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• What is a Cohort Report ? A Beginner’s Guide to Cohort Analysis

  3 janvier 2024, par Erin

  Handling 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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• Incrementality Testing : Quick-Start Guide (With Calculations)

  26 mars 2024, par Erin

  How do you know when a campaign is successful ? When you earn more revenue than last month ?

  Maybe.

  But how do you know how much of an impact a certain campaign or channel had on your sales ?

  With marketing attribution, you can determine credit for each sale.

  But if you want a deeper look, you need to understand the incremental impact of each channel and campaign.

  The way you do this ?

  Incrementality testing.

  In this guide, we break down what incrementality is, why it’s important and how to test it so you can double down on the activities driving the most growth.

  What is incrementality ?

  So, what exactly is incrementality ?

  Let’s say you just ran a marketing campaign for a new product. The launch was a success. Breakthrough numbers in your revenue. You used a variety of channels and activities to bring it all together.

  So, you launch a plan for next month’s campaign. But you don’t truly know what moved the needle.

  Did you just hit new highs because your audience is bigger ? And your brand is greater ?

  Or did the recent moves you made make a direct difference ?

  This is incrementality.

  

  Incrementality is growth directly attributed to marketing efforts beyond the overall impact of your brand. By measuring and conducting incrementality testing, you can clearly see how much of a difference each activity or channel truly impacted business growth. 

  What is incrementality testing ?

  Incrementality testing allows marketers to gauge the effectiveness of a marketing tactic or strategy. It tells you if a particular marketing activity had a positive, negative or neutral impact on your business. 

  It also tells you the overall impact it can have on your key performance indicators (KPIs). 

  The result ?

  You can pinpoint the highest-performing moves and incorporate them into your marketing workflows. You also discard marketing strategies with negligible, neutral or even negative impacts. 

  For example, let’s say you think a B2B LinkedIn ads campaign will help you reach your product launch goals. An incrementality test can tell you if the introduction of this campaign will help you get to the desired outcome.

  How incrementality testing works

  Before diving into your testing phase, you must clearly identify your KPIs.

  Here are the top KPIs you should be tracking on your website :

  • Ad impressions
  • Website visits
  • Leads
  • Sales

  The exact KPIs will depend on your marketing goals. You’re ready to move forward once you know your key performance indicators.

  Here’s how incrementality testing works step-by-step :

  1. Define a test and control group

  The first step is to define a test group and control group. 

  • A test group is a segment of your target audience that’s exposed to the marketing campaign. 
  • A control group is a segment that isn’t. 

  Keep in mind that both groups have similar demographics and other relevant characteristics. 

  2. Execute your campaign

  The second step is to run the marketing campaign on the test group. This can be a Facebook ad, LinkedIn ad or email marketing campaign.

  It all depends on your goals and your primary channels.

  3. Measure outcomes

  The third step is to measure the campaign’s impact based on your KPIs. 

  Let’s say a brand wants to see if a certain marketing move increases its leads. The test can tell them the number of email sign-ups with and without the campaign. 

  4. Compare results

  Next, compare the test group results with the control group. The difference in outcomes tells you the impact of that campaign. You can then use this difference to inform your future marketing strategies. 

  With Matomo, you can easily track results from campaigns — like conversions. 

  Our platform lets you quickly see what channels are getting the best results so you can gain insights into incrementality and optimise your strategy.

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  Why it’s important to conduct incrementality tests

  The digital marketing industry is constantly changing. Marketers need to stay on their toes to keep up. Incrementality tests help you stay on track.

  For example, let’s say you’re selling laptops. You can increase your warranty period to three years to see the impact on sales. An incrementality test will tell you if this move will boost your sales (and by how much).

  Now, let’s dive into the reasons why you need to consistently conduct incrementality tests :

  Determine the right tactics for success

  Identifying the best action to grow your business is a challenge every marketer faces.

  The best way to identify marketing tactics is by conducting incrementality testing. These tactics are bound to work since data back them. As a result, you can optimise your marketing budget and maximise your ROIs. 

  It lets you run multiple tests to identify the most impactful strategy between :

  • An email marketing strategy
  • A social media strategy 
  • A PPC ad

  For instance, an incrementality test might suggest email marketing will be more cost-effective than an ad campaign. What you can do is :

  • Expose the test group to the email marketing campaign and then compare the results with the control group
  • Expose the test group to the ad campaign and then compare its results with the control group

  Then, you can calculate the difference in results between the two marketing campaigns. This lets you focus on the strategy with a better ROI or ROAS potential. 

  Accurate data

  Marketing data is powerful. But getting accurate data can be challenging. With incrementality testing, you get to know the true impact of a marketing campaign. 

  Plus, with this testing strategy, you don’t have to waste your marketing budget. 

  With Matomo, you get 100% accurate data on all website activities. 

  Unlike Google Analytics, Matomo doesn’t rely on inaccurate data sampling — limiting the amount of data analysed.

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

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  Get the most out of your marketing investment

  Every business owner wants to maximise their return on investment. The ROI you get mainly depends on the marketing strategy. 

  For instance, email marketing offers an ROI of about 40:1 with some sources even reporting as high as 72:1.

  Incrementality testing helps you make informed investment decisions. With it, you can pinpoint the tactics that are most likely to bring the highest return. You can then focus your resources on them. It also helps you stay away from low-performing strategies. 

  Increase revenue

  It’s safe to say that the goal behind every marketing effort is a revenue boost. The higher your revenue, the more profits you generate. However, for many marketers, it’s an uphill battle. 

  With incrementality testing, you can boost your revenue by focusing your efforts in the right direction. 

  Get more traffic

  Incrementality testing tells you if a particular strategy can help you drive more traffic. You can use it to get more high-quality leads to your website or landing pages and double down on high-traffic strategies to increase those leads.

  How to test incrementality

  

  Developing an implementation plan is crucial to generate accurate insights from an incrementality test. Incrementality testing is like running a science experience. You need to go through several stages. Each stage is important for generating accurate results. 

  Here’s how you test incrementality :

  Define your goals

  Get clarity on what you want to achieve with this campaign. Which KPIs do you want to test ? Is it the return on your overall investment (ROI), return on ad spend (ROAS) or something else ?

  Segment your audience

  Selecting the right audience segment is crucial to getting accurate insights with an incrementality test. Decide the demographics and psychographics of the audience you want to target. Then, divide this audience segment into two sub-parts :

  • Test group (people you’ll expose to the marketing campaign)
  • Control group (people who won’t be exposed to the campaign)

  These groups are a part of the larger segment. This means people in both groups will have similar attributes. 

  Launch the test at the right time

  Before the launch, decide on the length of the test. Ideally, it should be at least one week. Don’t run any other campaigns in this window, as it can interfere with the results. 

  Analyse the data and take action

  Once the campaign is over, measure the results from both groups. Compare the data to identify incremental lift in your selected KPIs. 

  Let’s say you want to see if this campaign can boost your sales. Check to see if the test group responded differently than the control group. If the sales equal your desired outcome, you have a winning strategy. 

  Not all incrementality tests result in a positive incremental lift ; Some can be neutral, indicating that the campaign didn’t have any effect. Some can even indicate a negative lift, which means your core group performed better than the test group. 

  Lastly, take action based on the test findings. 

  Incrementality test examples 

  You can use incrementality testing to identify gaps and growth opportunities in your strategy. 

  Here’s an example :

  Let’s say a company runs an incrementality test on a YouTube marketing strategy for sales. The results indicate that the ROI was only $0.10, as the company makes $1.10 for every $1.00 spent. This alarms the marketing department and helps them optimise the campaign for a higher ROI. 

  Here’s another practical example :

  Let’s say a retail business wanted to test the effectiveness of its ad campaign. So, the retailer optimises its ad campaign after conducting an incrementality test on a test and control group. As a result, they experienced a 34% incremental increase in sales.

  How to calculate incrementality in marketing

  Once you’ve aggregated the data, it’s time to calculate. There are two ways to calculate incrementality :

  Incremental profit 

  The first one is incremental profit. It tells you how much profit you can generate with a strategy (If any). With it, you get the actual value of a marketing campaign. 

  It’s calculated with the following formula :

  Test group profit – control group profit = incremental profit 

  For example, let’s say you’re exposing a test group to a paid ads campaign. And it generates a profit of $3,000. On the other hand, the control group generated a $2,000 profit. 

  In this case, your incremental profit will be $1,000 ($3,000 – $2,000). 

  However, if the paid ads campaign generates a $2,000 profit, the incremental profit would be zero. Essentially, you’re generating the same profit as before, which means the campaign doesn’t work. Similarly, a marketing strategy is no good if it generates lower profits than the control group. 

  Incremental lift

  Incremental lift measures the difference in the conversions you generate with each group. 

  Here’s the formula :

  (Test – Control)/Control x 100 = Lift

  So, let’s say the test group and control group generated 2,000 and 1,000 conversions, respectively. 

  The incremental lift you’ll get from this incrementality test would be :

  (2,000 – 1,000)/1,000 x 100 = 100

  This turns out to be a 100% incremental lift.

  How to track incrementality with Matomo

  Incrementality testing lets you use a practical approach to identify the best marketing path for your business.

  It helps you develop a hyper-focused approach that gives you access to accurate and practical data. 

  With these insights, you can confidently move forward to maximise your ROI since it helps you focus on high-performing tactics. 

  The result is more revenue and profit for your business. 

  Plus, all you need to do is identify your target audience, divide them into two groups and run your test. Then, the results will be compared to determine if the marketing strategy offers any value. 

  Conducting incrementality tests may take time and expertise. 

  But, thanks to Matomo, you can leverage accurate insights for your incrementality tests to ensure you make the right decisions to grow your business.

  See for yourself why over 1 million websites choose Matomo. Try it free for 21-days now. No credit card required.

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• Benefits and Shortcomings of Multi-Touch Attribution

  13 mars 2023, par Erin — Analytics Tips

  Few sales happen instantly. Consumers take their time to discover, evaluate and become convinced to go with your offer. 

  Multi-channel attribution (also known as multi-touch attribution or MTA) helps businesses better understand which marketing tactics impact consumers’ decisions at different stages of their buying journey. Then double down on what’s working to secure more sales. 

  Unlike standard analytics, multi-channel modelling combines data from various channels to determine their cumulative and independent impact on your conversion rates. 

  The main benefit of multi-touch attribution is obvious : See top-performing channels, as well as those involved in assisted conversions. The drawback of multi-touch attribution : It comes with a more complex setup process. 

  If you’re on the fence about getting started with multi-touch attribution, here’s a summary of the main arguments for and against it. 

  What Are the Benefits of Multi-Touch Attribution ?

  Remember an old parable of blind men and an elephant ?

  Each one touched the elephant and drew conclusions about how it might look. The group ended up with different perceptions of the animal and thought the others were lying…until they decided to work together on establishing the truth.

  Multi-channel analytics works in a similar way : It reconciles data from various channels and campaign types into one complete picture. So that you can get aligned on the efficacy of different campaign types and gain some other benefits too. 

  Better Understanding of Customer Journeys 

  On average, it takes 8 interactions with a prospect to generate a conversion. These interactions happen in three stages : 

  • Awareness : You need to introduce your company to the target buyers and pique their interest in your solution (top-of-the-funnel). 
  • Consideration : The next step is to channel this casual interest into deliberate research and evaluation of your offer (middle-of-the-funnel). 
  • Decision : Finally, you need to get the buyer to commit to your offer and close the deal (bottom-of-the-funnel). 

  You can analyse funnels using various attribution models — last-click, fist-click, position-based attribution, etc. Each model, however, will spotlight the different element(s) of your sales funnel. 

  For example, a single-touch attribution model like last-click zooms in on the bottom-of-the-funnel stage. You can evaluate which channels (or on-site elements) sealed the deal for the prospect. For example, a site visitor arrived from an affiliate link and started a free trial. In this case, the affiliate (referral traffic) gets 100% credit for the conversion. 

  This measurement tactic, however, doesn’t show which channels brought the customer to the very bottom of your funnel. For instance, they may have interacted with a social media post, your landing pages or a banner ad before that. 

  Multi-touch attribution modelling takes funnel analysis a notch further. In this case, you map more steps in the customer journey — actions, events, and pages that triggered a visitor’s decision to convert — in your website analytics tool.

  

  Then, select a multi-touch attribution model, which provides more backward visibility aka allows you to track more than one channel, preceding the conversion. 

  For example, a Position Based attribution model reports back on all interactions a site visitor had between their first visit and conversion. 

  A prospect first lands at your website via search results (Search traffic), which gets a 40% credit in this model. Two days later, the same person discovers a mention of your website on another blog and visits again (Referral traffic). This time, they save the page as a bookmark and revisit it again in two more days (Direct traffic). Each of these channels will get a 10% credit. A week later, the prospect lands again on your site via Twitter (Social) and makes a request for a demo. Social would then receive a 40% credit for this conversion. Last-click would have only credited social media and first-click — search engines. 

  The bottom line : Multi-channel attribution models show how different channels (and marketing tactics) contribute to conversions at different stages of the customer journey. Without it, you get an incomplete picture.

  Improved Budget Allocation 

  Understanding causal relationships between marketing activities and conversion rates can help you optimise your budgets.

  First-click/last-click attribution models emphasise the role of one channel. This can prompt you toward the wrong conclusions. 

  For instance, your Facebook ads campaigns do great according to a first-touch model. So you decide to increase the budget. What you might be missing though is that you could have an even higher conversion rate and revenue if you fix “funnel leaks” — address high drop-off rates during checkout, improve page layout and address other possible reasons for exiting the page.

  

  By knowing when and why people abandon their purchase journey, you can improve your marketing velocity (aka the speed of seeing the campaign results) and your marketing costs (aka the budgets you allocate toward different assets, touchpoints and campaign types). 

  Or as one of the godfathers of marketing technology, Dan McGaw, explained in a webinar :

  “Once you have a multi-touch attribution model, you [can] actually know the return on ad spend on a per-campaign basis. Sometimes, you can get it down to keywords. Sometimes, you can get down to all kinds of other information, but you start to realise, “Oh, this campaign sucks. I should shut this off.” And then really, that’s what it’s about. It’s seeing those campaigns that suck and turning them off and then taking that budget and putting it into the campaigns that are working”.

  More Accurate Measurements 

  The big boon of multi-channel marketing attribution is that you can zoom in on various elements of your funnel and gain granular data on the asset’s performance. 

  In other words : You get more accurate insights into the different elements involved in customer journeys. But for accurate analytics measurements, you must configure accurate tracking. 

  Define your objectives first : How do you want a multi-touch attribution tool to help you ? Multi-channel attribution analysis helps you answer important questions such as :

  • How many touchpoints are involved in the conversions ? 
  • How long does it take for a lead to convert on average ? 
  • When and where do different audience groups convert ? 
  • What is your average win rate for different types of campaigns ?

  Your objectives will dictate which multi-channel modelling approach will work best for your business — as well as the data you’ll need to collect. 

  At the highest level, you need to collect two data points :

  • Conversions : Desired actions from your prospects — a sale, a newsletter subscription, a form submission, etc. Record them as tracked Goals. 
  • Touchpoints : Specific interactions between your brand and targets — specific page visits, referral traffic from a particular marketing channel, etc. Record them as tracked Events. 

  Your attribution modelling software will then establish correlation patterns between actions (conversions) and assets (touchpoints), which triggered them. 

  The accuracy of these measurements, however, will depend on the quality of data and the type of attribution modelling used. 

  Data quality stands for your ability to procure accurate, complete and comprehensive information from various touchpoints. For instance, some data won’t be available if the user rejected a cookie consent banner (unless you’re using a privacy-focused web analytics tool like Matomo). 

  Different attribution modelling techniques come with inherent shortcomings too as they don’t accurately represent the average sales cycle length or track visitor-level data, which allows you to understand which customer segments convert best.

  Learn more about selecting the optimal multi-channel attribution model for your business.

  What Are the Limitations of Multi-Touch Attribution ?

  Overall, multi-touch attribution offers a more comprehensive view of the conversion paths. However, each attribution model (except for custom ones) comes with inherent assumptions about the contribution of different channels (e.g,. 25%-25%-25%-25% in linear attribution or 40%-10%-10%-40% in position-based attribution). These conversion credit allocations may not accurately represent the realities of your industry. 

  Also, most attribution models don’t reflect incremental revenue you gain from existing customers, which aren’t converting through analysed channels. For example, account upgrades to a higher tier, triggered via an in-app offer. Or warranty upsell, made via a marketing email. 

  In addition, you should keep in mind several other limitations of multi-touch attribution software.

  Limited Marketing Mix Analysis 

  Multi-touch attribution tools work in conjunction with your website analytics app (as they draw most data from it). Because of that, such models inherit the same visibility into your marketing mix — a combo of tactics you use to influence consumer decisions.

  Multi-touch attribution tools cannot evaluate the impact of :

  • Dark social channels 
  • Word-of-mouth 
  • Offline promotional events
  • TV or out-of-home ad campaigns 

  If you want to incorporate this data into your multi-attribution reporting, you’ll have to procure extra data from other systems — CRM, ad measurement partners, etc, — and create complex custom analytics models for its evaluation.

  Time-Based Constraints 

  Most analytics apps provide a maximum 90-day lookback window for attribution. This can be short for companies with longer sales cycles. 

  

  Marketing channels can be overlooked or underappreciated when your attribution window is too short. Because of that, you may curtail spending on brand awareness campaigns, which, in turn, will reduce the number of people entering the later stages of your funnel. 

  At the same time, many businesses would also want to track a look-forward window — the revenue you’ll get from one customer over their lifetime. In this case, not all tools may allow you to capture accurate information on repeat conversions — through re-purchases, account tier updates, add-ons, upsells, etc. 

  Again, to get an accurate picture you’ll need to understand how far into the future you should track conversions. Will you only record your first sales as a revenue number or monitor customer lifetime value (CLV) over 3, 6 or 12 months ? 

  The latter is more challenging to do. But CLV data can add another depth of dimension to your modelling accuracy. With Matomo, you set up this type of tracking by using our visitors’ tracking feature. We can help you track select visitors with known identifiers (e.g. name or email address) to discover their visiting patterns over time. 

  

  Limited Access to Raw Data 

  In web analytics, raw data stands for unprocessed website visitor information, stripped from any filters, segmentation or sampling applied. 

  Data sampling is a practice of analysing data subsets (instead of complete records) to extrapolate findings towards the entire data set. Google Analytics 4 applies data sampling once you hit over 500k sessions at the property level. So instead of accurate, real-life reporting, you receive approximations, generated by machine learning models. Data sampling is one of the main reasons behind Google Analytics’ accuracy issues. 

  In multi-channel attribution modelling, usage of sampled data creates further inconsistencies between the reports and the actual state of affairs. For instance, if your website generates 5 million page views, GA multi-touch analytical reports are based on the 500K sample size aka only 90% of the collected information. This hardly represents the real effect of all marketing channels and can lead to subpar decision-making. 

  With Matomo, the above is never an issue. We don’t apply data sampling to any websites (no matter the volume of traffic) and generate all the reports, including multi-channel attribution ones, based on 100% real user data. 

  AI Application 

  On the other hand, websites with smaller traffic volumes often have limited sampling datasets for building attribution models. Some tracking data may also be not available because the visitor rejected a cookie banner, for instance. On average, less than 50% of users in Australia, France, Germany, Denmark and the US among other countries always consent to all cookies. 

  To compensate for such scenarios, some multi-touch attribution solutions apply AI algorithms to “fill in the blanks”, which impacts the reporting accuracy. Once again, you get approximate data of what probably happened. However, Matomo is legally exempt from showing a cookie consent banner in most EU markets. Meaning you can collect 100% accurate data to make data-driven decisions.

  Difficult Technical Implementation 

  Ever since attribution modelling got traction in digital marketing, more and more tools started to emerge.

  

  Most web analytics apps include multi-touch attribution reports. Then there are standalone multi-channel attribution platforms, offering extra features for conversion rate optimization, offline channel tracking, data-driven custom modelling, etc. 

  Most advanced solutions aren’t available out of the box. Instead, you have to install several applications, configure integrations with requested data sources, and then use the provided interfaces to code together custom data models. Such solutions are great if you have a technical marketer or a data science team. But a steep learning curve and high setup costs make them less attractive for smaller teams. 

  Conclusion 

  Multi-touch attribution modelling lifts the curtain in more steps, involved in various customer journeys. By understanding which touchpoints contribute to conversions, you can better plan your campaign types and budget allocations. 

  That said, to benefit from multi-touch attribution modelling, marketers also need to do the preliminary work : Determine the key goals, set up event and conversion tracking, and then — select the optimal attribution model type and tool. 

  Matomo combines simplicity with sophistication. We provide marketers with familiar, intuitive interfaces for setting up conversion tracking across the funnel. Then generate attribution reports, based on 100% accurate data (without any sampling or “guesstimation” applied). You can also get access to raw analytics data to create custom attribution models or plug it into another tool ! 

  Start using accurate, easy-to-use multi-channel attribution with Matomo. Start your free 21-day trial now. No credit card requried.