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• Unveiling GA4 Issues : 8 Questions from a Marketer That GA4 Can’t Answer

  8 janvier 2024, par Alex

  It’s hard to believe, but Universal Analytics had a lifespan of 11 years, from its announcement in March 2012. Despite occasional criticism, this service established standards for the entire web analytics industry. Many metrics and reports became benchmarks for a whole generation of marketers. It truly was an era.

  For instance, a lot of marketers got used to starting each workday by inspecting dashboards and standard traffic reports in the Universal Analytics web interface. There were so, so many of those days. They became so accustomed to Universal Analytics that they would enter reports, manipulate numbers, and play with metrics almost on autopilot, without much thought.

  However, six months have passed since the sunset of Universal Analytics – precisely on July 1, 2023, when Google stopped processing requests for resources using the previous version of Google Analytics. The time when data about visitors and their interactions with the website were more clearly structured within the UA paradigm is now in the past. GA4 has brought a plethora of opportunities to marketers, but along with those opportunities came a series of complexities.

  GA4 issues

  Since its initial announcement in 2020, GA4 has been plagued with errors and inconsistencies. It still has poor and sometimes illogical documentation, numerous restrictions, and peculiar interface solutions. But more importantly, the barrier to entry into web analytics has significantly increased.

  If you diligently follow GA4 updates, read the documentation, and possess skills in working with data (SQL and basic statistics), you probably won’t feel any problems – you know how to set up a convenient and efficient environment for your product and marketing data. But what if you’re not that proficient ? That’s when issues arise.

  In this article, we try to address a series of straightforward questions that less experienced users – marketers, project managers, SEO specialists, and others – want answers to. They have no time to delve into the intricacies of GA4 but seek access to the fundamentals crucial for their functionality.

  Previously, in Universal Analytics, they could quickly and conveniently address their issues. Now, the situation has become, to put it mildly, more complex. We’ve identified 8 such questions for which the current version of GA4 either fails to provide answers or implies that answers would require significant enhancements. So, let’s dive into them one by one.

  Question 1 : What are the most popular traffic sources on my website ?

  Seemingly a straightforward question. What does GA4 tell us ? It responds with a question : “Which traffic source parameter are you interested in ?”

  

  Wait, what ?

  People just want to know which resources bring them the most traffic. Is that really an issue ?

  Unfortunately, yes. In GA4, there are not one, not two, but three traffic source parameters :

  1. Session source.
  2. First User Source – the source of the first session for each user.
  3. Just the source – determined at the event or conversion level.

  If you wanted to open a report and draw conclusions quickly, we have bad news for you. Before you start ranking your traffic sources by popularity, you need to do some mental work on which parameter and in what context you will look. And even when you decide, you’ll need to make a choice in the selection of standard reports : work with the User Acquisition Report or Traffic Acquisition.

  Yes, there is a difference between them : the first uses the First User Source parameter, and the second uses the session source. And you need to figure that out too.

  Question 2 : What is my conversion rate ?

  This question concerns everyone, and it should be simple, implying a straightforward answer. But no.

  

  In GA4, there are three conversion metrics (yes, three) :

  1. Session conversion – the percentage of sessions with a conversion.
  2. User conversion – the percentage of users who completed a conversion.
  3. First-time Purchaser Conversion – the share of active users who made their first purchase.

  If the last metric doesn’t interest us much, GA4 users can still choose something from the remaining two. But what’s next ? Which parameters to use for comparison ? Session source or user source ? What if you want to see the conversion rate for a specific event ? And how do you do this in analyses rather than in standard reports ?

  In the end, instead of an answer to a simple question, marketers get a bunch of new questions.

  Question 3. Can I trust user and session metrics ?

  Unfortunately, no. This may boggle the mind of those not well-versed in the mechanics of calculating user and session metrics, but it’s the plain truth : the numbers in GA4 and those in reality may and will differ.

  

  The reason is that GA4 uses the HyperLogLog++ statistical algorithm to count unique values. Without delving into details, it’s a mechanism for approximate estimation of a metric with a certain level of error.

  This error level is quite well-documented. For instance, for the Total Users metric, the error level is 1.63% (for a 95% confidence interval). In simple terms, this means that 100,000 users in the GA4 interface equate to 100,000 1.63% in reality.

  Furthermore – but this is no surprise to anyone – GA4 samples data. This means that with too large a sample size or when using a large number of parameters, the application will assess your metrics based on a partial sample – let’s say 5, 10, or 30% of the entire population.

  It’s a reasonable assumption, but it can (and probably will) surprise marketers – the metrics will deviate from reality. All end-users can do (excluding delving into raw data methodologies) is to take this error level into account in their conclusions.

  Question 4. How do I calculate First Click attribution ?

  You can’t. Unfortunately, as of late, GA4 offers only three attribution models available in the Attribution tab : Last Click, Last Click For Google Ads, and Data Driven. First Click attribution is essential for understanding where and when demand is generated. In the previous version of Google Analytics (and until recently, in the current one), users could quickly apply First Click and other attribution models, compare them, and gain insights. Now, this capability is gone.

  

  Certainly, you can look at the conversion distribution considering the First User Source parameter – this will be some proxy for First Click attribution. However, comparing it with others in the Model Comparison tab won’t be possible. In the context of the GA4 interface, it makes sense to forget about non-standard attribution models.

  Question 5. How do I account for intra-session traffic ?

  Intra-session traffic essentially refers to a change in traffic sources within a session. Imagine a scenario where a user comes to your site organically from Google and, within a minute, comes from an email campaign. In the previous version of Google Analytics, a new session with the traffic source “e-mail” would be created in such a case. But now, the situation has changed.

  A session now only ends in the case of a timeout – say, 30 minutes without interaction. This means a session will always have a source from which it started. If a user changes the source within a session (clicks on an ad, from email campaigns, and so on), you won’t know anything about it until they convert. This is a significant blow to intra-session traffic since their contribution to traffic remains virtually unnoticed. 

  Question 6. How can I account for users who have not consented to the use of third-party cookies ?
  

  You can’t. Google Consent Mode settings imply several options when a user rejects the use of 3rd party cookies. In GA4 and BigQuery, depersonalized cookieless pings will be sent. These pings do not contain specific client_id, session_id, or other custom dimensions. As a result, you won’t be able to consider them as users or link the actions of such users together.

  Question 7. How can I compare data in explorations with the previous year ?

  The maximum data retention period for a free GA4 account is 14 months. This means that if the date range is wider, you can only use standard reports. You won’t be able to compare or view cohorts or funnels for periods more than 14 months ago. This makes the product functionality less rich because various report formats in explorations are very convenient for comparing specific metrics in easily digestible reports.

  

  Of course, you always have the option to connect BigQuery and store raw data without limitations, but this process usually requires the involvement of an advanced analyst. And precisely this option is unavailable to most marketers in small teams.

  Question 8. Is the data for yesterday accurate ?

  Unknown. Google declares that data processing in GA4 takes up to 48 hours. And although this process is faster, most users still have room for frustration. And they can be understood.

  

  What does “data processing takes 24-48 hours” mean ? When will the data in reports be complete ? For yesterday ? Or the day before yesterday ? Or for all days that were more than two days ago ? Unclear. What should marketers tell their managers when they were asked if all the data is in this report ? Well, probably all of it… or maybe not… Let’s wait for 48 hours…

  Undoubtedly, computational resources and time are needed for data preprocessing and aggregation. It’s okay that data for today will not be up-to-date. And probably not for yesterday either. But people just want to know when they can trust their data. Are they asking for too much : just a note that this report contains all the data sent and processed by Google Analytics ?

  What should you do ?

  Credit should be given to the Google team – they have done a lot to enable users to answer these questions in one form or another. For example, you can use data streaming in BigQuery and work with raw data. The entry threshold for this functionality has been significantly lowered. In fact, if you are dissatisfied with the GA4 interface, you can organize your export to BigQuery and create your own reports without (almost) any restrictions.

  Another strong option is the widespread launch of GTM Server Side. This allows you to quite freely modify the event model and essentially enrich each hit with various parameters, doing this in a first-party context. This, of course, reduces the harmful impact of most of the limitations described in this text.

  But this is not a solution.

  The users in question – marketers, managers, developers – they do not want or do not have the time for a deep dive into the issue. And they want simple answers to simple (it seemed) questions. And for now, unfortunately, GA4 is more of a professional tool for analysts than a convenient instrument for generating insights for not very advanced users.

  Why is this such a serious issue ?

  The thing is – and this is crucial – over the past 10 years, Google has managed to create a sort of GA-bubble for marketers. Many of them have become so accustomed to Google Analytics that when faced with another issue, they don’t venture to explore alternative solutions but attempt to solve it on their own. And almost always, this turns out to be expensive and inconvenient.

  However, with the latest updates to GA4, it is becoming increasingly evident that this application is struggling to address even the most basic questions from users. And these questions are not fantastically complex. Much of what was described in this article is not an unsolvable mystery and is successfully addressed by other analytics services.

  Let’s try to answer some of the questions described from the perspective of Matomo.

  Question 1 : What are the most popular traffic sources ? [Solved]

  In the Acquisition panel, you will find at least three easily identifiable reports – for traffic channels (All Channels), sources (Websites), and campaigns (Campaigns). 

  

  With these, you can quickly and easily answer the question about the most popular traffic sources, and if needed, delve into more detailed information, such as landing pages.

  Question 2 : What is my conversion rate ? [Solved]

  Under Goals in Matomo, you’ll easily find the overall conversion rate for your site. Below that you’ll have access to the conversion rate of each goal you’ve set in your Matomo instance.

  

  Question 3 : Can I trust user and session metrics ? [Solved]

  Yes. With Matomo, you’re guaranteed 100% accurate data. Matomo does not apply sampling, does not employ specific statistical algorithms, or any analogs of threshold values. Yes, it is possible, and it’s perfectly normal. If you see a metric in the visits or users field, it accurately represents reality by 100%.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Question 4 : How do I calculate First Click attribution ? [Solved]
  
  You can do this in the same section where the other 5 attribution models, available in Matomo, are calculated – in the Multi Attribution section.

  

  You can choose a specific conversion and, in a few clicks, calculate and compare up to 3 marketing attribution models. This means you don’t have to spend several days digging through documentation trying to understand how a particular model is calculated. Have a question – get an answer.

  Question 5 : How do I account for intra-session traffic ? [Solved]

  Matomo creates a new visit when a user changes a campaign. This means that you will accurately capture all relevant traffic if it is adequately tagged. No campaigns will be lost within a visit, as they will have a new utm_campaign parameter.

  This is a crucial point because when the Referrer changes, a new visit is not created, but the key lies in something else – accounting for all available traffic becomes your responsibility and depends on how you tag it.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Question 6 : How can I account for users who have not consented to the use of third-party cookies ? [Solved]

  Google Analytics requires users to accept a cookie consent banner with “analytics_storage=granted” to track them. If users reject cookie consent banners, however, then Google Analytics can’t track these visitors at all. They simply won’t show up in your traffic reports. 

  Matomo doesn’t require cookie consent banners (apart from in the United Kingdom and Germany) and can therefore continue to track visitors even after they have rejected a cookie consent screen. This is achieved through a config_id variable (the user identifier equivalent which is updating once a day). 

  

  This means that virtually all of your website traffic will be tracked regardless of whether users accept a cookie consent banner or not.

  Question 7 : How can I compare data in explorations with the previous year ? [Solved]
  

  There is no limitation on data retention for your aggregated reports in Matomo. The essence of Matomo experience lies in the reporting data, and consequently, retaining reports indefinitely is a viable option. So you can compare data for any timeframe. 7

  

• Attribution Tracking (What It Is and How It Works)

  23 février 2024, par Erin

  Facebook, TikTok, Google, email, display ads — which one is best to grow your business ? There’s one proven way to figure it out : attribution tracking.

  Marketing attribution allows you to see which channels are producing the best results for your marketing campaigns.

  In this guide, we’ll show you what attribution tracking is, why it’s important and how you can leverage it to accelerate your marketing success.

  What is attribution tracking ?

  By 2026, the global digital marketing industry is projected to reach $786.2 billion.

  With nearly three-quarters of a trillion U.S. dollars being poured into digital marketing every year, there’s no doubt it dominates traditional marketing.

  The question is, though, how do you know which digital channels to use ?

  By measuring your marketing efforts with attribution tracking.

  

  So, what is attribution tracking ?

  Attribution tracking is where you use software to keep track of different channels and campaign efforts to determine which channel you should attribute conversion to.

  In other words, you can (and should) use attribution tracking to analyse which channels are pushing the needle and which ones aren’t.

  By tracking your marketing efforts, you’ll be able to accurately measure the scale of impact each of your channels, campaigns and touchpoints have on a customer’s purchasing decision.

  If you don’t track your attribution, you’ll end up blindly pouring time, money, and effort into activities that may or may not be helpful.

  Attribution tracking simply gives you insight into what you’re doing right as a marketer — and what you’re doing wrong.

  By understanding which efforts and channels are driving conversions and revenue, you’ll be able to properly allocate resources toward winning channels to double down on growth.

  Matomo lets you track attribution across various channels. Whether you’re looking to track your conversions through organic, referral websites, campaigns, direct traffic, or social media, you can see all your conversions in one place.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Why attribution tracking is important

  Attribution tracking is crucial to succeed with your marketing since it shows you your most valuable channels.

  It takes the guesswork out of your efforts.

  You don’t need to scratch your head wondering what made your campaigns a success (or a failure).

  While most tools show you last click attribution by default, using attribution tracking, or marketing attribution, you can track revenue and conversions for each touchpoint.

  For example, a Facebook ad might have no led to a conversion immediately. But, maybe the visitor returned to your website two weeks later through your email campaign. Attribution tracking will give credit over longer periods of time to see the bigger picture of how your marketing channels are impacting your overall performance.

  Here are five reasons you need to be using attribution tracking in your business today :

  

  1. Measure channel performance

  The most obvious way attribution tracking helps is to show you how well each channel performs.

  When you’re using a variety of marketing channels to reach your audience, you have to know what’s actually doing well (and what’s not).

  This means having clarity on the performance of your :

  • Emails
  • Google Ads
  • Facebook Ads
  • Social media marketing
  • Search engine optimisation (SEO)
  • And more

  Attribution tracking allows you to measure each channel’s ROI and identify how much each channel impacted your campaigns.

  It gives you a more accurate picture of the performance of each channel and each campaign.

  With it, you can easily break down your channels by how much they drove sales, conversions, signups, or other actions.

  With this information, you can then understand where to further allocate your resources to fuel growth.

  2. See campaign performance over longer periods of time

  When you start tracking your channel performance with attribution tracking, you’ll gain new insights into how well your channels and campaigns are performing.

  The best part — you don’t just get to see recent performance.

  You get to track your campaign results over weeks or months.

  For example, if someone found you through Google by searching a question that your blog had an answer to, but they didn’t convert, your traditional tracking strategy would discount SEO.

  But, if that same person clicked a TikTok ad you placed three weeks later, came back, and converted — SEO would receive some attribution on the conversion.

  Using an attribution tracking tool like Matomo can help paint a holistic view of how your marketing is really doing from channel to channel over the long run.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  3. Increase revenue

  Attribution tracking has one incredible benefit for marketers : optimised marketing spend.

  When you begin looking at how well your campaigns and your channels are performing, you’ll start to see what’s working.

  Attribution tracking gives you clarity into the performance of campaigns since it’s not just looking at the first time someone clicks through to your site. It’s looking at every touchpoint a customer made along the way to a conversion.

  By understanding what channels are most effective, you can pour more resources like time, money and labour into those effective channels.

  By doubling down on the winning channels, you’ll be able to grow like never before.

  Rather than trying to “diversify” your marketing efforts, lean into what’s working.

  This is one of the key strategies of an effective marketer to maximise your campaign returns and experience long-term success in terms of revenue.

  4. Improve profit margins

  The final benefit to attribution tracking is simple : you’ll earn more profit.

  Think about it this way : let’s say you’re putting 50% of your marketing spend into Facebook ads and 50% of your spend into email marketing.

  You do this for one year, allocating $500,000 to Facebook and $500,000 to email.

  Then, you start tracking attribution.

  You find that your Facebook ads are generating $900,000 in revenue. 

  That’s a 1,800% return on your investment.

  Not bad, right ?

  Well, after tracking your attribution, you see what your email revenue is.

  In the past year, you generated $1.7 million in email revenue.

  That’s a 3,400% return on your investment (close to the average return of email marketing across all industries).

  In this scenario, you can see that you’re getting nearly twice as much of a return on your marketing spend with email.

  So, the following year, you decide to go for a 75/25 split.

  Instead of putting $500,000 into both email and Facebook ads and email, you put $750,000 into email and $250,000 into Facebook ads.

  You’re still diversifying, but you’re doubling down on what’s working best.

  The result is that you’ll be able to get more revenue by investing the same amount of money, leaving you with higher profit margins.

  Different types of marketing attribution tracking

  There are several types of attribution tracking models in marketing.

  Depending on your goals, your business and your preferred method, there are a variety of types of attribution tracking you can use.

  Here are the six main types of attribution tracking :

  

  1. Last interaction

  Last interaction attribution model is also called “last touch.”

  It’s one of the most common types of attribution. The way it works is to give 100% of the credit to the final channel a customer interacted with before they converted into a customer.

  This could be through a paid ad, direct traffic, or organic search.

  One potential drawback of last interaction is that it doesn’t factor in other channels that may have assisted in the conversion. However, this model can work really well depending on the business.

  2. First interaction

  This is the opposite of the previous model.

  First interaction, or “first touch,” is all about the first interaction a customer has with your brand.

  It gives 100% of the credit to the channel (i.e. a link clicked from a social media post). And it doesn’t report or attribute anything else to another channel that someone may have interacted with in your marketing mix.

  For example, it won’t attribute the conversion or revenue if the visitor then clicked on an Instagram ad and converted. All credit would be given to the first touch which in this case would be the social media post. 

  The first interaction is a good model to use at the top of your funnel to help establish which channels are bringing leads in from outside your audience.

  3. Last non-direct

  Another model is called the last non-direct attribution model. 

  This model seeks to exclude direct traffic and assigns 100% credit for a conversion to the final channel a customer interacted with before becoming a customer, excluding clicks from direct traffic.

  For instance, if someone first comes to your website from an emai campaignl, and then, a week later, directly visits and buys a product, the email campaign gets all the credit for the sale.

  This attribution model tells a bit more about the whole sales process, shedding some more light on what other channels may have influenced the purchase decision.

  4. Linear

  Another common attribution model is linear.

  This model distributes completely equal credit across every single touchpoint (that’s tracked). 

  Imagine someone comes to your website in different ways : first, they find it through a Google search, then they click a link in an email from your campaign the next day, followed by visiting from a Facebook post a few days later, and finally, a week later, they come from a TikTok ad. 

  Here’s how the attribution is divided among these sources :

  • 25% Organic
  • 25% Email
  • 25% Facebook
  • 25% TikTok ad

  This attirubtion model provides a balanced perspective on the contribution of various sources to a user’s journey on your website.

  5. Position-based

  Position-based attribution is when you give 40% credit to both the first and last touchpoints and 20% credit is spread between the touchpoints in between.

  This model is preferred if you want to identify the initial touchpoint that kickstarted a conversion journey and the final touchpoint that sealed the deal.

  The downside is that you don’t gain much insight into the middle of the customer journey, which can make it hard to make effective decisions.

  For example, someone may have been interacting with your email newsletter for seven weeks, which allowed them to be nurtured and build a relationship with you.

  But that relationship and trust-building effort will be overlooked by the blog post that brought them in and the social media ad that eventually converted them.

  6. Time decay

  The final attribution model is called time decay attribution.

  This is all about giving credit based on the timing of the interactions someone had with your brand.

  For example, the touchpoints that just preceded the sale get the highest score, while the first touchpoints get the lowest score.

  For example, let’s use that scenario from above with the linear model :

  • 25% SEO
  • 25% Email
  • 25% Facebook ad
  • 25% Organic TikTok

  But, instead of splitting credit by 25% to each channel, you weigh the ones closer to the sale with more credit.

  Instead, time decay may look at these same channels like this :

  • 5% SEO (6 weeks ago)
  • 20% Email (3 weeks ago)
  • 30% Facebook ad (1 week ago)
  • 45% Organic TikTok (2 days ago)

  One downside is that it underestimates brand awareness campaigns. And, if you have longer sales cycles, it also isn’t the most accurate, as mid-stage nurturing and relationship building are underlooked. 

  Leverage Matomo : A marketing attribution tool

  Attribution tracking is a crucial part of leading an effective marketing strategy.

  But it’s impossible to do this without the right tools.

  A marketing attribution tool can give you insights into your best-performing channels automatically. 

  

  One of the best marketing attribution tools available is Matomo, a web analytics tool that helps you understand what’s going on with your website and different channels in one easy-to-use dashboard.

  With Matomo, you get marketing attribution as a plug-in or within Matomo On-Premise or for free in Matomo Cloud.

  The best part is it’s all done with crystal-clear data. Matomo gives you 100% accurate data since it doesn’t use data sampling on any plans like Google Analytics.

  To start tracking attribution today, try Matomo’s 21-day free trial. No credit card required.

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• Marketing Cohort Analysis : How To Do It (With Examples)

  12 janvier 2024, par Erin

  The better you understand your customers, the more effective your marketing will become. 

  The good news is you don’t need to run expensive focus groups to learn much about how your customers behave. Instead, you can run a marketing cohort analysis using data from your website analytics.

  A marketing cohort groups your users by certain traits and allows you to drill down to discover why they take the actions on your website they do. 

  In this article, we’ll explain what a marketing cohort analysis is, show you what you can achieve with this analytical technique and provide a step-by-step guide to pulling it off. 

  What is cohort analysis in marketing ?

  A marketing cohort analysis is a form of behavioural analytics where you analyse the behavioural patterns of users who share a similar trait to better understand their actions. 

  These shared traits could be anything like the date they signed up for your product, users who bought your service through a paid ad or email subscribers from the United Kingdom.

  It’s a fantastic way to improve your marketing efforts, allowing you to better understand complex user behaviours, personalise campaigns accordingly and improve your ROI. 

  You can run marketing analysis using an analytics platform like Google Analytics or Matomo. With these platforms, you can measure how cohorts perform using traffic, engagement and conversion metrics.

  

  There are two types of cohort analysis : acquisition-based cohort analysis and behavioural-based cohort analysis.

  Acquisition-based cohort analysis

  An acquisition-based cohort divides users by the date they purchased your product or service and tracks their behaviour afterward. 

  For example, one cohort could be all the users who signed up for your product in November. Another could be the users who signed up for your product in October. 

  You could then run a cohort analysis to see how the behaviour of the two cohorts differed. 

  Did the November cohort show higher engagement rates, increased frequency of visits post-acquisition or quicker conversions compared to the October cohort ? Analysing these cohorts can help with refining marketing strategies, optimising user experiences and improving retention and conversion rates.

  As you can see from the example, acquisition-based cohorts are a great way to track the initial acquisition and how user behaviour evolves post-acquisition.

  Behavioural-based cohort analysis

  A behavioural-based cohort divides users by their actions on your site. That could be their bounce rate, the number of actions they took on your site, their average time on site and more.

  

  Behavioural cohort analysis gives you a much deeper understanding of user behaviour and how they interact with your website.

  What can you achieve with a marketing cohort analysis ?

  A marketing cohort analysis is a valuable tool that can help marketers and product teams achieve the following goals :

  

  Understand which customers churn and why

  Acquisition and behavioural cohort analyses help marketing teams understand when and why customers leave. This is one of the most common goals of a marketing cohort analysis. 

  Learn which customers are most valuable

  Want to find out which channels create the most valuable customers or what actions customers take that increase their loyalty ? You can use a cohort analysis to do just that. 

  For example, you may find out you retain users who signed up via direct traffic better than those that signed up from an ad campaign. 

  Discover how to improve your product

  You can even use cohort analysis to identify opportunities to improve your website and track the impact of your changes. For example, you could see how visitor behaviour changes after a website refresh or whether visitors who take a certain action make more purchases. 

  Find out how to improve your marketing campaign

  A marketing cohort analysis makes it easy to find out which campaigns generate the best and most profitable customers. For example, you can run a cohort analysis to determine which channel (PPC ads, organic search, social media, etc.) generates customers with the lowest churn rate. 

  If a certain ad campaign generates the low-churn customers, you can allocate a budget accordingly. Alternatively, if customers from another ad campaign churn quickly, you can look into why that may be the case and optimise your campaigns to improve them. 

  Measure the impact of changes

  You can use a behavioural cohort analysis to understand what impact changes to your website or product have on active users. 

  If you introduced a pricing page to your website, for instance, you could analyse the behaviour of visitors who interacted with that page compared to those who didn’t, using behavioural cohort analysis to gauge the impact of these website changes on engagemen or conversions.

  The problem with cohort analysis in Google Analytics

  Google Analytics is often the first platform marketers turn to when they want to run a cohort analysis. While it’s a free solution, it’s not the most accurate or easy to use and users often encounter various issues. 

  For starters, Google Analytics can’t process user visitor data if they reject cookies. This can lead to an inaccurate view of traffic and compromise the reliability of your insights.

  In addition, GA is also known for sampling data, meaning it provides a subset rather than the complete dataset. Without the complete view of your website’s performance, you might make the wrong decisions, leading to less effective campaigns, missed opportunities and difficulties in reaching marketing goals.

  How to analyse cohorts with Matomo

  Luckily, there is an alternative to Google Analytics. 

  As the leading open-source web analytics solution, Matomo offers a robust option for cohort analysis. With its 100% accurate data, thanks to the absence of sampling, and its privacy-friendly tracking, users can rely on the data without resorting to guesswork. It is a premium feature included with our Matomo Cloud or available to purchase on the Matomo Marketplace for Matomo On-Premise users.

  Below, we’ll show how you can run a marketing cohort analysis using Matomo.

  Set a goal

  Setting a goal is the first step in running a cohort analysis with any platform. Define what you want to achieve from your analysis and choose the metrics you want to measure. 

  For example, you may want to improve your customer retention rate over the first 90 days. 

  Define cohorts

  Next, create cohorts by defining segmentation criteria. As we’ve discussed above, this could be acquisition-based or behavioural. 

  Matomo makes it easy to define cohorts and create charts. 

  In the sidebar menu, click Visitors > Cohorts. You’ll immediately see Matomo’s standard cohort report (something like the one below).

  

  In the example above, we’ve created cohorts by bounce rate. 

  You can view cohorts by weekly, monthly or yearly periods using the date selector and change the metric using the dropdown. Other metrics you can analyse cohorts by include :

  • Unique visitors
  • Return visitors
  • Conversion rates
  • Revenue
  • Actions per visit

  Change the data selection to create your desired cohort, and Matomo will automatically generate the report. 

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  Analyse your cohort chart

  Cohort charts can be intimidating initially, but they are pretty easy to understand and packed with insights. 

  Here’s an example of an acquisition-based cohort chart from Matomo looking at the percentage of returning visitors :

  

  Cohorts run vertically. The oldest cohort (visitors between February 13 – 19) is at the top of the chart, with the newest cohort (April 17 – 23) at the bottom. 

  The period of time runs horizontally — daily in this case. The cells show the corresponding value for the metric we’re plotting (the percentage of returning visitors). 

  For example, 98.69% of visitors who landed on your site between February 13 – 19, returned two weeks later. 

  Usually, running one cohort analysis isn’t enough to identify a problem or find a solution. That’s why comparing several cohort analyses or digging deeper using segmentation is important.

  Segment your cohort chart

  Matomo lets you dig deeper by segmenting each cohort to examine their behaviour’s specifics. You can do this from the cohort report by clicking the segmented visitor log icon in the relevant row.

  

  

  Segmenting cohorts lets you understand why users behave the way they do. For example, suppose you find that users you purchased on Black Friday don’t return to your site often. In that case, you may want to rethink your offers for next year to target an audience with potentially better customer lifetime value. 

  Start using Matomo for marketing cohort analysis

  A marketing cohort analysis can teach you a lot about your customers and the health of your business. But you need the right tools to succeed. 

  Matomo provides an effective and privacy-first way to run your analysis. You can create custom customer segments based on almost anything, from demographics and geography to referral sources and user behaviour. 

  Our custom cohort analysis reports and colour-coded visualisations make it easy to analyse cohorts and spot patterns. Best of all, the data is 100% accurate. Unlike other web analytics solution or cohort analysis tools, we don’t sample data. 

  Find out how you can use Matomo to run marketing cohort analysis by trialling us free for 21 days. No credit card required.

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