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Submit bugs and patches
13 avril 2011Unfortunately a software is never perfect.
If you think you have found a bug, report it using our ticket system. Please to help us to fix it by providing the following information : the browser you are using, including the exact version as precise an explanation as possible of the problem if possible, the steps taken resulting in the problem a link to the site / page in question
If you think you have solved the bug, fill in a ticket and attach to it a corrective patch.
You may also (...) -
List of compatible distributions
26 avril 2011, parThe table below is the list of Linux distributions compatible with the automated installation script of MediaSPIP. Distribution nameVersion nameVersion number Debian Squeeze 6.x.x Debian Weezy 7.x.x Debian Jessie 8.x.x Ubuntu The Precise Pangolin 12.04 LTS Ubuntu The Trusty Tahr 14.04
If you want to help us improve this list, you can provide us access to a machine whose distribution is not mentioned above or send the necessary fixes to add (...) -
MediaSPIP v0.2
21 juin 2013, parMediaSPIP 0.2 is the first MediaSPIP stable release.
Its official release date is June 21, 2013 and is announced here.
The zip file provided here only contains the sources of MediaSPIP in its standalone version.
To get a working installation, you must manually install all-software dependencies on the server.
If you want to use this archive for an installation in "farm mode", you will also need to proceed to other manual (...)
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What is a Cohort Report ? A Beginner’s Guide to Cohort Analysis
3 janvier 2024, par ErinHandling your user data as a single mass of numbers is rarely conducive to figuring out meaningful patterns you can use to improve your marketing campaigns.
A cohort report (or cohort analysis) can help you quickly break down that larger audience into sequential segments and contrast and compare based on various metrics. As such, it is a great tool for unlocking more granular trends and insights — for example, identifying patterns in engagement and conversions based on the date users first interacted with your site.
In this guide, we explain the basics of the cohort report and the best way to set one up to get the most out of it.
What is a cohort report ?
In a cohort report, you divide a data set into groups based on certain criteria — typically a time-based cohort metric like first purchase date — and then analyse the data across those segments, looking for patterns.
Date-based cohort analysis is the most common approach, often creating cohorts based on the day a user completed a particular action — signed up, purchased something or visited your website. Depending on the metric you choose to measure (like return visits), the cohort report might look something like this :
Note that this is not a universal benchmark or anything of the sort. The above is a theoretical cohort analysis based on app users who downloaded the app, tracking and comparing the retention rates as the days go by.
The benchmarks will be drastically different depending on the metric you’re measuring and the basis for your cohorts. For example, if you’re measuring returning visitor rates among first-time visitors to your website, expect single-digit percentages even on the second day.
Your industry will also greatly affect what you consider positive in a cohort report. For example, if you’re a subscription SaaS, you’d expect high continued usage rates over the first week. If you sell office supplies to companies, much less so.
What is an example of a cohort ?
As we just mentioned, a typical cohort analysis separates users or customers by the date they first interacted with your business — in this case, they downloaded your app. Within that larger analysis, the users who downloaded it on May 3 represent a single cohort.
In this case, we’ve chosen behaviour and time — the app download day — to separate the user base into cohorts. That means every specific day denotes a specific cohort within the analysis.
Diving deeper into an individual cohort may be a good idea for important holidays or promotional events like Black Friday.
Of course, cohorts don’t have to be based on specific behaviour within certain periods. You can also create cohorts based on other dimensions :
- Transactional data — revenue per user
- Churn data — date of churn
- Behavioural cohort — based on actions taken on your website, app or e-commerce store, like the number of sessions per user or specific product pages visited
- Acquisition cohort — which channel referred the user or customer
For more information on different cohort types, read our in-depth guide on cohort analysis.
How to create a cohort report (and make sense of it)
Matomo makes it easy to view and analyse different cohorts (without the privacy and legal implications of using Google Analytics).
Here are a few different ways to set up a cohort report in Matomo, starting with our built-in cohorts report.
Cohort reports
With Matomo, cohort reports are automatically compiled based on the first visit date. The default metric is the percentage of returning visitors.
Changing the settings allows you to create multiple variations of cohort analysis reports.
Break down cohorts by different metrics
The percentage of returning visits can be valuable if you’re trying to improve early engagement in a SaaS app onboarding process. But it’s far from your only option.
You can also compare performance by conversion, revenue, bounce rate, actions per visit, average session duration or other metrics.
Change the time and scope of your cohort analysis
Splitting up cohorts by single days may be useless if you don’t have a high volume of users or visitors. If the average cohort size is only a few users, you won’t be able to identify reliable patterns.
Matomo lets you set any time period to create your cohort analysis report. Instead of the most recent days, you can create cohorts by week, month, year or custom date ranges.
Cohort sizes will depend on your customer base. Make sure each cohort is large enough to encapsulate all the customers in that cohort and not so small that you have insignificant cohorts of only a few customers. Choose a date range that gives you that without scaling it too far so you can’t identify any seasonal trends.
Cohort analysis can be a great tool if you’ve recently changed your marketing, product offering or onboarding. Set the data range to weekly and look for any impact in conversions and revenue after the changes.
Using the “compare to” feature, you can also do month-over-month, quarter-over-quarter or any custom date range comparisons. This approach can help you get a rough overview of your campaign’s long-term progress without doing any in-depth analysis.
You can also use the same approach to compare different holiday seasons against each other.
If you want to combine time cohorts with segmentation, you can run cohort reports for different subsets of visitors instead of all visitors. This can lead to actionable insights like adjusting weekend or specific seasonal promotions to improve conversion rates.
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Easily create custom cohort reports beyond the time dimension
If you want to split your audience into cohorts by focusing on something other than time, you will need to create a custom report and choose another dimension. In Matomo, you can choose from a wide range of cohort metrics, including referrers, e-commerce signals like viewed product or product category, form submissions and more.
Then, you can create a simple table-based report with all the insights you need by choosing the metrics you want to see. For example, you could choose average visit duration, bounce rate and other usage metrics.
If you want more revenue-focused insights, add metrics like conversions, add-to-cart and other e-commerce events.
Custom reports make it easy to create cohort reports for almost any dimension. You can use any metric within demographic and behavioural analytics to create a cohort. (You can explore the complete list of our possible segmentation metrics.)
We cover different types of custom reports (and ideas for specific marketing campaigns) in our guide on custom segmentation.
Create your first cohort report and gain better insights into your visitors
Cohort reports can help you identify trends and the impact of short-term marketing efforts like events and promotions.
With Matomo cohort reports you have the power to create complex custom reports for various cohorts and segments.
If you’re looking for a powerful, easy-to-use web analytics solution that gives you 100% accurate data without compromising your users’ privacy, Matomo is a great fit. Get started with a 21-day free trial today. No credit card required.
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21 day free trial. No credit card required.
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A Complete Guide to Metrics in Google Analytics
11 janvier 2024, par ErinThere’s no denying that Google Analytics is the most popular web analytics solution today. Many marketers choose it to understand user behaviour. But when it offers so many different types of metrics, it can be overwhelming to choose which ones to focus on. In this article, we’ll dive into how metrics work in Google Analytics 4 and how to decide which metrics may be most useful to you, depending on your analytics needs.
However, there are alternative web analytics solutions that can provide more accurate data and supplement GA’s existing features. Keep reading to learn how to overcome Google Analytics limitations so you can get the more out of your web analytics.
What is a metric in Google Analytics ?
In Google Analytics, a metric is a quantitative measurement or numerical data that provides insights into specific aspects of user behaviour. Metrics represent the counts or sums of user interactions, events or other data points. You can use GA metrics to better understand how people engage with a website or mobile app.
Unlike the previous Universal Analytics (the previous version of GA), GA4 is event-centric and has automated and simplified the event tracking process. Compared to Universal Analytics, GA4 is more user-centric and lets you hone in on individual user journeys. Some examples of common key metrics in GA4 are :
- Sessions : A group of user interactions on your website that occur within a specific time period. A session concludes when there is no user activity for 30 minutes.
- Total Users : The cumulative count of individuals who accessed your site within a specified date range.
- Engagement Rate : The percentage of visits to your website or app that included engagement (e.g., one more pageview, one or more conversion, etc.), determined by dividing engaged sessions by sessions.
Metrics are invaluable when it comes to website and conversion optimisation. Whether you’re on the marketing team, creating content or designing web pages, understanding how your users interact with your digital platforms is essential.
GA4 metrics vs. dimensions
GA4 uses metrics to discuss quantitative measurements and dimensions as qualitative descriptors that provide additional context to metrics. To make things crystal clear, here are some examples of how metrics and dimensions are used together :
- “Session duration” = metric, “device type” = dimension
- In this situation, the dimension can segment the data by device type so you can optimise the user experience for different devices.
- “Bounce rate” = metric, “traffic source/medium” = dimension
- Here, the dimension helps you segment by traffic source to understand how different acquisition channels are performing.
- “Conversion rate” = metric, “Landing page” = dimension
- When the conversion rate data is segmented by landing page, you can better see the most effective landing pages.
You can get into the nitty gritty of granular analysis by combining metrics and dimensions to better understand specific user interactions.
How do Google Analytics metrics work ?
Before diving into the most important metrics you should track, let’s review how metrics in GA4 work.
- Tracking code implementation
The process begins with implementing Google Analytics 4 tracking code into the HTML of web pages. This tracking code is JavaScript added to each website page — it collects data related to user interactions, events and other important tidbits.
- Data collection
As users interact with the website or app, the Google Analytics 4 tracking code captures various data points (i.e., page views, clicks, form submissions, custom events, etc.). This raw data is compiled and sent to Google Analytics servers for processing.
- Data processing algorithms
When the data reaches Google Analytics servers, data processing algorithms come into play. These algorithms analyse the incoming raw data to identify the dataset’s trends, relationships and patterns. This part of the process involves cleaning and organising the data.
- Segmentation and customisation
As discussed in the previous section, Google Analytics 4 allows for segmentation and customisation of data with dimensions. To analyse specific data groups, you can define segments based on various dimensions (e.g., traffic source, device type). Custom events and user properties can also be defined to tailor the tracking to the unique needs of your website or app.
- Report generation
Google Analytics 4 can make comprehensive reports and dashboards based on the processed and segmented data. These reports, often in the form of graphs and charts, help identify patterns and trends in the data.
What are the most important Google Analytics metrics to track ?
In this section, we’ll identify and define key metrics for marketing teams to track in Google Analytics 4.
- Pageviews are the total number of times a specific page or screen on your website or app is viewed by visitors. Pageviews are calculated each time a web page is loaded or reloaded in a browser. You can use this metric to measure the popularity of certain content on your website and what users are interested in.
- Event tracking monitors user interactions with content on a website or app (i.e., clicks, downloads, video views, etc.). Event tracking provides detailed insights into user engagement so you can better understand how users interact with dynamic content.
- Retention rate can be analysed with a pre-made overview report that Google Analytics 4 provides. This user metric measures the percentage of visitors who return to your website or app after their first visit within a specific time period. Retention rate = (users with subsequent visits / total users in the initial cohort) x 100. Use this information to understand how relevant or effective your content, user experience and marketing efforts are in retaining visitors. You probably have more loyal/returning buyers if you have a high retention rate.
- Average session duration calculates the average time users spend on your website or app per session. Average session duration = total duration of all sessions / # of sessions. A high average session duration indicates how interested and engaged users are with your content.
- Site searches and search queries on your website are automatically tracked by Google Analytics 4. These metrics include search terms, number of searches and user engagement post-search. You can use site search metrics to better understand user intent and refine content based on users’ searches.
- Entrance and exit pages show where users first enter and leave your site. This metric is calculated by the percentage of sessions that start or end on a specific page. Knowing where users are entering and leaving your site can help identify places for content optimisation.
- Device and browser info includes data about which devices and browsers websites or apps visitors use. This is another metric that Google Analytics 4 automatically collects and categorises during user sessions. You can use this data to improve the user experience on relevant devices and browsers.
- Bounce rate is the percentage of single-page sessions where users leave your site or app without interacting further. Bounce rate = (# of single-page sessions / total # of sessions) x 100. Bounce rate is useful for determining how effective your landing pages are — pages with high bounce rates can be tweaked and optimised to enhance user engagement.
Examples of how Matomo can elevate your web analytics
Although Google Analytics is a powerful tool for understanding user behaviour, it also has privacy concerns, limitations and a list of issues. Another web analytics solution like Matomo can help fill those gaps so you can get the most out of your analytics.
- Cross-verify and validate your observations from Google Analytics by comparing data from Matomo’s Heatmaps and Session Recordings for the same pages. This process grants you access to these advanced features that GA4 does not offer.
- Matomo provides you with greater accuracy thanks to its privacy-friendly design. Unlike GA4, Matomo can be configured to operate without cookies. This means increased accuracy without intrusive cookie consent screens interrupting the user experience. It’s a win for you and for your users. Matomo also doesn’t apply data sampling so you can rest assured that the data you see is 100% accurate.
- Unlike GA4, Matomo offers direct access to customer support so you can save time sifting through community forum threads and online documentation. Gain personalised assistance and guidance for your analytics questions, and resolve issues efficiently.
- Matomo’s Form Analytics and Media Analytics extend your analytics capabilities beyond just pageviews and event tracking.
Tracking user interactions with forms can tell you which fields users struggle with, common drop-off points, in addition to which parts of the form successfully guide visitors towards submission.
See first-hand how Concrete CMS 3x their leads using Matomo’s Form Analytics.
Media Analytics can provide insight into how users interact with image, video, or audio content on your website. You can use this feature to assess the relevance and popularity of specific content by knowing what your audience is engaged by.
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Get the web insights you need, without compromising data accuracy.
Final thoughts
Although Google Analytics is a powerful tool on its own, Matomo can elevate your web analytics by offering advanced features, data accuracy and a privacy-friendly design. Don’t play a guessing game with your data — Matomo provides 100% accurate data so you don’t have to rely on AI or machine learning to fill in the gaps. Matomo can be configured cookieless which also provides you with more accurate data and a better user experience.
Lastly, Matomo is fully compliant with some of the world’s strictest privacy regulations like GPDR. You won’t have to sacrifice compliance for accurate, high quality data.
Start your 21-day free trial of Matomo — no credit card required.
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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 ErinThe 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.
Try Matomo for Free
21 day free trial. No credit card required.
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