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Personnaliser en ajoutant son logo, sa bannière ou son image de fond
5 septembre 2013, par kent1Certains thèmes prennent en compte trois éléments de personnalisation : l’ajout d’un logo ; l’ajout d’une bannière l’ajout d’une image de fond ;
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Publier sur MédiaSpip
13 juin 2013Puis-je poster des contenus à partir d’une tablette Ipad ?
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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.
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21 day free trial. No credit card required.
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Linear Attribution Model : What Is It and How Does It Work ?
16 février 2024, par ErinWant a more in-depth way to understand the effectiveness of your marketing campaigns ? Then, the linear attribution model could be the answer.
Although you can choose from several different attribution models, a linear model is ideal for giving value to every touchpoint along the customer journey. It can help you identify your most effective marketing channels and optimise your campaigns.
So, without further ado, let’s explore what a linear attribution model is, when you should use it and how you can get started.
What is a linear attribution model ?
A linear attribution model is a multi-touch method of marketing attribution where equal credit is given to each touchpoint. Every marketing channel used across the entire customer journey gets credit, and each is considered equally important.
So, if a potential customer has four interactions before converting, each channel gets 25% of the credit.
Let’s look at how linear attribution works in practice using a hypothetical example of a marketing manager, Sally, who is looking for an alternative to Google Analytics.
Sally starts her conversion path by reading a Matomo article comparing Matomo to Google Analytics she finds when searching on Google. A few days later she signs up for a webinar she saw on Matomo’s LinkedIn page. Two weeks later, Sally gets a sign-off from her boss and decides to go ahead with Matomo. She visits the website and starts a free trial by clicking on one of the paid Google Ads.
Using a linear attribution model, we credit each of the channels Sally uses (organic traffic, organic social, and paid ads), ensuring no channel is overlooked in our marketing analysis.
Are there other types of attribution models ?
Absolutely. There are several common types of attribution models marketing managers can use to measure the impact of channels in different ways.
- First interaction : Also called a first-touch attribution model, this method gives all the credit to the first channel in the customer journey. This model is great for optimising the top of your sales funnel.
- Last interaction : Also called a last-touch attribution model, this approach gives all the credit to the last channel the customer interacts with. It’s a great model for optimising the bottom of your marketing funnel.
- Last non-direct interaction : This attribution model excludes direct traffic and credits the previous touchpoint. This is a fantastic alternative to a last-touch attribution model, especially if most customers visit your website before converting.
- Time decay attribution model : This model adjusts credit according to the order of the touchpoints. Those nearest the conversion get weighted the highest.
- Position-based attribution model : This model allocates 40% of the credit to the first and last touchpoints and splits the remaining 20% evenly between every other interaction.
Why use a linear attribution model ?
Marketing attribution is vital if you want to understand which parts of your marketing strategy are working. All of the attribution models described above can help you achieve this to some degree, but there are several reasons to choose a linear attribution model in particular.
It uses multi-touch attribution
Unlike single-touch attribution models like first and last interaction, linear attribution is a multi-touch attribution model that considers every touchpoint. This is vital to get a complete picture of the modern customer journey, where customers interact with companies between 20 and 500 times.
Single-touch attribution models can be misleading by giving conversion credit to a single channel, especially if it was the customer’s last use. In our example above, Sally’s last interaction with our brand was through a paid ad, but it was hardly the most important.
It’s easy to understand
Attribution models can be complicated, but linear attribution is easy to understand. Every touchpoint gets the same credit, allowing you to see how your entire marketing function works. This simplicity also makes it easy for marketers to take action.
It’s great for identifying effective marketing channels
Because linear attribution is one of the few models that provides a complete view of the customer journey, it’s easy to identify your most common and influential touchpoints.
It accounts for the top and bottom of your funnel, so you can also categorise your marketing channels more effectively and make more informed decisions. For example, PPC ads may be a more common bottom-of-the-full touchpoint and should, therefore, not be used to target broad, top-of-funnel search terms.
Are there any reasons not to use linear attribution ?
Linear attribution isn’t perfect. Like all attribution models, it has its weaknesses. Specifically, linear attribution can be too simple, dilute conversion credit and unsuitable for long sales cycles.
It can be too simple
Linear attribution lacks nuance. It only considers touchpoints while ignoring other factors like brand image and your competitors. This is true for most attribution models, but it’s still important to point it out.
It can dilute conversion credit
In reality, not every touchpoint impacts conversions to the same extent. In the example above, the social media post promoting the webinar may have been the most effective touchpoint, but we have no way of measuring this.
The risk with using a linear model is that credit can be underestimated and overestimated — especially if you have a long sales cycle.
It’s unsuitable for very long sales cycles
Speaking of long sales cycles, linear attribution models won’t add much value if your customer journey contains dozens of different touchpoints. Credit will get diluted to the point where analysis becomes impossible, and the model will also struggle to measure the precise ways certain touchpoints impact conversions.
Should you use a linear attribution model ?
A linear attribution model is a great choice for any company with shorter sales cycles or a reasonably straightforward customer journey that uses multiple marketing channels. In these cases, it helps you understand the contribution of each touchpoint and find your best channels.
It’s also a practical choice for small businesses and startups that don’t have a team of data scientists on staff or the budget to hire outside help. Because it’s so easy to set up and understand, anyone can start generating insights using this model.
How to set up a linear attribution model
Are you sold on the idea of using a linear attribution model ? Then follow the steps below to get started :
Choose a marketing attribution tool
Given the market is worth $3.1 billion, you won’t be surprised to learn there are plenty of tools to choose from. But choose carefully. The tool you pick can significantly impact your success with attribution modelling.
Take Google Analytics, for instance. While GA4 offers several marketing attribution models for free, including linear attribution, it lacks accuracy due to cookie consent rejection and data sampling.
Accurate marketing attribution is included as a feature in Matomo Cloud and is available as a plugin for Matomo On-Premise users. We support a full range of attribution models that use 100% accurate data because we don’t use data sampling, and cookie consent isn’t an issue (with the exception of Germany and the UK). That means you can trust our insights.
Matomo’s marketing attribution is available out of the box, and we also provide access to raw data, allowing you to develop your custom attribution model.
Collect data
The quality of your marketing attribution also depends on the quality and quantity of your data. It’s why you need to avoid a platform that uses data sampling.
This should include :
- General data from your analytics platform, like pages visited and forms filled
- Goals and conversions, which we’ll discuss in more detail in the next step
- Campaign tracking data so you can monitor the behaviour of traffic from different referral channels
- Behavioural data from features like Heatmaps or Session Recordings
Set up goals and conversions
You can’t assign conversion values to customer journey touchpoints if you don’t have conversion goals in place. That’s why the next step of the process is to set up conversion tracking in your web analytics platform.
Depending on your type of business and the product you sell, conversions could take one of the following forms :
- A product purchase
- Signing up for a webinar
- Downloading an ebook
- Filling in a form
- Starting a free trial
Setting up these kinds of goals is easy if you use Matomo.
Just head to the Goals section of the dashboard, click Manage Goals and then click the green Add A New Goal button.
Fill in the screen below, and add a Goal Revenue at the bottom of the page. Doing so will mean Matomo can automatically calculate the value of each touchpoint when using your attribution model.
If your analytics platform allows it, make sure you also set up Event Tracking, which will allow you to analyse how many users start to take a desired action (like filling in a form) but never complete the task.
Try Matomo for Free
Get the web insights you need, without compromising data accuracy.
Test and validate
As we’ve explained, linear attribution is a great model in some scenarios, but it can fall short if you have a long or complex sales funnel. Even if you’re sure it’s the right model for your company, testing and validating is important.
Ideally, your chosen attribution tool should make this process pretty straightforward. For example, Matomo’s Marketing Attribution feature makes comparing and contrasting three different attribution models easy.
Here we compare the performance of three attribution models—linear, first-touch, and last-non-direct—in Matomo’s Marketing Attribution dashboard, providing straightforward analysis.
If you think linear attribution accurately reflects the value of your channels, you can start to analyse the insights it generates. If not, then consider using another attribution model.
Don’t forget to take action from your marketing efforts, either. Linear attribution helps you spot the channels that contribute most to conversions, so allocate more resources to those channels and see if you can improve your conversion rate or boost your ROI.
Make the most of marketing attribution with Matomo
A linear attribution model lets you measure every touchpoint in your customer journey. It’s an easy attribution model to start with and lets you identify and optimise your most effective marketing channels.
However, accurate data is essential if you want to benefit the most from marketing attribution data. If your web analytics solution doesn’t play nicely with cookies or uses sampled data, then your linear model isn’t going to tell you the whole story.
That’s why over 1 million sites trust Matomo’s privacy-focused web analytics, ensuring accurate data for a comprehensive understanding of customer journeys.
Now you know what linear attribution modelling is, start employing the model today by signing up for a free 21-day trial, no credit card required.
Try Matomo for Free
21 day free trial. No credit card required.
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Google Analytics 4 (GA4) vs Universal Analytics (UA)
24 janvier 2022, par Erin — Analytics TipsMarch 2022 Update : It’s official ! Google announced that Universal Analytics will no longer process any new data as of 1 July 2023. Google is now pushing Universal Analytics users to switch to the latest version of GA – Google Analytics 4.
Currently, Google Analytics 4 is unable to accept historical data from Universal Analytics. Users need to take action before July 2022, to ensure they have 12 months of data built up before the sunset of Universal Analytics
So how do Universal Analytics and Google Analytics 4 compare ? And what alternative options do you have ? Let’s dive in.
In this blog, we’ll cover :
What is Google Analytics 4 ?
In October 2020, Google launched Google Analytics 4, a completely redesigned analytics platform. This follows on from the previous version known as Universal Analytics (or UA).
Amongst its touted benefits, GA4 promises a completely new way to model data and even the ability to predict future revenue.
However, the reception of GA4 has been largely negative. In fact, some users from the digital marketing community have said that GA4 is awful, unusable and so bad it can bring you to tears.
Gill Andrews via Twitter Google Analytics 4 vs Universal Analytics
There are some pretty big differences between Google Analytics 4 and Universal Analytics but for this blog, we’ll cover the top three.
1. Redesigned user interface (UI)
GA4 features a completely redesigned UI to Universal Analytics’ popular interface. This dramatic change has left many users in confusion and fuelled some users to declare that “most of the time you are going round in circles to find what you’re looking for.”
Mike Huggard via Twitter 2. Event-based tracking
Google Analytics 4 also brings with it a new data model which is purely event-based. This event-based model moves away from the typical “pageview” metric that underpins Universal Analytics.
3. Machine learning insights
Google Analytics 4 promises to “predict the future behavior of your users” with their machine-learning-powered predictive metrics. This feature can “use shared aggregated and anonymous data to improve model quality”. Sounds powerful, right ?
Unfortunately, it only works if at least 1,000 returning users triggered the relevant predictive condition over a seven-day period. Also, if the model isn’t sustained over a “period of time” then it won’t work. And according to Google, if “the model quality for your property falls below the minimum threshold, then Analytics will stop updating the corresponding predictions”.
This means GA4’s machine learning insights probably won’t work for the majority of analytics users.
Ultimately, GA4 is just not ready to replace Google’s Universal Analytics for most users. There are too many missing features.
What’s missing in Google Analytics 4 ?
Quite a lot. Even though it offers a completely new approach to analytics, there are a lot of key features and functions missing in GA4.
Behavior Flow
The Behavior Flow report in Universal Analytics helps to visualise the path users take from one page or Event to the next. It’s extremely useful when you’re looking for quick and clear insight. But it no longer exists in Google Analytics 4, and instead, two new overcomplicated reports have been introduced to replace it – funnel exploration report and path exploration report.
The decision to remove this critical report will leave many users feeling disappointed and frustrated.
Limitations on custom dimensions
You can create custom dimensions in Google Analytics 4 to capture advanced information. For example, if a user reads a blog post you can supplement that data with custom dimensions like author name or blog post length. But, you can only use up to 50, and for some that will make functionality like this almost pointless.
Machine learning (ML) limitations
Google Analytics 4 promises powerful ML insights to predict the likelihood of users converting based on their behaviors. The problem ? You need 1,000 returning users in one week. For most small-medium businesses this just isn’t possible.
And if you do get this level of traffic in a week, there’s another hurdle. According to Google, if “the model quality for your property falls below the minimum threshold, then GA will stop updating the corresponding predictions.” To add insult to injury Google suggests that this might make all ML insights unavailable. But they can’t say for certain…
Views
One cornerstone of Universal Analytics is the ability to configure views. Views allow you to set certain analytics environments for testing or cleaning up data by filtering out internal traffic, for example.
Views are great for quickly and easily filtering data. Preset views that contain just the information you want to see are the ideal analytics setup for smaller businesses, casual users, and do-it-yourself marketing departments.
Via Reddit There are a few workarounds but they’re “messy [,] annoying and clunky,” says a disenfranchised Redditor.
Another helpful Reddit user stumbled upon an unhelpful statement from Google. Google says that they “do not offer [the views] feature in Google Analytics 4 but are planning similar functionality in the future.” There’s no specific date yet though.
Bounce rate
Those that rely on bounce rate to understand their site’s performance will be disappointed to find out that bounce rate is also not available in GA4. Instead, Google is pushing a new metric known as “Engagement Rate”. With this metric, Google now uses their own formula to establish if a visitor is engaged with a site.
Lack of integration
Currently, GA4 isn’t ready to integrate with many core digital marketing tools and doesn’t accept non-Google data imports. This makes it difficult for users to analyse ROI and ROAS for campaigns measured in other tools.
Content Grouping
Yet another key feature that Google has done away with is Content Grouping. However, as with some of the other missing features in GA4, there is a workaround, but it’s not simple for casual users to implement. In order to keep using Content Grouping, you’ll need to create event-scoped custom dimensions.
Annotations
A key feature of Universal Analytics is the ability to add custom Annotations in views. Annotations are useful for marking dates that site changes were made for analysis in the future. However, Google has removed the Annotations feature and offered no alternative or workaround.
Historical data imports are not available
The new approach to data modelling in GA4 adds new functionality that UA can’t match. However, it also means that you can’t import historical UA data into GA4.
Google’s suggestion for this one ? Keep running UA with GA4 and duplicate events for your GA4 property. Now you will have two different implementations running alongside each other and doing slightly different things. Which doesn’t sound like a particularly streamlined solution, and adds another level of complexity.
Should you switch to Google Analytics 4 ?
So the burning question is, should you switch from Universal Analytics to Google Analytics 4 ? It really depends on whether you have the available resources and if you believe this tool is still right for your organisation. At the time of writing, GA4 is not ready for day-to-day use in most organisations.
If you’re a casual user or someone looking for quick, clear insights then you will likely struggle with the switch to GA4. It appears that the new Google Analytics 4 has been designed for enterprise-scale businesses with large internal teams of analysts.
Micah Fisher-Kirshner via Twitter Unfortunately, for most casual users, business owners and do-it-yourself marketers there are complex workarounds and time-consuming implementations to handle. Ultimately, it’s up to you to decide if the effort to migrate and relearn GA is worth it.
Right now is the best time to draw the line and make a decision to either switch to GA4 or look for a better alternative to Google Analytics.
Google Analytics alternative
Matomo is one of the best Google Analytics alternatives offering an easy to use design with enhanced insights on our Cloud, On-Premise and on Matomo for WordPress solutions.
Mark Samber via Twitter Matomo is an open-source analytics solution that provides a comprehensive, user-friendly and compliance-focused alternative to both Google Analytics 4 and Universal Analytics.
The key benefits of using Matomo include :
- Easy to use – Matomo provides a simpler interface and understandable KPIs. See for yourself with our live demo.
- Compliance – Future-proof your tech stack for looming privacy regulations. Matomo covers all of your ePrivacy, GDPR, HIPAA, CCPA, and PECR data compliance requirements.
- Data privacy and ownership – Your analytics data is 100% yours to own, with no external parties looking in.
- Flexible, all-in-one solution – Get features like A/B Testing, Heatmaps, Session Recordings, SEO Web Vitals, Tag Manager, Media Analytics, Search Engine Keyword Performance, custom reports and much more.
- Integrations galore – Expand your Matomo capabilities by adding integrations from over 100 leading technologies.
Plus, unlike GA4, Matomo will accept your historical data from UA so you don’t have to start all over again. Check out our 7 step guide to migrating from Google Analytics to find out how.
Getting started with Matomo is easy. Check out our live demo and start your free 21-day trial. No credit card required.
In addition to the limitations and complexities of GA4, there are many other significant drawbacks to using Google Analytics.
Google’s data ethics are a growing concern of many and it is often discussed in the mainstream media. In addition, GA is not GDPR compliant by default and has resulted in 200k+ data protection cases against websites using GA.
What’s more, the data that Google Analytics actually provides its end-users is extrapolated from samples. GA’s data sampling model means that once you’ve collected a certain amount of data Google Analytics will make educated guesses rather than use up its server space collecting your actual data.
The reasons to switch from Google Analytics are rising each day.
Wrap up
The now required update to GA4 will add new layers of complexity, which will leave many casual web analytics users and marketers wondering if there’s a better way. Luckily there is. Get clear insights quickly and easily with Matomo – start your 21-day free trial now.