more in Matomo. 

Check out our guide to the 7 Ecommerce Metrics to Track and Improve to learn more. 

Custom reports

Every ecommerce store is different, so choose a flexible analytics platform that lets you create custom reports. 

In Matomo, you can choose from over 200 dimensions and metrics, as well as different visualisations like bar, pie, and line graphs.

You can even automate your reporting by integrating Matomo’s Custom Reports feature with the Email Reports feature. 

Customer segmentation 

Even though you’ll want to collect information on every visitor, it can be helpful to learn more about audience groups. 

Customer segmentation lets you analyse shoppers based on demographics, behaviour and other factors to create more targeted campaigns and encourage repeat purchases. 

In Matomo, for example, you can create segments based on:

• Demographics
• Visit patterns
• Buyer behaviour
• Marketing campaigns
• Technology customers use
• Average order value
• Lifetime value

By using these segments to zero in on particular audiences, you can offer more value to your customers and gain a competitive advantage in the marketplace.

Conversion rate optimisation capabilities

Conversion rate optimisation is the key to higher revenues, better return on ad spend and long-term customer retention. 

But you don’t need to spend time and money on a dedicated tool. Choose an ecommerce analytics tool with built-in conversion rate optimisation capabilities to keep everything under one roof. 

With Matomo, for example, you can:

• Use heatmaps to see how users engage with your site
• Replay website sessions to learn why users don’t convert
• Run A/B tests to experiment with different headlines, images, calls to action or page layouts

Matomo also measures the impact of your experiments on key ecommerce metrics and lets you implement changes based on statistically significant differences, not guesswork. 

Marketing attribution

Marketing attribution assesses the impact each channel has on conversions and revenue. It helps you understand which channels drive the best shoppers.

Matomo makes it easy to understand which channels drive the most conversions and how much each is worth.

Marketing attribution in Matomo

To measure the impact of each channel across the customer journey, you can choose from several attribution models (last interaction, first interaction, position-based, etc.).

Without marketing attribution, you risk wasting time, money and effort on channels that don’t benefit your business.

Ecommerce platform integration

Make it as easy as possible to start by choosing an analytics tool with native integrations with ecommerce platforms like Shopify and Magento.

Magento, for example, integrates with every major platform and numerous smaller tools, including PrestaShop, OpenCart, and Zen Cart. 

Server-side tracking

Most ecommerce analytics platforms collect ecommerce data using JavaScript-based tracking. While this is largely effective, it can’t collect every interaction. That’s because ad blockers and other tools can block JS tracking. 

The only way to guarantee you collect data on every shopping action is through server-side tracking. 

Server-side tracking means that when a user interacts with your website, your backend server captures the event data and sends it to Matomo’s tracking API endpoint. This bypasses the browser and improves data accuracy, reliability, and privacy compliance. You control what data is collected, stored, and processed.

Data privacy and security

Some ecommerce analytics platforms — like Google — use the data they collect about your customers to power additional services like Google Ads and sell to other companies.

Matomo, on the other hand, was designed with privacy in mind. You can configure it to follow strict privacy laws like GDPR and CCPA. Using Matomo also means all of your valuable data is owned by you and you alone.

Privacy-first ecommerce with Matomo

An ecommerce analytics tool helps you understand customer behaviour, make smarter marketing decisions and drive growth.

With Matomo, you can do all that while prioritising your users’ privacy. Trusted by over one million websites, Matomo’s open-source software is the ethical analytics solution every store owner needs.

Start your 21-day free trial — no credit card required.

Fortunately, there are a number of more affordable, accurate and user-centric analytics solutions that address these concerns. 

This guide explores six top Adobe Analytics alternatives, compares their key features and capabilities and explains how to find the best fit for your analytics needs.

What is Adobe Analytics? 

Adobe Analytics is a popular digital analytics platform. It’s known for its enterprise-grade capabilities, which are aimed at larger organisations with complex data needs. 

It offers detailed insights into website traffic, customer behaviour and conversion performance. It can segment audiences, track activity and compare key metrics like page views, traffic sources and customer journeys.

Its AI-powered tools, like anomaly detection and predictive analysis, help spot trends and optimise marketing strategies. 

Despite its sophisticated capabilities, it does come with challenges. 

What’s the problem with Adobe Analytics? Why switch?

One of many common struggles among ‌Adobe Analytics users is the platform’s setup. 

The UI is complex and overwhelming for non-technical users, and the platform has a steep learning curve.

Adobe Analytics also has some problematic features. Manual tagging, for instance, is extremely time-consuming. Updating and keeping track of tags “by hand” like this makes it hard to scale along with your business.

The manual tagging feature is also error-prone and requires technical expertise to tag appropriate actions and extract valid insights. 

Even if you tag everything correctly, these insights are limited to a certain number of pre-set interaction types, which may not reflect the full spectrum of customer behaviour.

Users also complain about latency issues with Adobe Analytics. Delays in reporting make it hard to get instant insights, leading to slower decision-making.

Pricing is another issue. The higher tiers can get quite expensive. And there’s no free option beyond a product demo, so there’s no real way to try it before committing. 

6 Adobe Analytics alternatives and who they work best for

Clearly, Adobe Analytics isn’t for everyone. Let’s explore some of the top alternatives for website analytics.

1. Matomo – Best for privacy-focused companies that need all-in-one analytics

For organisations that must comply with stringent regulations like GDPR or CCPA, privacy features are critical factors in a web analytics solution.

Matomo offers an ethical, privacy-first approach to analytics. It gives businesses deeper control over customer data to ensure its accuracy, security and integrity.

One of Matomo’s key benefits that sets it apart from other analytics solutions is its capability for users to self-host data. This offers unparalleled security and compliance. 

It’s also one of the few platforms that combines traditional web analytics with behavioural analytics. Users can access features like heatmaps, A/B testing and session recordings, all under one roof.

Matomo can track everything from technical site performance to customer experiences and show the results on custom dashboards or automatic email reports.

Plus, with cookieless tracking and no data sampling, organisations know they’re getting 100% accurate insights without sacrificing user privacy.

Matomo dashboard with visits log, visits over time, visitor map, combined keywords and traffic sources
(Image Source)

Key Features

• Advanced multi-channel reporting for websites, mobile apps and ecommerce
• Heatmaps and session recordings
• A/B testing platform
• Multi-channel conversion attribution
• User flow to visualise customer journeys
• IP anonymisation
• Cookie-free tracking
• Search engine keyword performance reports
• Customisable dashboards and reports
• Integration with over 100 platforms, including Google Ads, WordPress and Magento

Matomo was designed with the strictest data privacy and compliance requirements in mind: no third-party access and no data sold to advertisers. Instead, users can anonymise IP addresses and configure “DoNotTrack” settings.

All data is accurate. There’s no AI filling in gaps or skewed samples, just 100% factual data that drives better decision-making.

Strengths

• Fully GDPR compliant with advanced privacy features
• Full data ownership with no third-party interference
• Comprehensive analytics tools that don’t rely on data sampling
• Cookieless tracking for more accurate, compliant insights
• Self-hosting and cloud options available
• Supports 100 currencies and multiple time zones
• Strong integration with other tools via API

Common community critiques

• Short learning curve for beginners
• Some premium features come at an extra cost

Pricing

• Matomo On-Premise is free.
• Matomo Cloud starts at just under $22 a month if paid annually. 

Matomo vs. Adobe Analytics – The verdict 

Adobe Analytics offers a diverse set of enterprise-level tools. But it comes at a much higher cost and doesn’t have the same emphasis on privacy. It’s also missing some of the key advanced features that Matomo offers, such as search engine keyword reports, cookie-less analytics and full GDPR compliance. 

Matomo, on the other hand, stands out for privacy-focused companies. Not only is it a budget-friendly solution, but it also provides 100% data ownership. 

In contrast, Adobe’s platform relies on data sampling and third-party tracking. If privacy and customisability are top priorities for your company, choose Matomo over Adobe Analytics.

Ready to learn more? Try Matomo for free now.

2. Google Analytics – Best for budget-conscious businesses that track with cookies

Google Analytics is a household name in web analytics. It’s a free tool that allows businesses to track website traffic and user behaviour.

It provides basic insights into digital performance without the high pricing plans of more advanced tools, making it great for small companies and startups with limited budgets. 

It’s a natural choice for marketers using Google Ads who want to measure ad performance. 

GA isn’t the most accurate tool as it relies on data sampling, meaning the results don’t consider 100% of website visitors.

Unfortunately, Google Analytics also lacks advanced privacy controls and relies on cookies for tracking.

However, if you prioritise price over privacy, Google Analytics delivers strong foundational analytics.

GA reporting dashboard with active visitors, traffic type, average visit duration and bounce rates
(Image Source)

Key Features

• Visits by traffic type (organic, social and direct)
• User behaviour tracking (clicks, scrolls and time on page)
• Demographic and interest data
• Conversion and ecommerce tracking

Strengths

• Easy to track behaviour across multiple devices
• Integrates easily with Google Ads for ad tracking

Common community critiques

• Limited privacy controls
• Data sampling

Pricing: 

• Google Analytics is free to use. 
•  Advanced features are available through Google Analytics 360 for an additional cost.

Google Analytics vs. Adobe Analytics – The verdict
Google Analytics offers good value for budget-conscious businesses. However, it falls short when it comes to enterprise-level depth and privacy. While easy to set up, it lacks the custom dashboards Adobe offers.

3. Mixpanel – Best for product-led companies identifying audiences

Mixpanel is a product analytics platform. It’s designed to show companies how users interact with their products.

It ‌excels at uncovering high-value audiences by tracking customer actions and analysing user journeys. Its strong behavioural analytics and segmentation tools help businesses see how to make products more engaging for specific groups.

However, the free plan limits historical data retention. This restricts long-term trend analysis. 

Mixpanel boasts an intuitive interface for basic tasks, which is ideal for less experienced teams. However, you’ll need technical expertise to use advanced features like SQL queries and custom events. 

Its reliance on third-party storage also raises privacy concerns.

While Mixpanel is great for tailoring product experiences to target audiences, it’s not ideal for teams that prioritise data ethics or lack technical expertise.

Mixpanel product metrics dashboard with channels by plan, channel stickiness, annual spend, new user accounts and more
(Image Source)

Key features

• Custom event tracking for specific user actions
• Detailed reporting for immediate insights
• User behaviour funnels to track conversion steps

Strengths:

• Self-serve support interface for non-technical users
• Advanced segmentation for detailed audience analysis

Common community critiques

• Restricted historical data retention in free tiers
• Limited performance metric tracking

Pricing: 

• The free plan includes basic analytics for up to 1M monthly events
• Beyond that, pricing scales with usage — 1.5M events is ~$140/month (or $100 with annual billing), 3M events is ~$378/month ($270 with annual billing). For exact estimates, use their Growth plan price calculator.

Mixpanel vs. Adobe Analytics – The verdict
Mixpanel excels in product analytics but doesn’t provide the same depth of web analytics as Adobe Analytics.

Adobe also offers better custom reports and audience segmentation to help with marketing and traffic analysis. 

4. Amplitude Analytics – Best for growth-stage companies focused on product customer journeys

Similar to Mixpanel, Amplitude Analytics is also a product analytics platform. It focuses on optimising customer journeys for digital products.

It excels in user segmentation. You can create detailed cohorts to track drop-offs throughout your funnels. Its A/B testing feature lets product teams compare conversion strategies and identify effective solutions.

However, its session-based tracking is restrictive. It analyses discrete visits but doesn’t track multiple visits or visitor origins, making it challenging to draw long-term insights.

Amplitude is ideal for growth-stage companies seeking deep insights into user interactions, but it is not suitable for businesses that need comprehensive, long-term customer journey tracking.

Amplitude Analytics dashboard snapshot showing average revenue per user for a specific product
(Image Source)

Key features:

• User path tracking
• Custom event tracking for product usage
• Behavioural cohort analysis for targeted insights
• A/B testing to optimise product experiences

Strengths:

• Detailed segmentation of user behaviour
• Strong focus on product metrics

Common community critiques

• Limited support for traditional web analytics
• Session-based tracking isn’t very comprehensive

Pricing: A free starter plan is available. Paid plans are custom-built and pay-as-you-go. 

Amplitude vs. Adobe Analytics – The verdict
Amplitude ‌helps companies understand customer journeys around product usage, concentrating on detailed behavioural analysis. Web analytics are product-focused rather than sales-focused.

For more traditional web analytics, Adobe Analytics provides traffic source tracking, conversion optimisation and insightful reporting.

5. Heap – Best for lean startups that want code-free analytics

Heap is a code-free analytics platform. It’s for tracking and enhancing user behaviour across digital experiences. It offers behavioural analytics and session replays to pinpoint friction points in the user journey.

Its standout feature is automatic event tracking, which helps you capture user interactions without manual setup. This makes it particularly appealing for lean teams and those without technical expertise. 

Unfortunately, Heap has limited customisation options, restricting advanced users with complex analytics requirements. 

It also lacks GDPR compliance support. This is an issue for privacy-conscious organisations.

Heap is excellent for startups that want user-friendly analytics with automated tracking, but it won’t fit businesses that need extensive customisation or strict privacy compliance.

Heap marketing KPI dashboard
(Image Source)

Key features:

• Automatic event tracking 
• Session replays
• Customisable dashboards for quick insights
• Behavioural analytics for deep user understanding

Strengths:

• Simple setup 
• Supports cross-device user journeys
• Codeless event tracking

Common community critiques:

• Limited customisation for advanced users
• Events can pile up quickly without dedicated monitoring 

Pricing: It’s free for up to 10k sessions. Paid plans have custom pricing.

Heap vs. Adobe Analytics – The verdict
Thanks to its code-free setup, Heap is far easier to implement than Adobe Analytics, even without technical expertise. 

That said, Adobe offers more advanced features to monitor site traffic and marketing performance. 

6. Open Web Analytics – Best for developers seeking analytics customisation

Open Web Analytics (OWA) is a free, open-source analytics platform for developers who need customisable tracking solutions.

It’s highly flexible, offering users full control over their data. Features like heatmaps, clickstream tracking and API support are some of its main strengths.

But OWA is not for inexperienced teams. It requires extensive technical expertise to set up and maintain, and it relies on community support. If you need timely assistance, you might struggle.

OWA doesn’t rely on third-party storage, which is good for privacy-focused teams. However, it’s best for technically skilled teams, not those needing out-of-the-box solutions.

OWA developer dashboard with site metrics, top content, actions and traffic sources
(Image Source)

Key features:

• Customisable web analytics with Javascript and PHP APIs
• Heatmaps and clickstream tracking
• Integration with WordPress and MediaWiki
• Conversion goal and funnel tracking

Strengths:

• Full data control 
• Custom tracking 

Common community critiques:

• Limited support
• Requires significant setup and deep technical knowledge

Pricing: Free 

OWA vs. Adobe Analytics – The verdict
Firstly, OWA is free, while Adobe Analytics comes with a steep price tag.

However, Adobe Analytics gives users an enterprise-grade packaged solution with AI-driven insights. Customising OWA is hands-on and meant for developers. 

What to look for when picking a web analytics tool

Here are the key factors to consider when picking an Adobe Analytics alternative.

1. Comprehensive features

Look for analytics tools that offer detailed analysis through heat maps, session recordings and interactive dashboards.

All-in-one solutions like Matomo provide in-depth feedback, analysis and reporting on user behaviour patterns. Teams only need one tool to understand user experiences and optimise web performance.

2. Privacy and compliance

Tools that handle sensitive customer behavioural data need to prioritise privacy and compliance.

That means full compliance with privacy features like IP anonymisation, cookie-less tracking and total data ownership. This ensures data is secure, private and compliant.

3. 100% data accuracy

Many tools claim to provide accurate data while using data sampling to speed up data processing and analysis.

These samples aren’t always representative of the entire dataset. So, conclusions can be skewed or inaccurate.

Matomo doesn’t use data sampling, meaning 100% data accuracy and more reliable insights.

Choose analytics that don’t compromise data privacy

Choosing the right analytics software can preserve user privacy and build customer trust. 

Remember, not all tools offer the same protection and control over data. 

Choose Matomo for 100% data ownership, full privacy and completely accurate analytics. Keep your data in your hands. Try Matomo for free now. 

But with hundreds of tools to choose from in a market valued at over $5.6 billion, how can you find the best platform that offers cross-channel tracking and advanced analysis while staying on the right side of privacy laws?

We’re here to help. 

In this article, let’s review seven of the top marketing analytics tools, highlighting their standout features, pricing, and common community critiques. You’ll learn why choosing the right tool is crucial and what factors to consider when making a decision. 

What are marketing analytics tools?

Marketing analytics tools capture and analyse data from various marketing channels, such as your website, social media profiles, and paid ad campaigns. 

Marketers use these platforms to find ways to optimise campaigns and drive more conversions. Marketing attribution tools, for example, measure marketing effectiveness and help marketers understand which channels drive the most conversions. As a result, they can optimise budgets, allocating more money to the most effective channels. 

Multi-Channel conversion attribution in Matomo
(Image Source)

Marketers can also reduce friction from the customer journey. Behavioural analytics tools like heatmaps and session recordings help marketing teams understand what’s stopping users from converting and run experiments to increase conversion rates. 

Marketers can use an all-in-one analytics tool or a platform-specific alternative. Some analytics only track your social media efforts, for example. Others, like Matomo, let you track web visitors, paid ad performance, SEO data and attribute conversions from multiple campaigns. 

The features and capabilities of marketing analytics tools can also vary by industry. For example, financial marketing analytics platforms will prioritise compliance and data security, while e-commerce teams focus on user behaviour analysis. Advanced tools now leverage machine learning to predict trends and automate insights, making them indispensable for data-driven decision-making.

7 of the best marketing analytics tools

With numerous marketing analytics platforms to choose from, it can be challenging to determine the best one for your business. 

We’ve done the hard work, though. Below you’ll find reviews of seven of the leading tools, why they’re great and what customers say about them.

1. Matomo

Matomo Analytics is a leading ethical open-source marketing analytics platform that powers over a million websites in more than 190+ countries.

Main dashboard in Matomo
(Image Source)

Why Matomo: Matomo empowers organisations to get the insights they need without compromising user privacy. Businesses can significantly reduce the amount of personal identifiable information they collect and comply with privacy laws like GDPR and CCPA. At the same time, they can use visitor logs to track the entire customer journey, assess the value of marketing channels using multi-touch attribution and analyse visitor behaviour using heatmaps and session recordings.

Standout features include multi-touch attribution, visitor logs, goal tracking, custom reports, e-commerce tools, form analytics, tag manager, Google Analytics Importer, heatmaps and session recordings. 

Integrations: Matomo integrates with more than 100 content management systems, e-commerce platforms and frameworks, including WordPress, Cloudflare, Magento, Google Ads, Drupal, WooCommerce and Wix.

Strengths:

• 100% accurate, unsampled data
• Privacy-focused marketing analytics
• Complete data ownership 
• Open-source software 
• Self-hosting and cloud-based options
• A built-in GDPR Manager

Common community critiques:

• Non-technical users can experience a learning curve with some of the platform’s more advanced features
• Premium features are proprietary

Pricing: Matomo On-Premise is free to use. Matomo Cloud costs $23 per month and comes with a 21-day free trial (no credit card required).

2. Heap by Contentsquare

Heap by Contentsquare is a digital insights platform that gives businesses a near-real-time understanding of their users’ digital journeys.

Demo dashboard in Heap
(Image Source)

Why Heap: Heap helps businesses paint a complete picture of their customers. It automatically records every user interaction (clicks, page views, form submissions and more) without manual event tagging to give marketers access to every metric and allow for retroactive analysis. 

Standout features include data science tools that identify customer friction, journey analysis, session replays, heatmaps, pre-built dashboards and customer cohort analysis.

Strengths:

• Automatic event tracking eliminates the need for manual tagging, saving time and reducing implementation errors.
• Setting up Heap is easy with a single code snippet. You don’t need advanced technical skills.
• Real-time reporting and live data feeds help marketers quickly spot opportunities and issues. 

Common community critiques:

• The volume of data capture can create more noise than signal, which clouds analysis
• Users can find the platform’s interface unintuitive
• Businesses can accidentally collect personally identifiable information (PII) if they don’t configure the platform correctly

Pricing: Heap has a limited free plan for up to 10,000 monthly sessions. Pricing for Growth, Pro and Premier plans is available upon request. 

3. Mixpanel

Mixpanel is a product and marketing analytics platform that helps SaaS and mobile marketers track user retention and engagement. 

Product metrics dashboard in Mixpanel
(Image Source)

Why Mixpanel: Unlike traditional analytics tools that focus on pageviews and sessions, Mixpanel uses event-based analytics to track, analyse, and optimise user actions. It also has AI-powered predictive analytics that help marketers identify trends and proactively address churn. 

Standout features include predictive analytics, funnel analysis, GA4 migration, A/B testing and real-time reports

Strengths:

• Intuitive dashboards and reports make Mixpanel accessible for non-technical users
• Extensive integrations ensure seamless data flow across your tech stack
• Advanced cohort analysis and customer segmentation support targeting and personalisation efforts

Common community critiques:

• The wide range of features means there’s a steep learning curve for new users
• Pricing rises quickly for enterprise users
• Event tracking can be difficult to set up

Pricing: Mixpanel has a free forever plan with limited features. Premium plans give you one million monthly events free and then charge $.00028 per event after that.

4. Funnel

Funnel is a low-code marketing data platform that automates the collection and transformation of marketing data from hundreds of sources. 

Performance marketing dashboard in Funnel
(Image source)

Why Funnel: Funnel is the ideal choice for marketers operating across dozens of different channels. It helps you gain a holistic view of marketing performance by pulling in data from over 500 sources, cleansing and visualising it.

Standout features include a vast number of integration partners, automated data collection and transformation, two-year data storage and custom integrations.

Strengths:

• Low-code setup makes Funnel accessible to anyone
• Highly responsive customer support
• Custom metrics for personalised reporting

Common community critiques:

• The visualisation features are fairly basic. Marketers often need to use other tools like Tableau.
• The platform has a steep learning curve
• Delays can occur when processing data from third-party sources

Pricing: Available upon request

5. HubSpot

HubSpot is a comprehensive analytics platform that helps marketers improve every stage of the buyer’s journey. Detailed insights and robust automation capabilities let marketers manage campaigns, track leads and optimise customer experiences. 

Marketing dashboard in HubSpot
(Image Source)

Why HubSpot: HubSpot’s all-in-one platform is ideal for marketing and sales teams that want to paint a complete picture of their combined efforts. Analytics features let marketers track visitors and campaign performance, while automation tools nurture prospects and turn visitors into MQLs.

Standout features include an easy-to-use dashboard, marketing automation, A/B testing and pre-made reports. 

Strengths:

• A very intuitive dashboard makes it easy for users of all abilities to navigate
• Powerful automation features help marketers save time
• There’s strong customer support and a large community of certified partners

Common community critiques:

• Pricing is expensive and increases quickly 
• Engagement tracking is less granular than dedicated behavioural analytics tools
• The wide range of features can lead to analysis paralysis

Pricing: Marketing Hub Professional starts at $800 per month. Marketing Hub Enterprise starts from $3,600 per month.

6. Whatagraph

Whatagraph is a marketing analytics and automated reporting platform that helps agencies and in-house teams turn complex, multi-channel marketing data into visually easy-to-understand reports.

Web analytics report in Whatagraph
(Image Source)

Why Whatagraph: Whatagraph is a great choice for companies that prioritise data visualisation. It lets users combine data from over 50 sources into customisable dashboards and reports. There are plenty of ready-made templates as well as a drag-and-drop interface in case you want to create your own.

Standout features include direct integration with 50+ data sources, data blending across different channels, digital ad spend tracking and automated report creation.

Strengths:

• A very intuitive and user-friendly interface that lets anyone start building reports immediately
• Visually appealing reports make it easy to share insights with stakeholders
• Highly responsive support team

Common community critiques:

• No freemium pricing
• It can take users time to get to grips with Whatagraph’s wide range of features
• It lacks native integrations for some platforms

Pricing: Available on request

7. Google Analytics

Google Analytics offers two analytics platforms: GA4 and GA360. GA4 is Google’s free analytics solution you’re probably familiar with. GA360 is the premium, enterprise-level version of GA4. It’s built for large organisations with complex analytics needs and high data volumes.

Home page in GA4
(Image Source)

Why Google: GA4 is a well-known and widely used analytics platform. It’s free, familiar to most people and has plenty of online resources to help if you get stuck. However, it doesn’t protect user privacy, uses data sampling and lacks advanced features like behavioural analytics. 

GA360 users can configure the platform to be more privacy-friendly, but there are still better (and cheaper) privacy-friendly alternatives.

Standout features include event-based tracking, cross-platform tracking, audience segmentation and real-time reporting.

Strengths:

• GA4 is free to use
• There’s no shortage of online guides
• Cross-platform tracking helps you get a better view of your visitors 

Common community critiques:

• Not privacy focused or GDPR-compliant
• Data sampling muddles insights
• Both GA4 and GA360 look and are very different from Universal Analytics

Pricing: GA4 is free to use. GA360 pricing is available on request

What are the benefits of marketing analytics tools

Research by Supermetrics reveals that marketing teams are using 230% more data than they did in 2020. 

Analytics tools are the primary means of generating marketing data, but they have other uses as well. Here are four reasons every department needs a comprehensive analytics platform:

• Track marketing efforts. Marketing analytics offers a unified view of all your campaigns across channels — from paid ads and social media to email and organic search. By consolidating data from multiple sources, these platforms help marketers monitor campaign performance in real time and prove campaign effectiveness to stakeholders. 
  
• Improve customer understanding. Analytics platforms that have built-in behavioural tracking capabilities like heatmaps and session recordings help marketers generate qualitative and quantitative data that reveals how users interact with your site, what content resonates and where friction points occur.
  
• Optimise web and marketing experiences. Marketing is a game of continuous improvement. Analytics platforms help marketing teams attribute conversions to specific campaigns, refine user journeys with A/B testing and improve the overall experience. 
  
• Drive more conversions. Ultimately, the goal of marketing analytics is to increase conversions, whether that means sales, sign-ups or other events. Performance insights help marketers fine-tune their strategies, target high-value segments, and craft campaigns that move prospects down the funnel more efficiently. In a world where marketing budgets are falling by 15% year-on-year, it’s important to squeeze every drop of ROI from your campaigns. 

Top features to look for in a marketing analytics tool

With so many platforms to choose from, picking the right analytics tool can be a challenge. 

Make it easier for yourself by looking for a tool that offers features to enhance your insights while ensuring your business remains compliant with data privacy regulations. 

Advanced analytics features

Don’t settle for a simple web analytics tool or try to juggle different analytics platforms for each channel. Instead, choose a single tool that provides a range of advanced analytics features, including the following:

• Marketing attribution
• Cohort analysis
• Session recordings and heatmaps
• Ecommerce analytics
• SEO metrics

By doing so, you’ll get everything you need from a single platform. This will keep costs down and make managing marketing data much easier.

Data visualisation

A great marketing analytics tool will offer customizable dashboards and reports that marketers can use to make sense of complex data. Look for:

• Drag-and-drop interfaces
• Pre-built templates
• Detailed visitor profiles

Data visualisation not only aids decision-making but also helps communicate results clearly to non-technical team members and executives.

Near-real-time reporting

Many platforms will claim to offer real-time reporting. But that’s rarely possible. Instead, choose tools with near-real-time reporting that help marketers measure the impact of campaigns as quickly as possible. 

Matomo, for example, offers a Visits in Real-time Report that lets you see the flow of visitors on your site and shows how many people visited in the last 30 minutes and 24 hours. 

Visits Overview in Matomo

The report refreshes every 5 seconds to display new visits and tracks a range of visitor attributes, including country, operating system, referrer, time spent on site and whether they are a new or returning visitor. 

Data security and privacy

Data privacy should be a top priority for modern marketers. Employing ethical analytics and data practices will mean you don’t have to annoy users with cookie banners. But it also improves trust and minimises legal risk.

Choose analytics tools that are transparent about data collection, offer robust privacy controls, and comply with regulations like GDPR and CCPA. Features such as anonymised tracking, customisable consent banners and secure data storage help protect both your business and your customers.

Matomo has all of these features and more, protecting your visitors’ privacy in a dozen different ways. 

100% data ownership and no sampling

A lot of analytics platforms don’t let you own or properly use your data. Data sampling — where tools only analyse a portion of your data — is a particular problem in Google Analytics. It clouds insights, meaning marketers make decisions based on guesses, not facts. 

Who owns your data matters, too. When you use a platform like Google Analytics, you give permission for Google to use your customers’ data for advertising purposes. 

Instead of trading your customers’ data for free analytics, use a platform that gives you 100% ownership of your data. Matomo does this in a couple of ways:

• Matomo On-Premise offers 100% data ownership, as it’s hosted on your own servers. You choose where to store it, and we cannot access it. 
  
• Matomo Analytics for WordPress provides a self-hosted WordPress-specific option that offers the benefits of On-Premise without the technical setup.
  
• Matomo Cloud subscriptions are governed by our Terms, which state that you own all rights, titles and interests in your users’ data. In other words, we can’t sell it to third parties or claim ownership. 

While Matomo products may change, our commitment to privacy never will. You’ll always be able to self-host Matomo for free. 

Trust Matomo for comprehensive marketing analytics

The right analytics platform empowers marketers to track campaigns across channels, gain deep insights into customer behaviour, optimise user experiences and ultimately drive more conversions. 

If you care about collecting data while respecting your users’ privacy, a tool like Matomo is the way to go. Try Matomo free for 21 days. No credit card required.

It’s a well-rounded tool with features that help product teams understand how customers navigate their website or app. It’s also straightforward to set up, GDPR compliant, and easy for non-technical folks to use, thanks to an intuitive UI and drag-and-drop reports. 

However, Mixpanel is just one of many product and web analytics platforms. Some are cheaper, others are more secure, and a few have more advanced or specialist features.

This article will explore the leading Mixpanel alternatives for product teams and marketers. We’ll cover their key features, what users love about them, and why they may (or may not) be the right pick for you. 

Mixpanel: an overview

Let’s start by giving Mixpanel its dues. The platform does a great job of arming product teams with an arsenal of tools to track the impact of their updates, find ways to boost engagement and track which features users love. 

Marketing teams use the platform to track customers through the sales funnel, attribute marketing campaigns and find ways to optimise spend. 

There’s plenty to like about Mixpanel, including: 

• Easy setup and maintenance: Mixpanel’s onboarding flow allows you to build a tracking plan and choose the specific events to measure. When Mixpanel collects data, you’ll see an introductory “starter board.” 
• Generous free plan: Mixpanel doesn’t limit freemium users like some platforms. Collect data on 20 million monthly events, use pre-built templates and access its Slack community. There are also no limits on collaborators or integrations.
• Extensive privacy configurations: Mixpanel provides strong consent management configurations. Clients can let their users opt out of tracking, disable geolocation and anonymise their data. It also automatically deletes user data after five years and offers an EU Data Residency Program that can help customers meet GDPR regulations. 
• Comprehensive features: Mixpanel gives marketers and product teams the tools and features they need to understand the customer, improve the product and increase conversions. 
• Easy-to-use UI: The platform prioritises self-service data, meaning users don’t need to be technically minded to use Mixpanel. Drag-and-drop dashboards democratise access to data and let anyone on your team find answers to their questions.

You wouldn’t be reading this page if Mixpanel offered everything, though. No platform is perfect, and there are several reasons people may want to look for a Mixpanel alternative:

• No self-hosted option: You’ll never have complete control over your data with Mixpanel due to the lack of a self-hosted option. Data will always live on Mixpanel’s servers, meaning compliance with data regulations like GDPR isn’t a given.
• Lack of customisation: Mixpanel doesn’t offer much flexibility when it comes to visualising data. While the platform’s in-built reports are accessible to everyone, you’ll need a developer to build custom reports. 
• Not open source: Mixpanel’s proprietary software doesn’t provide the transparency, security and community that comes with using open-source software like Matomo. Proprietary software isn’t inherently wrong, but it could mean your analytics solution isn’t future-proof. 
• Steep learning curve: The learning curve can be steep unless you’re a developer. While setting up the software is straightforward, Mixpanel’s reliance on manual tracking means teams must spend a lot of time creating and structuring events to collect the data they need.

If any of those struck a chord, see if one of the following seven Mixpanel alternatives might better fulfil your needs. 

The top 7 Mixpanel alternatives

Now, let’s look at the alternatives.

We’ll explain exactly how each platform differs from Mixpanel, its standout features, strengths, common community critiques, and when it may be (or may not be) the right choice. 

1. Matomo

Matomo is a privacy-focused, open-source web and mobile analytics platform. As a proponent of an ethical web, Matomo prioritises data ownership and privacy protection. 

It’s a great Mixpanel alternative for those who care about data privacy. You own 100% of your data and will always comply with data regulations like GDPR when using the platform. 

Main dashboard with visits log, visits over time, visitor map, combined keywords, and traffic sources
(Image Source)

Matomo isn’t short on features, either. Product teams and marketers can evaluate the entire user journey, capture detailed visitor profiles, combine web, mobile and app reports, and use custom reporting to generate the specific insides they need.

Key features:

• Complete app and web analytics: Matomo tracks performance metrics and KPIs across web, app and mobile. Understand which pages users visit, how long they stay and how they move between devices.
• Marketing attribution: Built-in marketing attribution capabilities make it easy for marketers to pinpoint their most profitable campaigns and channels. 
• User behaviour tracking: Generate in-depth user behaviour data thanks to heatmaps, form analytics and session recordings.

Strengths

• On-premise and cloud versions: Use Matomo for free on your servers or subscribe to Matomo Cloud for hosting and additional support. Either way, you remain in control of your data.
• Exceptional customer support: On-premise and Matomo Cloud users get free access to the forum. Cloud customers get dedicated support, which is available at an additional cost for on-premise customers. 
• Consent-free tracking: Matomo doesn’t ruin the user’s experience with cookie banners. 
• Open-source software: Matomo’s software is free to use, modify, and distribute. Users get a more secure, reliable and transparent solution thanks to the community of developers and contributors working on the project. Matomo will never become proprietary software, so there’s no risk of vendor lock-in. You will always have access to the source code, raw data and APIs. 

Common community critiques:

• On-premise setup: The on-premise version requires some technical knowledge and a server.
• App tracking features: Some features, like heatmaps, available on web analytics aren’t available in-app analytics. Features may also differ between Android SDK and iOS SDK.

Price: 

Matomo has three plans:

• Free: on-premise analytics is free to use
• Cloud: Hosted business plans start at €22 per month
• Enterprise: custom-priced, cloud-hosted enterprise plan tailored to meet a business’s specific requirements.

There’s a free 21-day trial for Matomo Cloud and a 30-day plugin trial for Matomo On-Premise.

2. Adobe Analytics

Adobe Analytics is an enterprise analytics platform part of the Adobe Experience Cloud. This makes it a great Mixpanel alternative for those already using other Adobe products. But, getting the most from the platform is challenging without the rest of the Adobe ecosystem. 

Adobe Analytics Analysis Workspace training tutorial
(Image Source)

Adobe Analytics offers many marketing tools, but product teams may find their offer lacking. Small or inexperienced teams may also need help using this feature-heavy platform. 

Key features:

• Detailed web and marketing analytics: Adobe lets marketers draw in data from almost any source to get a comprehensive view of the customer journey. 
• Marketing attribution: There’s a great deal of flexibility when crediting conversions. There are unlimited attribution models, too, including both paid and organic media channels.
• Live Stream: This feature lets brands access raw data in near real time (with a 30- to 90-second delay) to assess the impact of marketing campaigns as soon as they launch. 

Strengths:

• Enterprise focus: Adobe Analytics’s wide range of advanced features makes It attractive to large companies with one or more high-traffic websites or apps. 
• Integrations: Adobe Analytics integrates neatly with other Adobe products like Campaign and Experience Cloud). Access marketing, analytics and content management tools in one place. 
• Customisation: The platform makes it easy for users to tailor reports and dashboards to their specific needs.

Common community critiques:

• Few product analytics features: While marketers will likely love Adobe, product teams may find it lacking. For example, the heatmap tool isn’t well developed. You’ll need to use Adobe Target to run A/B tests.
• Complexity: The sheer number of advanced features can make Adobe Analytics a confusing experience for inexperienced or non-technically minded users. While a wealth of support documentation is available, it will take longer to generate value. 
• Price: Adobe Analytics costs several thousand dollars monthly, making it suitable only for enterprise clients.

Price: 

Adobe offers three tiers: Select, Prime and Ultimate. Pricing is only available on request.

3. Amplitude

Amplitude is a product analytics and event-tracking platform. It is arguably the most like-for-like platform on this list, and there is a lot of overlap between Amploitduce’s and Mixpanel’s capabilities. 

The Ask Amplitude™ feature helps build and analyse conversion funnel charts.
(Image Source)

The platform is an excellent choice for marketers who want to create a unified view of the customer by tracking them across different devices. This is possible with several other analytics platforms on this list (Matomo included), but Mixpanel doesn’t centralise data from web and app users in a signal report. 

Amplitude also has advanced features Mixpanel doesn’t have, like feature management and AI, as well as better customisation. 

Key features:

• Product analytics: Amplitude comes packed with features product teams will use regularly, including customer journey analysis, session replays and heatmaps. 
• AI: Amplitude AI can clean up data, generate insights and detect anomalies.
• Feature management: Amplitude provides near-real-time feedback on feature usage and adoption rates so that product teams can analyse the impact of their work. Developers can also use the platform to manage progressive rollouts. 

Strengths:

• Self-serve reporting: The platform’s self-serve nature means employees of all levels and abilities can get the insights they need. That includes data teams that want to run detailed and complex analyses. 
• Integrated web experimentation. Product teams or marketers don’t need a third-party tool to run A/B tests because Amplitude has a comprehensive feature that lets users set up tests, collect data and create reports. 
• Extensive customer support: Amplitude records webinars, holds out-of-office sessions and runs a Slack community to help customers extract as much value as possible.

Common community critiques:

• Off-site tracking: While Amplitude has many features for tracking customer interaction across your product, it lacks ways to track customers once they are off-site. This is not great for marketing attribution, for example, or growing search traffic. 
• Too complex: The sheer number of things Amplitude tracks can overwhelm inexperienced users who must spend time learning how to use the platform. 
• Few templates: Few stock templates make getting started with Amplitude even harder. Users have to create reports from scratch rather than customise a stock graph. 

Price: 

• Starter: Free to track up to 50,000 users per month. 
• Plus: $49 per month to track up to 300,000 users.
• Growth: Custom pricing for no tracking limits
• Enterprise: Custom pricing for dedicated account managers and predictive analytics
  

4. Google Analytics

Google Analytics is the most popular web analytics platform. It’s completely free to use and easy to install. Although there’s no customer support, the thousands of online how-to videos and articles go some way to making up for it. 

GA dashboard showing acquisition, conversion and behaviour data across all channels 
(Image Source)

Most people are familiar with Google’s web analytics data, which makes it a great Mixpanel alternative for marketers. However, product teams may struggle to get the qualitative data they need.

Key features:

• User and conversion tracking: People don’t just use Google Analytics because it’s free. The platform boasts a competitive user engagement and conversion tracking offering, which lets businesses of any size understand how consumers navigate their sites and make purchases. 
• Audience segmentation: Segment audiences based on time and event parameters.
• Google Ads integration: Track users from the moment they interact with one of your ads. 

Strengths:

• It’s free: Web and product analytics platforms can cost hundreds of dollars monthly and put a sizable dent in a small business marketing budget. Google provides the basic tools most marketers need for free.
• Cross-platform tracking: GA4 lets teams track mobile and web analytics in one place, which wasn’t possible in Universal Analytics.
• A wealth of third-party support: There’s no shortage of Google Analytics tutorials on YouTube to help you set up and use the platform. 

Common community critiques:

• Data privacy concerns: There are concerns about Google’s lack of compliance with regulations like GDPR. The workaround is asking people for permission to collect their data, but that requires a consent pop-up that can disrupt the user experience. 
• No CRO features: Google Analytics lacks the conversion optimisation features of other tools in this list, including Matomo. It can’t record sessions, track user interactions via a heatmap or run A/B tests. 
• AI data sampling: Google generates insights using AI-powered data sampling rather than analysing your actual data, which may make your data inaccurate. 

Price: 

Google Analytics is free to use. Google also offers a premium version, GA 360, which starts at $50,000 per year. 

5. Heap

Heap is a digital insights and product analytics platform. It gives product managers and marketers the quantitative and qualitative data they need to improve conversion rates, improve product features, and reduce churn. 

Heap marketing KPI dashboard
(Image Source)

The platform offers everything you’d expect from a product analytics perspective, including session replays, heatmaps and user journey analysis. It even has an AI tool that can answer your questions. 

Key features:

• Auto-capture: Unlike other analytics tools (Mixpanel and Google Analytics, for instance), you don’t need to manually code events. Heap’s auto-capture feature automatically collects every user interaction, allowing for retroactive analysis. 
• Segmentation: Create distinct customer cohorts based on behaviour. Integrate other platforms like Marketo to use that information to personalise marketing campaigns. 
• AI CoPilot: Heap has a generative AI tool, CoPilot, that answers questions like “How many people visited the About page last week?” It can also handle follow-up questions and suggest what to search next. 

Strengths:

• Integrations: Heap’s integrations allow teams to centralise data from dozens of third-party applications. Popular integrations include Shopify and Salesforce. Heap can also connect to your data warehouse. 
• Near real-time tracking: Heap has a live data feed that lets teams track user behaviour in near real-time (there’s a 15-second delay).
• Collaboration: Heap facilitates cross-department collaboration via shared spaces and shared reports. You can also share session replays across teams.

Common community critiques:

• Struggles at scale: Heap’s auto-capture functionality can be more of a pain than a perk when working at scale. Sites with a million or more weekly visitors may need to limit data capture.
• Data overload: Heap tracks so much data it can be hard to find the specific events you want to measure.
• Poor-quality graphics: Heap’s visualisations are basic and may not appeal to non-technically minded users.

Price: 

Heap offers four plans with pricing available on request.

• Free
• Growth
• Pro
• Premier
  

6. Hotjar

Hotjar is a product experience insight tool that analyses why users behave as they do. The platform collects behavioural data using heatmaps, surveys and session recordings. 

It’s a suitable alternative for product teams and marketers who care about collecting qualitative rather than quantitative data. 

New heatmap feature in hotjar
(Image Source)

It’s not your typical analytics platform, however. Hotjar doesn’t track site visits or conversions, so teams use it alongside a web analytics platform like Google Analytics or Matomo.

Key features:

• Surveys: Product teams can place surveys on specific pages to capture quantitative and qualitative data. 
• Heatmaps: Hotjar provides several heatmaps — click, scroll and interaction — that show how users behave when browsing your site. 
• Session recordings: Support quantitative analytics data with videos of genuine user behaviour. It’s like watching someone browsing your site over their shoulder. 

Strengths:

• User-friendly interface: The tool is easy to navigate and accessible to all employees. Anyone can start using it quickly. 
• Funnel analysis: Use Hotjar’s range of tools to analyse your entire funnel, identifying friction points and opportunities to improve the customer experience. 
• Cross-platform tracking: Hotjar compares user behaviour across desktop, mobile and app. 

Common community critiques:

• Limited web analytics: While Hotjar is great for understanding customer behaviour, it doesn’t collect standard web analytics data. 
• Data retention: Hotjar only retains data for one month to a year on some plans.
• Impacts page speed: The tool’s code impacts your site’s performance, leading to slower load times. 

Price: 

• Free: Up to five thousand monthly sessions, including screen recordings and heatmaps
• Growth: $49 per month for 7,000 to 10,000 monthly sessions
• Pro: Custom pricing for up to 500 million monthly sessions
• Enterprise: Custom pricing for up to 6 billion monthly sessions. 
  

7. Kissmetrics

Kissmetrics is a web and mobile analytics platform that aims to help teams generate more revenue and acquire more users through product-led growth. 

As such, the platform offers more to marketers than product teams — particularly online store owners and SaaS businesses. 

Kissmetrics funnel report 
(Image Source)

Kissmetrics provides a suite of behavioural analytics tools that analyse how customers move through your funnel, where they drop off and why. That’s great for marketers, but product teams will struggle to understand how customers actually use their product once they’ve converted.

Key features:

• User journey mapping: Follow individual customer journeys to learn how each customer finds and engages with your brand. 
• Funnel analysis: Funnel reports help marketers track cart abandonments and other drop-offs along the customer journey. 
• A/B testing: Kissmetrics’s A/B testing tool measures how customers respond to different page layouts

Strengths:

• Detailed revenue metrics: Kissmetrics makes measuring customer lifetime value, churn rate, and other revenue-focused KPIs easy. 
• Stellar onboarding experience: Kissmetrics gives new users a detailed walkthrough and tutorial, which helps non-technical users get up to speed. 
• Integrations: Integrate data from dozens of platforms and tools, such as Facebook, Instagram, Shopify, and Woocommerce, so all your data is in one place. 

Common community critiques:

• Predominantly web-based: Kissmetrics focuses on web-based traffic over app- or cross-platform tracking. It may be fine for some teams, but product managers or marketers who track users across apps and smartphones may want to look elsewhere. 
• Slow to load large data sources: The platform can be slow to load, react to, and analyse large volumes of data, which could be an issue for enterprise clients. 
• Price: Kissmetrics is significantly more expensive than Mixpanel. There is no freemium tier, meaning you’ll need to pay at least $199 monthly. 

Price: 

• Silver: $199 per month for up to 2 million monthly events
• Gold: $499 per month for up to five million monthly events
• Platinum: Custom pricing

Switch from Mixpanel to Matomo

When it comes to extracting deep insights from user data while balancing compliance and privacy protection, Mixpanel delivers mixed results. If you want a more straightforward alternative, more websites chose Matomo over Mixpanel for their analytics because of its:

• Accurate web analytics collected in an ethical, GDPR-compliant manner
• Behavioural analytics (like heatmaps and session recordings) to understand how users engage with your site
• Rolled-up cross-platform reporting for mobile and apps
• Flexibility and customisation with 250+ settings, plentiful plugins and integrations, APIs, raw data access
• Open-source code to create plugins to fit your specific business needs
• 100% data ownership with Matomo On-Premise and Matomo Cloud

Over one million websites in 190+ countries use Matomo’s powerful web analytics platform. Join them today by starting a free 21-day trial — no credit card required.