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• Overcoming Fintech and Finserv’s Biggest Data Analytics Challenges

  13 septembre 2024, par Daniel Crough — Banking and Financial Services, Marketing, Security

  Data powers innovation in financial technology (fintech), from personalized banking services to advanced fraud detection systems. Industry leaders recognize the value of strong security measures and customer privacy. A recent survey highlights this focus, with 72% of finance Chief Risk Officers identifying cybersecurity as their primary concern.

  Beyond cybersecurity, fintech and financial services (finserv) companies are bogged down with massive amounts of data spread throughout disconnected systems. Between this, a complex regulatory landscape and an increasingly tech-savvy and sceptical consumer base, fintech and finserv companies have a lot on their plates.

  How can marketing teams get the information they need while staying focused on compliance and providing customer value ? 

  This article will examine strategies to address common challenges in the finserv and fintech industries. We’ll focus on using appropriate tools, following effective data management practices, and learning from traditional banks’ approaches to similar issues.

  What are the biggest fintech data analytics challenges, and how do they intersect with traditional banking ?

  Recent years have been tough for the fintech industry, especially after the pandemic. This period has brought new hurdles in data analysis and made existing ones more complex. As the market stabilises, both fintech and finserve companies must tackle these evolving data issues.

  Let’s examine some of the most significant data analytics challenges facing the fintech industry, starting with an issue that’s prevalent across the financial sector :

  1. Battling data silos

  In a recent survey by InterSystems, 54% of financial institution leaders said data silos are their biggest barrier to innovation, while 62% said removing silos is their priority data strategy for the next year.

  

  Data silos segregate data repositories across departments, products and other divisions. This is a major issue in traditional banking and something fintech companies should avoid inheriting at all costs.

  Siloed data makes it harder for decision-makers to view business performance with 360-degree clarity. It’s also expensive to maintain and operationalise and can evolve into privacy and data compliance issues if left unchecked.

  To avoid or remove data silos, develop a data governance framework and centralise your data repositories. Next, simplify your analytics stack into as few integrated tools as possible because complex tech stacks are one of the leading causes of data silos.

  Use an analytics system like Matomo that incorporates web analytics, marketing attribution and CRO testing into one toolkit.

  

  Matomo’s support plans help you implement a data system to meet the unique needs of your business and avoid issues like data silos. We also offer data warehouse exporting as a feature to bring all of your web analytics, customer data, support data, etc., into one centralised location.

  Try Matomo for free today, or contact our sales team to discuss support plans.

  2. Compliance with laws and regulations

  A survey by Alloy reveals that 93% of fintech companies find it difficult to meet compliance regulations. The cost of staying compliant tops their list of worries (23%), outranking even the financial hit from fraud (21%) – and this in a year marked by cyber threats.

  

  Data privacy laws are constantly changing, and the landscape varies across global regions, making adherence even more challenging for fintechs and traditional banks operating in multiple markets. 

  In the US market, companies grapple with regulations at both federal and state levels. Here are some of the state-level legislation coming into effect for 2024-2026 :

  • Oregon – Oregon Consumer Privacy Act (July 1, 2024)
  • Florida – Florida Digital Bill of Rights (July 1, 2024)
  • Texas – Texas Data Privacy and Security Act (July 1, 2024)
  • Montana – Montana Consumer Data Privacy Act (October 1, 2024)
  • Delaware – Delaware Personal Privacy Act (January 1, 2025)
  • Iowa – Iowa Consumer Data Protection Act (January 1, 2025)
  • New Jersey – SB 332 (January 15, 2025)
  • Tennessee – Tennessee Information Protection Act (July 1, 2025)
  • Indiana – Indiana Consumer Data Protection Act ( January 1, 2026)

  Other countries are also ramping up regional regulations. For instance, Canada has Quebec’s Act Respecting the Protection of Personal Information in the Private Sector and British Columbia’s Personal Information Protection Act (BC PIPA).

  Ignorance of country- or region-specific laws will not stop companies from suffering the consequences of violating them.

  The only answer is to invest in adherence and manage business growth accordingly. Ultimately, compliance is more affordable than non-compliance – not only in terms of the potential fines but also the potential risks to reputation, consumer trust and customer loyalty.

  This is an expensive lesson that fintech and traditional financial companies have had to learn together. GDPR regulators hit CaixaBank S.A, one of Spain’s largest banks, with multiple multi-million Euro fines, and Klarna Bank AB, a popular Swedish fintech company, for €720,000.

  To avoid similar fates, companies should :

  1. Build solid data systems
  2. Hire compliance experts
  3. Train their teams thoroughly
  4. Choose data analytics tools carefully

  Remember, even popular tools like Google Analytics aren’t automatically safe. Find out how Matomo helps you gather useful insights while sticking to rules like GDPR.

  3. Protecting against data security threats

  Cyber threats are increasing in volume and sophistication, with the financial sector becoming the most breached in 2023.

  

  The cybersecurity risks will only worsen, with WEF estimating annual cybercrime expenses of up to USD $10.5 trillion globally by 2025, up from USD $3 trillion in 2015.

  While technology brings new security solutions, it also amplifies existing risks and creates new ones. A 2024 McKinsey report warns that the risk of data breaches will continue to increase as the financial industry increasingly relies on third-party data tools and cloud computing services unless they simultaneously improve their security posture.

  The reality is that adopting a third-party data system without taking the proper precautions means adopting its security vulnerabilities.

  In 2023, the MOVEit data breach affected companies worldwide, including financial institutions using its file transfer system. One hack created a global data crisis, potentially affecting the customer data of every company using this one software product.

  The McKinsey report emphasises choosing tools wisely. Why ? Because when customer data is compromised, it’s your company that takes the heat, not the tool provider. As the report states :

  “Companies need reliable, insightful metrics and reporting (such as security compliance, risk metrics and vulnerability tracking) to prove to regulators the health of their security capabilities and to manage those capabilities.”

  Don’t put user or customer data in the hands of companies you can’t trust. Work with providers that care about security as much as you do. With Matomo, you own all of your data, ensuring it’s never used for unknown purposes.

  

  4. Protecting users’ privacy

  With security threats increasing, fintech companies and traditional banks must prioritise user privacy protection. Users are also increasingly aware of privacy threats and ready to walk away from companies that lose their trust.

  Cisco’s 2023 Data Privacy Benchmark Study reveals some eye-opening statistics :

  • 94% of companies said their customers wouldn’t buy from them if their data wasn’t protected, and 
  • 95% see privacy as a business necessity, not just a legal requirement.

  Modern financial companies must balance data collection and management with increasing privacy demands. This may sound contradictory for companies reliant on dated practices like third-party cookies, but they need to learn to thrive in a cookieless web as customers move to banks and service providers that have strong data ethics.

  This privacy protection journey starts with implementing web analytics ethically from the very first session.

  

  The most important elements of ethically-sound web analytics in fintech are :

  1. 100% data ownership : Make sure your data isn’t used in other ways by the tools that collect it.
  2. Respecting user privacy : Only collect the data you absolutely need to do your job and avoid personally identifiable information.
  3. Regulatory compliance : Stick with solutions built for compliance to stay out of legal trouble.
  4. Data transparency : Know how your tools use your data and let your customers know how you use it.

  Read our guide to ethical web analytics for more information.

  5. Comparing customer trust across industries 

  While fintech companies are making waves in the financial world, they’re still playing catch-up when it comes to earning customer trust. According to RFI Global, fintech has a consumer trust score of 5.8/10 in 2024, while traditional banking scores 7.6/10.

  

  This trust gap isn’t just about perception – it’s rooted in real issues :

  • Security breaches are making headlines more often.
  • Privacy regulations like GDPR are making consumers more aware of their rights.
  • Some fintech companies are struggling to handle fraud effectively.

  According to the UK’s Payment Systems Regulator, digital banking brands Monzo and Starling had some of the highest fraudulent activity rates in 2022. Yet, Monzo only reimbursed 6% of customers who reported suspicious transactions, compared to 70% for NatWest and 91% for Nationwide.

  So, what can fintech firms do to close this trust gap ?

  • Start with privacy-centric analytics from day one. This shows customers you value their privacy from the get-go.
  • Build and maintain a long-term reputation free of data leaks and privacy issues. One major breach can undo years of trust-building.
  • Learn from traditional banks when it comes to handling issues like fraudulent transactions, identity theft, and data breaches. Prompt, customer-friendly resolutions go a long way.
  • Remember : cutting-edge financial technology doesn’t make up for poor customer care. If your digital bank won’t refund customers who’ve fallen victim to credit card fraud, they’ll likely switch to a traditional bank that will.

  The fintech sector has made strides in innovation, but there’s still work to do in establishing trustworthiness. By focusing on robust security, transparent practices, and excellent customer service, fintech companies can bridge the trust gap and compete more effectively with traditional banks.

  6. Collecting quality data

  Adhering to data privacy regulations, protecting user data and implementing ethical analytics raises another challenge. How can companies do all of these things and still collect reliable, quality data ?

  Google’s answer is using predictive models, but this replaces real data with calculations and guesswork. The worst part is that Google Analytics doesn’t even let you use all of the data you collect in the first place. Instead, it uses something called data sampling once you pass certain thresholds.

  In practice, this means that Google Analytics uses a limited set of your data to calculate reports. We’ve discussed GA4 data sampling at length before, but there are two key problems for companies here :

  1. A sample size that’s too small won’t give you a full representation of your data.
  2. The more visitors that come to your site, the less accurate your reports will become.

  For high-growth companies, data sampling simply can’t keep up. Financial marketers widely recognise the shortcomings of big tech analytics providers. In fact, 80% of them say they’re concerned about data bias from major providers like Google and Meta affecting valuable insights.

  This is precisely why CRO:NYX Digital approached us after discovering Google Analytics wasn’t providing accurate campaign data. We set up an analytics system to suit the company’s needs and tested it alongside Google Analytics for multiple campaigns. In one instance, Google Analytics failed to register 6,837 users in a single day, approximately 9.8% of the total tracked by Matomo.

  In another instance, Google Analytics only tracked 600 visitors over 24 hours, while Matomo recorded nearly 71,000 visitors – an 11,700% discrepancy.

  

  Financial companies need a more reliable, privacy-centric alternative to Google Analytics that captures quality data without putting users at potential risk. This is why we built Matomo and why our customers love having total control and visibility of their data.

  Unlock the full power of fintech data analytics with Matomo

  Fintech companies face many data-related challenges, so compliant web analytics shouldn’t be one of them. 

  With Matomo, you get :

  • An all-in-one solution that handles traditional web analytics, behavioural analytics and more with strong integrations to minimise the likelihood of data siloing
  • Full compliance with GDPR, CCPA, PIPL and more
  • Complete ownership of your data to minimise cybersecurity risks caused by negligent third parties
  • An abundance of ways to protect customer privacy, like IP address anonymisation and respect for DoNotTrack settings
  • The ability to import data from Google Analytics and distance yourself from big tech
  • High-quality data that doesn’t rely on sampling
  • A tool built with financial analytics in mind

  Don’t let big tech companies limit the power of your data with sketchy privacy policies and counterintuitive systems like data sampling. 

  Start your Matomo free trial or request a demo to unlock the full power of fintech data analytics without putting your customers’ personal information at unnecessary risk.

• What is Behavioural Segmentation and Why is it Important ?

  28 septembre 2023, par Erin — Analytics Tips

  Amidst the dynamic landscape of web analytics, understanding customers has grown increasingly vital for businesses to thrive. While traditional demographic-focused strategies possess merit, they need to uncover the nuanced intricacies of individual online behaviours and preferences. As customer expectations evolve in the digital realm, enterprises must recalibrate their approaches to remain relevant and cultivate enduring digital relationships.

  In this context, the surge of technology and advanced data analysis ushers in a marketing revolution : behavioural segmentation. Businesses can unearth invaluable insights by meticulously scrutinising user actions, preferences and online interactions. These insights lay the foundation for precisely honed, high-performing, personalised campaigns. The era dominated by blanket, catch-all marketing strategies is yielding to an era of surgical precision and tailored engagement. 

  While the insights from user behaviours empower businesses to optimise customer experiences, it’s essential to strike a delicate balance between personalisation and respecting user privacy. Ethical use of behavioural data ensures that the power of segmentation is wielded responsibly and in compliance, safeguarding user trust while enabling businesses to thrive in the digital age.

  What is behavioural segmentation ?

  Behavioural segmentation is a crucial concept in web analytics and marketing. It involves categorising individuals or groups of users based on their online behaviour, actions and interactions with a website. This segmentation method focuses on understanding how users engage with a website, their preferences and their responses to various stimuli. Behavioural segmentation classifies users into distinct segments based on their online activities, such as the pages they visit, the products they view, the actions they take and the time they spend on a site.

  Behavioural segmentation plays a pivotal role in web analytics for several reasons :

  1. Enhanced personalisation :

  Understanding user behaviour enables businesses to personalise online experiences. This aids with delivering tailored content and recommendations to boost conversion, customer loyalty and customer satisfaction.

  2. Improved user experience :

  Behavioural segmentation optimises user interfaces (UI) and navigation by identifying user paths and pain points, enhancing the level of engagement and retention.

  3. Targeted marketing :

  Behavioural segmentation enhances marketing efficiency by tailoring campaigns to user behaviour. This increases the likelihood of interest in specific products or services.

  4. Conversion rate optimisation :

  Analysing behavioural data reveals factors influencing user decisions, enabling website optimisation for a streamlined purchasing process and higher conversion rates.

  5. Data-driven decision-making :

  Behavioural segmentation empowers data-driven decisions. It identifies trends, behavioural patterns and emerging opportunities, facilitating adaptation to changing user preferences and market dynamics.

  6. Ethical considerations :

  Behavioural segmentation provides valuable insights but raises ethical concerns. User data collection and use must prioritise transparency, privacy and responsible handling to protect individuals’ rights.

  The significance of ethical behavioural segmentation will be explored more deeply in a later section, where we will delve into the ethical considerations and best practices for collecting, storing and utilising behavioural data in web analytics. It’s essential to strike a balance between harnessing the power of behavioural segmentation for business benefits and safeguarding user privacy and data rights in the digital age.

  

  Different types of behavioural segments with examples

  1. Visit-based segments : These segments hinge on users’ visit patterns. Analyse visit patterns, compare first-time visitors to returning ones, or compare users landing on specific pages to those landing on others.
     • Example : The real estate website Zillow can analyse how first-time visitors and returning users behave differently. By understanding these patterns, Zillow can customise its website for each group. For example, they can highlight featured listings and provide navigation tips for first-time visitors while offering personalised recommendations and saved search options for returning users. This could enhance user satisfaction and boost the chances of conversion.
  2. Interaction-based segments : Segments can be created based on user interactions like special events or goals completed on the site.
     • Example : Airbnb might use this to understand if users who successfully book accommodations exhibit different behaviours than those who don’t. This insight could guide refinements in the booking process for improved conversion rates.
  3. Campaign-based segments : Beyond tracking visit numbers, delve into usage differences of visitors from specific sources or ad campaigns for deeper insights.
     • Example : Nike might analyse user purchase behaviour from various traffic sources (referral websites, organic, direct, social media and ads). This informs marketing segmentation adjustments, focusing on high-performance channels. It also customises the website experience for different traffic sources, optimising content, promotions and navigation. This data-driven approach could boost user experiences and maximise marketing impact for improved brand engagement and sales conversions.
  4. Ecommerce segments : Separate users based on purchases, even examining the frequency of visits linked to specific products. Segment heavy users versus light users. This helps uncover diverse customer types and browsing behaviours.
     • Example : Amazon could create segments to differentiate between visitors who made purchases and those who didn’t. This segmentation could reveal distinct usage patterns and preferences, aiding Amazon in tailoring its recommendations and product offerings.
  5. Demographic segments : Build segments based on browser language or geographic location, for instance, to comprehend how user attributes influence site interactions.
     • Example : Netflix can create user segments based on demographic factors like geographic location to gain insight into how a visitor’s location can influence content preferences and viewing behaviour. This approach could allow for a more personalised experience.
  6. Technographic segments : Segment users by devices or browsers, revealing variations in site experience and potential platform-specific issues or user attitudes.
     • Example : Google could create segments based on users’ devices (e.g., mobile, desktop) to identify potential issues in rendering its search results. This information could be used to guide Google in providing consistent experiences regardless of device.

  

  The importance of ethical behavioural segmentation

  Respecting user privacy and data protection is crucial. Matomo offers features that align with ethical segmentation practices. These include :

  • Anonymization : Matomo allows for data anonymization, safeguarding individual identities while providing valuable insights.
  • GDPR compliance : Matomo is GDPR compliant, ensuring that user data is handled following European data protection regulations.
  • Data retention and deletion : Matomo enables businesses to set data retention policies and delete user data when it’s no longer needed, reducing the risk of data misuse.
  • Secured data handling : Matomo employs robust security measures to protect user data, reducing the risk of data breaches.

  Real-world examples of ethical behavioural segmentation :

  1. Content publishing : A leading news website could utilise data anonymization tools to ethically monitor user engagement. This approach allows them to optimise content delivery based on reader preferences while ensuring the anonymity and privacy of their target audience.
  2. Non-profit organisations : A charity organisation could embrace granular user control features. This could be used to empower its donors to manage their data preferences, building trust and loyalty among supporters by giving them control over their personal information.

  

  Examples of effective behavioural segmentation

  Companies are constantly using behavioural insights to engage their audiences effectively. In this section, we’ll delve into real-world examples showcasing how top companies use behavioural segmentation to enhance their marketing efforts.

  

  1. Coca-Cola’s behavioural insights for marketing strategy : Coca-Cola employs behavioural segmentation to evaluate its advertising campaigns. Through analysing user engagement across TV commercials, social media promotions and influencer partnerships, Coca-Cola’s marketing team can discover that video ads shared by influencers generate the highest ROI and web traffic.

     This insight guides the reallocation of resources, leading to increased sales and a more effective advertising strategy.

  2. eBay’s custom conversion approach : eBay excels in conversion optimisation through behavioural segmentation. When users abandon carts, eBay’s dynamic system sends personalised email reminders featuring abandoned items and related recommendations tailored to user interests and past purchase decisions.

     This strategy revives sales, elevates conversion rates and sparks engagement. eBay’s adeptness in leveraging behavioural insights transforms user experience, steering a customer journey toward conversion.

  3. Sephora’s data-driven conversion enhancement : Data analysts can use Sephora’s behavioural segmentation strategy to fuel revenue growth through meticulous data analysis. By identifying a dedicated subset of loyal customers who exhibit a consistent preference for premium skincare products, data analysts enable Sephora to customise loyalty programs.

     These personalised rewards programs provide exclusive discounts and early access to luxury skincare releases, resulting in heightened customer engagement and loyalty. The data-driven precision of this approach directly contributes to amplified revenue from this specific customer segment.

  Examples of the do’s and don’ts of behavioural segmentation 

  

  Behavioural segmentation is a powerful marketing and data analysis tool, but its success hinges on ethical and responsible practices. In this section, we will explore real-world examples of the do’s and don’ts of behavioural segmentation, highlighting companies that have excelled in their approach and those that have faced challenges due to lapses in ethical considerations.

  Do’s of behavioural segmentation :

  • Personalised messaging :
    • Example : Spotify
      • Spotify’s success lies in its ability to use behavioural data to curate personalised playlists and user recommendations, enhancing its music streaming experience.
  • Transparency :
    • Example : Basecamp
      • Basecamp’s transparency in sharing how user data is used fosters trust. They openly communicate data practices, ensuring users are informed and comfortable.
  • Anonymization
    • Example : Matomo’s anonymization features
      • Matomo employs anonymization features to protect user identities while providing valuable insights, setting a standard for responsible data handling.
  • Purpose limitation :
    • Example : Proton Mail
      • Proton Mail strictly limits the use of user data to email-related purposes, showcasing the importance of purpose-driven data practices.
  • Dynamic content delivery : 
    • Example : LinkedIn
      • LinkedIn uses behavioural segmentation to dynamically deliver job recommendations, showcasing the potential for relevant content delivery.
  • Data security :
    • Example : Apple
      • Apple’s stringent data security measures protect user information, setting a high bar for safeguarding sensitive data.
  • Adherence to regulatory compliance : 
    • Example : Matomo’s regulatory compliance features
      • Matomo’s regulatory compliance features ensure that businesses using the platform adhere to data protection regulations, further promoting responsible data usage.

  Don’ts of behavioural segmentation :

  • Ignoring changing regulations
    • Example : Equifax
      • Equifax faced major repercussions for neglecting evolving regulations, resulting in a data breach that exposed the sensitive information of millions.
  • Sensitive attributes
    • Example : Twitter
      • Twitter faced criticism for allowing advertisers to target users based on sensitive attributes, sparking concerns about user privacy and data ethics.
  • Data sharing without consent
    • Example : Meta & Cambridge Analytica
      • The Cambridge Analytica scandal involving Meta (formerly Facebook) revealed the consequences of sharing user data without clear consent, leading to a breach of trust.
  • Lack of control
    • Example : Uber
      • Uber faced backlash for its poor data security practices and a lack of control over user data, resulting in a data breach and compromised user information.
  • Don’t be creepy with invasive personalisation
    • Example : Offer Moment
      • Offer Moment’s overly invasive personalisation tactics crossed ethical boundaries, unsettling users and eroding trust.

  These examples are valuable lessons, emphasising the importance of ethical and responsible behavioural segmentation practices to maintain user trust and regulatory compliance in an increasingly data-driven world.

  Continue the conversation

  Diving into customer behaviours, preferences and interactions empowers businesses to forge meaningful connections with their target audience through targeted marketing segmentation strategies. This approach drives growth and fosters exceptional customer experiences, as evident from the various common examples spanning diverse industries.

  In the realm of ethical behavioural segmentation and regulatory compliance, Matomo is a trusted partner. Committed to safeguarding user privacy and data integrity, our advanced web analytics solution empowers your business to harness the power of behavioral segmentation, all while upholding the highest standards of compliance with stringent privacy regulations.

  To gain deeper insight into your visitors and execute impactful marketing campaigns, explore how Matomo can elevate your efforts. Try Matomo free for 21-days, no credit card required. 

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

  12 janvier 2024, par Erin

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

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

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

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

  What is cohort analysis in marketing ?

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

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

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

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

  

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

  Acquisition-based cohort analysis

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

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

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

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

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

  Behavioural-based cohort analysis

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

  

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

  What can you achieve with a marketing cohort analysis ?

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

  

  Understand which customers churn and why

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

  Learn which customers are most valuable

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

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

  Discover how to improve your product

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

  Find out how to improve your marketing campaign

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

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

  Measure the impact of changes

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

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

  The problem with cohort analysis in Google Analytics

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

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

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

  How to analyse cohorts with Matomo

  Luckily, there is an alternative to Google Analytics. 

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

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

  Set a goal

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

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

  Define cohorts

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

  Matomo makes it easy to define cohorts and create charts. 

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

  

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

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

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

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

  Try Matomo for Free

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

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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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