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• A Guide to Bank Customer Segmentation

  18 juillet 2024, par Erin

  Banking customers are more diverse, complex, and demanding than ever. As a result, banks have to work harder to win their loyalty, with 75% saying they would switch to a bank that better fits their needs.

  The problem is banking customers’ demands are increasingly varied amid economic uncertainties, increased competition, and generational shifts.

  If banks want to retain their customers, they can’t treat them all the same. They need a bank customer segmentation strategy that allows them to reach specific customer groups and cater to their unique demands.

  What is customer segmentation ?

  Customer segmentation divides a customer base into distinct groups based on shared characteristics or behaviours.

  This allows companies to analyse the behaviours and needs of different customer groups. Banks can use these insights to target segments with relevant marketing throughout the customer cycle, e.g., new customers, inactive customers, loyal customers, etc.

  You combine data points from multiple segmentation categories to create a customer segment. The most common customer segmentation categories include :

  • Demographic segmentation
  • Website activity segmentation
  • Geographic segmentation
  • Purchase history segmentation
  • Product-based segmentation
  • Customer lifecycle segmentation
  • Technographic segmentation
  • Channel preference segmentation
  • Value-based segmentation

  

  By combining segmentation categories, you can create detailed customer segments. For example, high-value customers based in a particular market, using a specific product, and approaching the end of the lifecycle. This segment is ideal for customer retention campaigns, localised for their market and personalised to satisfy their needs.

  

  Matomo’s privacy-centric web analytics solution helps you capture data from the first visit. Unlike Google Analytics, Matomo doesn’t use data sampling (more on this later) or AI to fill in data gaps. You get 100% accurate data for reliable insights and customer segmentation.

  Try Matomo for Free

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  Why is customer segmentation important for banks ?

  Customer segmentation allows you to address the needs of specific groups instead of treating all of your customers the same. This has never been more important amid a surge in bank switching, with three in four customers ready to switch to a provider that better suits their needs.

  Younger customers are the most likely to switch, with 19% of 18-24 year olds changing their primary bank in the past year (PDF).

  Customer expectations are changing, driven by economic uncertainties, declining trust in traditional banking, and the rise of fintech. Even as economic pressures lift, banks need to catch up with the demands of maturing millennials, Gen Z, and future generations of banking customers.

  Switching is the new normal, especially for tech-savvy customers encouraged by an expanding world of digital banking options.

  To retain customers, banks need to know them better and understand how their needs change over time. Customer retention provides the insights banks need to understand these needs at a granular level and the means to target specific customer groups with relevant messages.

  At its core, customer segmentation is essential to banks for two key reasons :

  • Customer retention : Holding on to customers for longer by satisfying their personal needs.
  • Customer lifetime value : Maximising ongoing customer revenue through retention, purchase frequency, cross-selling, and upselling.

  Here are some actionable bank customer segmentation strategies that can achieve these two objectives :

  Prevent switching with segment analysis

  Use customer segmentation to prevent them from switching to rivals by knowing what they want from you. Analyse customer needs and how they change throughout the lifecycle. Third-party data reveals general trends, but what do your customers want ?

  

  Use first-party customer data and segmentation to go beyond industry trends. Know exactly what your customers want from you and how to deliver targeted messages to each segment — e.g., first-time homebuyers vs. retirement planners.

  Keep customers active with segment targeting

  Target customer segments to keep customers engaged and motivated. Create ultra-relevant marketing messages and deliver them with precision to distinct customer segments. Nurture customer motivation by continuing to address their problems and aspirations.

  Improve the quality of services and products

  Knowing your customers’ needs in greater detail allows you to adapt your products and messages to cater to the most important segments. Customers switch banks because they feel their needs are better met elsewhere. Prevent this by implementing customer segmentation insights into product development and marketing.

  Personalise customer experiences by layering segments

  Layer segments to create ultra-specific target customer groups for personalised services and marketing campaigns. For example, top-spending customers are one of your most important segments, but there’s only so much you can do with this. However, you can divide this group into even narrower target audiences by layering multiple segments.

  For example, segmenting top-spending customers by product type can create more relevant messaging. You can also segment recent activity and pinpoint specific usage segments, such as those with a recent drop in transactions.

  Now, you have a three-layered segment of high-spending customers who use specific products less often and whom you can target with re-engagement campaigns.

  Maximise customer lifetime value

  Bringing all of this together, customer segmentation helps you maximise customer lifetime value in several ways :

  • Prevent switching
  • Enhance engagement and motivation
  • Re-engage customers
  • Cross-selling, upselling
  • Personalised customer loyalty incentives

  The longer you retain customers, the more you can learn about them, and the more effective your lifetime value campaigns will be.

  Balancing bank customer segmentation with privacy and marketing regulations

  Of course, customer segmentation uses a lot of data, which raises important legal and ethical questions. First, you need to comply with data and privacy regulations, such as GDPR and CCPA. Second, you also have to consider the privacy expectations of your customers, who are increasingly aware of privacy issues and rising security threats targeting financial service providers.

  If you aim to retain and maximise customer value, respecting their privacy and protecting their data are non-negotiables.

  Regulators are clamping down on finance

  Regulatory scrutiny towards the finance industry is intensifying, largely driven by the rise of fintech and the growing threat of cyber attacks. Not only was 2023 a record-breaking year for finance security breaches but several compromises of major US providers “exposed shortcomings in the current supervisory framework and have put considerable public pressure on banking authorities to reevaluate their supervisory and examination programs” (Deloitte).

  Banks face some of the strictest consumer protections and marketing regulations, but the digital age creates new threats.

  In 2022, the Consumer Financial Protection Bureau (CFPB) warned that digital marketers must comply with finance consumer protections when targeting audiences. CFPB Director Rohit Chopra said : “When Big Tech firms use sophisticated behavioural targeting techniques to market financial products, they must adhere to federal consumer financial protection laws.”

  This couldn’t be more relevant to customer segmentation and the tools banks use to conduct it.

  Customer data in the hands of agencies and big tech

  Banks should pay attention to the words of CFPB Director Rohit Chopra when partnering with marketing agencies and choosing analytics tools. Digital marketing agencies are rarely experts in financial regulations, and tech giants like Google don’t have the best track record for adhering to them.

  Google is constantly in the EU courts over its data use. In 2022, the EU ruled that the previous version of Google Analytics violated EU privacy regulations. Google Analytics 4 was promptly released but didn’t resolve all the issues.

  Meanwhile, any company that inadvertently misuses Google Analytics is legally responsible for its compliance with data regulations.

  Banks need a privacy-centric alternative to Google Analytics

  Google’s track record with data regulation compliance is a big issue, but it’s not the only one. Google Analytics uses data sampling, which Google defines as the “practice of analysing a subset of data to uncover meaningful information from a larger data set.”

  This means Google Analytics places thresholds on how much of your data it analyses — anything after that is calculated assumptions. We’ve explained why this is such a problem before, and GA4 relies on data sampling even more than the previous version.

  In short, banks should question whether they can trust Google with their customer data and whether they can trust Google Analytics to provide accurate data in the first place. And they do. 80% of financial marketers say they’re concerned about ad tech bias from major providers like Google and Meta.

  

  Matomo is the privacy-centric alternative to Google Analytics, giving you 100% data ownership and compliant web analytics. With no data sampling, Matomo provides 20-40% more data to help you make accurate, informed decisions. Get the data you need for customer segmentation without putting their data at risk.

  Try Matomo for Free

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

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  Bank customer segmentation examples

  Now, let’s look at some customer segments you create and layer to target specific customer groups.

  Visit-based segmentation

  Visit segmentation filters audiences based on the pages they visit on your website and the behaviors they exhibit—for example, first-time visitors vs. returning visitors or landing page visitors vs. blog page visitors.

  If you look at HSBC’s website, you’ll see it is structured into several categories for key customer personas. One of its segments is international customers living in the US, so it has pages and resources expats, people working in the US, people studying in the US, etc. 

  

  By combining visit-based segmentation with ultra-relevant pages for specific target audiences, HSBC can track each group’s demand and interest and analyse their behaviours. It can determine which audiences are returning, which products they want, and which messages convert them.

  Demographic segmentation

  Demographic segmentation divides customers by attributes such as age, gender, and location. However, you can also combine these insights with other non-personal data to better understand specific audiences.

  For example, in Matomo, you can segment audiences based on the language of their browser, the country they’re visiting from, and other characteristics. So, in this case, HSBC could differentiate between visitors already residing in the US and those outside of the country looking for information on moving there.

  

  It could determine which countries they’re visiting, which languages to localise for, and which networks to run ultra-relevant social campaigns on.

  Interaction-based segmentation

  Interaction-based segmentation uses events and goals to segment users based on their actions on your website. For example, you can segment audiences who visit specific URLs, such as a loan application page, or those who don’t complete an action, such as failing to complete a form.

  

  With events and goals set up, you can track the actions visitors complete before making purchases. You can monitor topical interests, page visits, content interactions, and pathways toward conversions, which feed into their customer journey.

  From here, you can segment customers based on their path leading up to their first purchase, follow-up purchases, and other actions.

  Purchase-based segmentation

  Purchase-based segmentation allows you to analyse the customer behaviours related to their purchase history and spending habits. For example, you can track the journey of repeat customers or identify first-time buyers showing interest in other products/services.

  You can implement these insights into your cross-selling and upselling campaigns with relevant messages designed to increase retention and customer lifetime value.

  Get reliable website analytics for your bank customer segmentation needs

  With customers switching in greater numbers, banks need to prioritise customer retention and lifetime value. Customer segmentation allows you to target specific customer groups and address their unique needs — the perfect strategy to stop them from moving to another provider.

  Quality, accurate data is the key ingredient of an effective bank customer segmentation strategy. Don’t accept data sampling from Google Analytics or any other tool that limits the amount of your own data you can access. Choose a web analytics tool like Matamo that unlocks the full potential of your website analytics to get the most out of bank customer segmentation.

  Matomo is trusted by over 1 million websites globally, including many banks, for its accuracy, compliance, and reliability. Discover why financial institutions rely on Matomo to meet their web analytics needs.

  Start collecting the insights you need for granular, layered segmentation — without putting your bank customer data at risk. Request a demo of Matomo now.

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• Clickstream Data : Definition, Use Cases, and More

  15 avril 2024, par Erin

  Gaining a deeper understanding of user behaviour — customers’ different paths, digital footprints, and engagement patterns — is crucial for providing a personalised experience and making informed marketing decisions. 

  In that sense, clickstream data, or a comprehensive record of a user’s online activities, is one of the most valuable sources of actionable insights into users’ behavioural patterns. 

  This article will cover everything marketing teams need to know about clickstream data, from the basic definition and examples to benefits, use cases, and best practices. 

  What is clickstream data ? 

  As a form of web analytics, clickstream data focuses on tracking and analysing a user’s online activity. These digital breadcrumbs offer insights into the websites the user has visited, the pages they viewed, how much time they spent on a page, and where they went next.

  

  Your clickstream pipeline can be viewed as a “roadmap” that can help you recognise consistent patterns in how users navigate your website. 

  With that said, you won’t be able to learn much by analysing clickstream data collected from one user’s session. However, a proper analysis of large clickstream datasets can provide a wealth of information about consumers’ online behaviours and trends — which marketing teams can use to make informed decisions and optimise their digital marketing strategy. 

  Clickstream data collection can serve numerous purposes, but the main goal remains the same — gaining valuable insights into visitors’ behaviours and online activities to deliver a better user experience and improve conversion likelihood. 

  Depending on the specific events you’re tracking, clickstream data can reveal the following : 

  • How visitors reach your website 
  • The terms they type into the search engine
  • The first page they land on
  • The most popular pages and sections of your website
  • The amount of time they spend on a page 
  • Which elements of the page they interact with, and in what sequence
  • The click path they take 
  • When they convert, cancel, or abandon their cart
  • Where the user goes once they leave your website

  As you can tell, once you start collecting this type of data, you’ll learn quite a bit about the user’s online journey and the different ways they engage with your website — all without including any personal details about your visitors.

  Types of clickstream data 

  While all clickstream data keeps a record of the interactions that occur while the user is navigating a website or a mobile application — or any other digital platform — it can be divided into two types : 

  • Aggregated (web traffic) data provides comprehensive insights into the total number of visits and user interactions on a digital platform — such as your website — within a given timeframe 
  • Unaggregated data is broken up into smaller segments, focusing on an individual user’s online behaviour and website interactions 

  One thing to remember is that to gain valuable insights into user behaviour and uncover sequential patterns, you need a powerful tool and access to full clickstream datasets. Matomo’s Event Tracking can provide a comprehensive view of user interactions on your website or mobile app — everything from clicking a button and completing a form to adding (or removing) products from their cart. 

  On that note, based on the specific events you’re tracking when a user visits your website, clickstream data can include : 

  • Web navigation data : referring URL, visited pages, click path, and exit page
  • User interaction data : mouse movements, click rate, scroll depth, and button clicks
  • Conversion data : form submissions, sign-ups, and transactions 
  • Temporal data : page load time, timestamps, and the date and time of day of the user’s last login 
  • Session data : duration, start, and end times and number of pages viewed per session
  • Error data : 404 errors and network or server response issues 

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  Clickstream data benefits and use cases 

  Given the actionable insights that clickstream data collection provides, it can serve a wide range of use cases — from identifying behavioural patterns and trends and examining competitors’ performance to helping marketing teams map out customer journeys and improve ROI.

  

  According to the global Clickstream Analytics Market Report 2024, some key applications of clickstream analytics include click-path optimisation, website and app optimisation, customer analysis, basket analysis, personalisation, and traffic analysis. 

  

  The behavioural patterns and user preferences revealed by clickstream analytics data can have many applications — we’ve outlined the prominent use cases below. 

  Customer journey mapping 

  Clickstream data allows you to analyse the e-commerce customer’s online journey and provides insights into how they navigate your website. With such a comprehensive view of their click path, it becomes easier to understand user behaviour at each stage — from initial awareness to conversion — identify the most effective touchpoints and fine-tune that journey to improve their conversion likelihood. 

  Identifying customer trends 

  Clickstream data analytics can also help you identify trends and behavioural patterns — the most common sequences and similarities in how users reached your website and interacted with it — especially when you can access data from many website visitors. 

  Think about it — there are many ways in which you can use these insights into the sequence of clicks and interactions and recurring patterns to your team’s advantage. 

  Here’s an example : 

  It can reveal that some pieces of content and CTAs are performing well in encouraging visitors to take action — which shows how you should optimise other pages and what you should strive to create in the future, too. 

  Preventing site abandonment 

  Cart abandonment remains a serious issue for online retailers : 

  According to a recent report, the global cart abandonment rate in the fourth quarter of 2023 was at 83%. 

  That means that roughly eight out of ten e-commerce customers will abandon their shopping carts — most commonly due to additional costs, slow website loading times and the requirement to create an account before purchasing. 

  In addition to cart abandonment predictions, clickstream data analytics can reveal the pages where most visitors tend to leave your website. These drop-off points are clear indicators that something’s not working as it should — and once you can pinpoint them, you’ll be able to address the issue and increase conversion likelihood.

  Improving marketing campaign ROI 

  As previously mentioned, clickstream data analysis provides insights into the customer journey. Still, you may not realise that you can also use this data to keep track of your marketing effectiveness. 

  Global digital ad spending continues to grow — and is expected to reach $836 billion by 2026. It’s easy to see why relying on accurate data is crucial when deciding which marketing channels to invest in. 

  You want to ensure you’re allocating your digital marketing and advertising budget to the channels — be it SEO, pay-per-click (PPC) ads, or social media campaigns — that impact driving conversions. 

  When you combine clickstream e-commerce data with conversion rates, you’ll find the latter in Matomo’s goal reports and have a solid, data-driven foundation for making better marketing decisions.

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  Delivering a better user experience (UX) 

  Clickstream data analysis allows you to identify specific “pain points” — areas of the website that are difficult to use and may cause customer frustration. 

  It’s clear how this would be beneficial to your business : 

  Once you’ve identified these pain points, you can make the necessary changes to your website’s layout and address any technical issues that users might face, improving usability and delivering a smoother experience to potential customers. 

  Collecting clickstream data : Tools and legal implications 

  Your team will need a powerful tool capable of handling clickstream analytics to reap the benefits we’ve discussed previously. But at the same time, you need to respect users’ online privacy throughout clickstream data collection.

  

  Generally speaking, there are two ways to collect data about users’ online activity — web analytics tools and server log files.

  Web analytics tools are the more commonly used solution. Specifically designed to collect and analyse website data, these tools rely on JavaScript tags that run in the browser, providing actionable insights about user behaviour. Server log files can be a gold mine of data, too — but that data is raw and unfiltered, making it much more challenging to interpret and analyse. 

  That brings us to one of the major clickstream challenges to keep in mind as you move forward — compliance.

  While Google remains a dominant player in the web analytics market, there’s one area where Matomo has a significant advantage — user privacy. 

  Matomo operates according to privacy laws — including the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), making it an ethical alternative to Google Analytics. 

  It should go without saying, but compliance with data privacy laws — the most talked-about one being the GDPR framework introduced by the EU — isn’t something you can afford to overlook. 

  The GDPR was first implemented in the EU in 2018. Since then, several fines have been issued for non-compliance — including the record fine of €1.2 billion that Meta Platforms, Inc. received in 2023 for transferring personal data of EU-based users to the US.

  Clickstream analytics data best practices 

  

  As valuable as it might be, processing large amounts of clickstream analytics data can be a complex — and, at times, overwhelming — process. 

  Here are some best practices to keep in mind when it comes to clickstream analysis : 

  Define your goals 

  It’s essential to take the time to define your goals and objectives. 

  Once you have a clear idea of what you want to learn from a given clickstream dataset and the outcomes you hope to see, it’ll be easier to narrow down your scope — rather than trying to tackle everything at once — before moving further down the clickstream pipeline. 

  Here are a few examples of goals and objectives you can set for clickstream analysis : 

  • Understanding and predicting users’ behavioural patterns 
  • Optimising marketing campaigns and ROI 
  • Attributing conversions to specific marketing touchpoints and channels

  Analyse your data 

  Collecting clickstream analytics data is only part of the equation ; what you do with raw data and how you analyse it matters. You can have the most comprehensive dataset at your disposal — but it’ll be practically worthless if you don’t have the skill set to analyse and interpret it. 

  In short, this is the stage of your clickstream pipeline where you uncover common sequences and consistent patterns in user behaviour. 

  Clickstream data analytics can extract actionable insights from large datasets using various approaches, models, and techniques. 

  Here are a few examples : 

  • If you’re working with clickstream e-commerce data, you should perform funnel or conversion analyses to track conversion rates as users move through your sales funnel. 
  • If you want to group and analyse users based on shared characteristics, you can use Matomo for cohort analysis. 
  • If your goal is to predict future trends and outcomes — conversion and cart abandonment prediction, for example — based on available data, prioritise predictive analytics.

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  Organise and visualise your data

  As you reach the end of your clickstream pipeline, you need to start thinking about how you will present and communicate your data. And what better way to do that than to transform that data into easy-to-understand visualisations ? 

  Here are a few examples of easily digestible formats that facilitate quick decision-making : 

  • User journey maps, which illustrate the exact sequence of interactions and user flow through your website 
  • Heatmaps, which serve as graphical — and typically colour-coded — representations of a website visitor’s activity 
  • Funnel analysis, which are broader at the top but get increasingly narrower towards the bottom as users flow through and drop off at different stages of the pipeline 

  Collect clickstream data with Matomo 

  Clickstream data is hard to beat when tracking the website visitor’s journey — from first to last interaction — and understanding user behaviour. By providing real-time insights, your clickstream pipeline can help you see the big picture, stay ahead of the curve and make informed decisions about your marketing efforts. 

  Matomo accurate data and compliance with GDPR and other data privacy regulations — it’s an all-in-one, ethical platform that can meet all your web analytics needs. That’s why over 1 million websites use Matomo for their web analytics.

  Try Matomo free for 21 days. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

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• Web Analytics : The Quick Start Guide

  25 janvier 2024, par Erin

  You’ve spent ages carefully designing your website, crafting copy to encourage as many users as possible to purchase your product. 

  But they aren’t. And you don’t know why. 

  The good news is you don’t have to remain in the dark. Collecting and analysing web analytics lets you understand how your users behave on your site and why they aren’t converting. 

  But before you can do that, you need to know what those metrics and KPIs mean. That’s why this article is taking things back to basics. Below, we’ll show you which metrics to track, what they mean and how to choose the best web analytics platform. 

  What is web analytics ?

  Web analytics is the process of collecting, analysing and reporting website data to understand how users behave on your website. Web analytics platforms like Matomo collect this data by adding a code line to every site page. 

  Why is it important to track web analytics ?

  There are plenty of reasons you should start tracking web analytics, including the following :

  

  Analyse user behaviour

  Being able to analyse user behaviour is the most important reason to track website analytics. After all, you can’t improve your website’s conversion rate if you don’t know what users do on your site.

  A web analytics platform can show you how users move around your site, the links they click on and the forms they fill in. 

  Improve site experience

  Web analytics is a fantastic way to identify issues and find areas where your site could improve. You could look at your site’s exit pages, for example, and see why so many users leave your site when viewing one of these pages and what you can do to fix it.

  It can also teach you about your user’s preferences so you can improve the user experience in the future. Maybe they always click a certain type of button or prefer one page’s design over another. Whatever the case, you can use the data to make your site more user-friendly and increase conversions.

  Boost marketing efforts

  Web analytics is one of the best ways to understand your marketing efforts and learn how to improve them.

  A good platform can collect valuable data about your marketing campaigns, including :

  • Where users came from
  • What actions these users take on your site
  • Which traffic sources create the most conversions

  This information can help you decide which marketing campaigns send the best users to your site and generate the highest ROI. 

  Make informed decisions

  Ultimately, web analytics simplifies decision-making for your website and marketing efforts by relying on concrete data instead of guesswork.

  Rather than wonder why users aren’t adding products to their shopping cart or signing up for your newsletter, you can analyse how they behave and use that information to hypothesise how you can improve conversions. Web analytics will even give you the data to confirm whether you were right or wrong. 

  What are the key metrics you should track ?

  Getting your head around web analytics means knowing the most important metrics to track. Below are seven key metrics and how to track them using Matomo. 

  Traffic

  Traffic is the number of people visiting your website over a period of time. It is the lifeblood of your website since the more visits your site receives, the more revenue it stands to generate.

  However, simply having a high volume of visitors does not guarantee substantial revenue. To maximise your success, focus on attracting your ideal customers and generating quality traffic from those who are most likely to engage with your offerings.

  Ideally, you should be seeing an upward trend in traffic over time though. The longer your website has been published and the more quality and targeted content you create, the more traffic you should receive. 

  Matomo offers multiple ways to check your website’s traffic :

  • Visits log report
  • Real-time visitor map
  • Visits in real-time report

  The visits log report in Matomo is perfect if you want a granular view of your visitors.

  

  It shows you each user session and get a detailed picture of each user, including :

  • Their geographic location
  • The number of actions they took
  • How they found your site
  • The length of time they stayed
  • Their device type
  • What browser they are using
  • The keyword they used to find your site

  Traffic sources

  Traffic sources show how users access your website. They can enter via a range of traffic sources, including search engines, email and direct visits, for instance.

  Matomo has five default traffic source types :

  • Search engine – visitors from search platforms (like Google, Bing, etc.)
  • Direct traffic – individuals who directly type your website’s URL into their browser or have it bookmarked, bypassing search engines or external links
  • Websites – visits from other external sites
  • Campaigns – traffic resulting from specific marketing initiatives (like a newsletter or ad campaign, for instance)
  • Social networks  – visitors who access your website through various social media platforms (such as Facebook, LinkedIn, Instagram. etc.)

  But each of these can be broken into more granular sources. Take organic traffic from search engines, for example :

  

  Matomo tracks visits from each search engine, showing you how many visits you had in total, how many actions those visitors took, and the average amount of time those visitors spent on your site. 

  You can even integrate Google, Bing and Yahoo search consoles to monitor keyword performance and enhance your search engine optimisation efforts.

  Pageviews

  Whenever a browser loads a page, your web analytics tool records a pageview. This term, pageview, represents the count of unique times a page on your website is loaded.

  You can track pageviews in Matomo by opening the Pages tab in the Behaviour section of the main navigation. 

  

  You can quickly see your site’s most visited pages in this report in Matomo. 

  Be careful of deriving too much meaning from pageviews. Just because a page has lots of views, doesn’t necessarily mean it’s quality or valuable. There are a couple of reasons for this. First, the page might be confusing, so users have to keep revisiting it to understand the content. Second, it could be the default page most visitors land on when they enter your site, like the homepage. 

  While pageviews offer insights, it’s important to dig deeper into user behaviour and other metrics to truly gauge a page’s importance and impact.

  Average time on page

  Time on page is the amount of time users spend on the page on average. You can see average time on page in Matomo’s page analytics report.

  A low time on page score isn’t necessarily a bad thing. Users will naturally spend less time on gateway pages and checkout pages. A short time spent on checkout pages, especially if users are successfully completing their transactions, indicates that the checkout process is easy and seamless.

  Conversely, a longer time on blog posts is a positive indicator. It suggests that readers are genuinely engaged with the content.

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  Returning visitors

  Returning visitors measures the number of people who visit your site more than once. It can be expressed as a number or a percentage. 

  While some analytics tools only show returning visitors as a percentage, Matomo lets you learn more about each of them in the Visitor profile report. 

  

  This report offers a full summary of a user’s previous actions, including :

  • How many times they’ve visited your site
  • The pages they viewed on each visit
  • Where they visited from
  • The devices they used
  • How quickly pages loaded

  When people keep coming back to a website, it’s usually a positive sign and means they like the service, content or products. But, it depends on the type of website. If it’s the kind of site where people make one-off purchases, the focus might not be on getting visitors to return. For a site like this, a high number of returning visitors could indicate that the website is confusing or difficult to use. 

  It’s all about the context – different websites have different goals, and it’s important to keep this in mind when analysing your site.

  Conversions

  A conversion is when a user takes a desired action on your website. This could be :

  • Making a purchase
  • Subscribing to your newsletter
  • Signing up for a webinar

  You can track virtually any action as a conversion in Matomo by setting goals and analysing the goals report.

  

  As you can see in the screenshot above, Matomo shows your conversions plotted over time. You can also see your conversion rate to get a complete picture and assign a value to each conversion to calculate how much revenue each conversion generates. 

  Bounce rate

  A visitor bounces when they leave your website without taking an action or visiting another page. 

  Typically, you want bounce rate to be low because it means people are engaged with your site and more likely to convert. However, in some cases, a high bounce rate isn’t necessarily bad. It might mean that visitors found what they needed on the first page and didn’t feel the need to look further. 

  The impact of bounce rate depends on your website’s purpose and goals.

  You can view your website’s bounce rate using Matomo’s page analytics report — the same report that shows pageviews.

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  Web analytics best practices

  You should follow several best practices to get the most from website analytics data. 

  Choose metrics that align with your goals

  Only some metrics your analytics platform tracks will be relevant to your business. So don’t waste time analysing all of them.

  Instead, focus on the ones that matter most to your business. A marketer for an e-commerce store, for example, might focus on conversion-related metrics like conversion rate and total number of transactions. They might also want to look at campaign-related metrics, like traffic sources and bounce rates, so they can optimise paid ad campaigns accordingly. 

  A marketer looking to improve their site’s SEO, on the other hand, will want to track SEO web analytics like bounce rate and broken links.

  Add context to your data

  Don’t take your data at face value. There could be dozens of factors that impact how visitors access and use your site — many of which are outside your control. 

  For example, you may think an update to your site has sent your conversions crashing when, in reality, a Google algorithm update has negatively impacted your search traffic.

  Adding annotations within Matomo can provide invaluable context to your data. These annotations can be used to highlight specific events, changes or external factors that might influence your website metrics.

  

  By documenting significant occurrences, such as website updates, marketing campaigns or algorithm changes, you create a timeline that helps explain fluctuations in your data.

  Go further with advanced web analytics features

  It’s clear that a web analytics platform is a necessary tool to understand your website’s performance.

  However, if you want greater confidence in decision-making, quicker insights and better use of budget and resources, you need an advanced solution with behavioural analytics features like heatmaps, A/B testing and session recordings. 

  Most web analytics solutions don’t offer these advanced features, but Matomo does, so we’ll be showcasing Matomo’s behavioural analytics features.

  Now, if you don’t have a Matomo account, you can try it free for 21-days to see if it’s the right tool for you.

  

  A heatmap, like the example above, makes it easy to discover where your users pay attention, which part of your site they have problems with, and how they convert. It adds a layer of qualitative data to the facts offered by your web analytics tool.

  Similarly, session recordings will offer you real-time playbacks of user interactions, helping you understand their navigation patterns, identify pain points and gain insights into the user experience.

  Then you can run experiments bu using A/B testing to compare different versions of your website or specific elements, allowing you to make informed decisions based on actual user preferences and behaviour. For instance, you can compare different headlines, images, page layouts or call-to-action buttons to see which resonates better with your audience. 

  Together, these advanced features will give you the confidence to optimise your website, improve user satisfaction and make data-driven decisions that positively impact your business.

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  How to choose a web analytics tool

  A web analytics tool is the best way to track the above metrics. Choose the best one for your company by following the steps below. 

  Look for the right features

  Most popular web analytics platforms, like Google Analytics, will offer the same core features like tracking website traffic, monitoring conversions and generating reports. 

  But it’s the added features that set great tools apart. Do you need specific tools to measure the performance of your e-commerce store, for example ? What about paid ad performance, A/B testing or form analytics ?

  By understanding exactly what you need from an analytics platform, you can make an informed choice. 

  Think about data accuracy

  Data accuracy is one of the biggest issues with analytics tools. Many users block cookies or opt out of tracking, making it difficult to get a clear picture of user behaviour — and meaning that you have to think about how your user data will be collected with your chosen platform.

  Google Analytics, for instance, uses data sampling to make assumptions about traffic levels rather than relying on accurate data. This can lead to inaccurate reports and false conclusions. 

  It’s why Matomo doesn’t use data sampling and provides 100% accurate data. 

  Understand how you’ll deal with data privacy

  Data privacy is another big concern for analytics users. Several major analytics platforms aren’t compatible with regional data privacy laws like GDPR, which can impact your ability to collect data in these regions. 

  It’s why many companies trust privacy-focused analytics tools that abide by regulations without impacting your ability to collect data. Matomo is a market leader in this respect and is one of the few web analytics tools that the Centre for Data Privacy Protection in France has said is exempt from tracking consent requirements.

  Many government agencies across Europe, Asia, Africa and North America, including organisations like the United Nations and European Commission, rely on Matomo for web analytics.

  Conclusion

  Web analytics is a powerful tool that helps you better understand your users, improve your site’s performance and boost your marketing efforts. 

  If you want a platform that offers advanced features, 100% accurate data and protects your users’ privacy, then look no further than Matomo. 

  Try Matomo free for 21 days, no credit card required. 

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