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• A Comprehensive Guide to Robust Digital Marketing Analytics

  30 octobre 2023, par Erin

  First impressions are everything. This is not only true for dating and job interviews but also for your digital marketing strategy. Like a poorly planned resume getting tossed in the “no thank you” pile, 38% of visitors to your website will stop engaging with your content if they find the layout unpleasant. Thankfully, digital marketers can access data that can be harnessed to optimise websites and turn those “no thank you’s” into “absolutely’s.”

  So, how can we transform raw data into valuable insights that pay off ? The key is web analytics tools that can help you make sense of it all while collecting data ethically. In this article, we’ll equip you with ways to take your digital marketing strategy to the next level with the power of web analytics.

  What are the different types of digital marketing analytics ?

  Digital marketing analytics are like a cipher into the complex behaviour of your buyers. Digital marketing analytics help collect, analyse and interpret data from any touchpoint you interact with your buyers online. Whether you’re trying to gauge the effectiveness of a new email marketing campaign or improve your mobile app layout, there’s a way for you to make use of the insights you gain. 

  As we go through the eight commonly known types of digital marketing analytics, please note we’ll primarily focus on what falls under the umbrella of web analytics. 

  1. Web analytics help you better understand how users interact with your website. Good web analytics tools will help you understand user behaviour while securely handling user data. 
  2. Learn more about the effectiveness of your organisation’s social media platforms with social media analytics. Social media analytics include user engagement, post reach and audience demographics. 
  3. Email marketing analytics help you see how email campaigns are being engaged with.
  4. Search engine optimisation (SEO) analytics help you understand your website’s visibility in search engine results pages (SERPs). 
  5. Pay-per-click (PPC) analytics measure the performance of paid advertising campaigns.
  6. Content marketing analytics focus on how your content is performing with your audience. 
  7. Customer analytics helps organisations identify and examine buyer behaviour to retain the biggest spenders. 
  8. Mobile app analytics track user interactions within mobile applications. 

  Choosing which digital marketing analytics tools are the best fit for your organisation is not an easy task. When making these decisions, it’s critical to remember the ethical implications of data collection. Although data insights can be invaluable to your organisation, they won’t be of much use if you lose the trust of your users. 

  Tips and best practices for developing robust digital marketing analytics 

  So, what separates top-notch, robust digital marketing analytics from the rest ? We’ve already touched on it, but a big part involves respecting user privacy and ethically handling data. Data security should be on your list of priorities, alongside conversion rate optimisation when developing a digital marketing strategy. In this section, we will examine best practices for using digital marketing analytics while retaining user trust.

  

  Clear objectives

  Before comparing digital marketing analytics tools, you should define clear and measurable goals. Try asking yourself what you need your digital marketing analytics strategy to accomplish. Do you want to improve conversion rates while remaining data compliant ? Maybe you’ve noticed users are not engaging with your platform and want to fix that. Save yourself time and energy by focusing on the most relevant pain points and areas of improvement.

  Choose the right tools for the job

  Don’t just base your decision on what other people tell you. Take the tool for a test drive — free trials allow you to test features and user interfaces and learn more about the platform before committing. When choosing digital marketing analytics tools, look for ones that ensure compliance with privacy laws like GDPR.

  Don’t overlook data compliance

  GDPR ensures organisations prioritise data protection and privacy. You could be fined up to €20 million, or 4% of the previous year’s revenue for violations. Without data compliance practices, you can say goodbye to the time and money spent on digital marketing strategies. 

  Don’t sacrifice data quality and accuracy

  Inaccurate and low-quality data can taint your analysis, making it hard to glean valuable insights from your digital marketing analytics efforts. Regularly audit and clean your data to remove inaccuracies and inconsistencies. Address data discrepancies promptly to maintain the integrity of your analytics. Data validation measures also help to filter out inaccurate data.

  Communicate your findings

  Having insights is one thing ; effectively communicating complex data findings is just as important. Customise dashboards to display key metrics aligned with your objectives. Make sure to automate reports, allowing stakeholders to stay updated without manual intervention. 

  Understand the user journey

  To optimise your conversion rates, you need to understand the user journey. Start by analysing visitors interactions with your website — this will help you identify conversion bottlenecks in your sales or lead generation processes. Implement A/B testing for landing page optimisation, refining elements like call-to-action buttons or copy, and leverage Form Analytics to make informed, data-driven improvements to your forms.

  Continuous improvement

  Learn from the data insights you gain, and iterate your marketing strategies based on the findings. Stay updated with evolving web analytics trends and technologies to leverage new growth opportunities.

  Why you need web analytics to support your digital marketing analytics toolbox

  You wouldn’t set out on a roadtrip without a map, right ? Digital marketing analytics without insights into how users interact with your website are just as useless. Used ethically, web analytics tools can be an invaluable addition to your digital marketing analytics toolbox. 

  The data collected via web analytics reveals user interactions with your website. These could include anything from how long visitors stay on your page to their actions while browsing your website. Web analytics tools help you gather and understand this data so you can better understand buyer preferences. It’s like a domino effect : the more you understand your buyers and user behaviour, the better you can assess the effectiveness of your digital content and campaigns. 

  Web analytics reveal user behaviour, highlighting navigation patterns and drop-off points. Understanding these patterns helps you refine website layout and content, improving engagement and conversions for a seamless user experience.

  

  Concrete CMS harnessed the power of web analytics, specifically Form Analytics, to uncover a crucial insight within their user onboarding process. Their data revealed a significant issue : the “address” input field was causing visitors to drop off and not complete the form, severely impacting the overall onboarding experience and conversion rate.

  Armed with these insights, Concrete CMS made targeted optimisations to the form, resulting in a substantial transformation. By addressing the specific issue identified through Form Analytics, they achieved an impressive outcome – a threefold increase in lead generation.

  This case is a great example of how web analytics can uncover customer needs and preferences and positively impact conversion rates. 

  Ethical implications of digital marketing analytics

  As we’ve touched on, digital marketing analytics are a powerful tool to help better understand online user behaviour. With great power comes great responsibility, however, and it’s a legal and ethical obligation for organisations to protect individual privacy rights. Let’s get into the benefits of practising ethical digital marketing analytics and the potential risks of not respecting user privacy : 

  • If someone uses your digital platform and then opens their email one day to find it filled with random targeted ad campaigns, they won’t be happy. Avoid losing user trust — and facing a potential lawsuit — by informing users what their data will be used for. Give them the option to consent to opt-in or opt-out of letting you use their personal information. If users are also assured you’ll safeguard personal information against unauthorised access, they’ll be more likely to trust you to handle their data securely.
  • Protecting data against breaches means investing in technology that will let you end-to-end encrypt and securely store data. Other important data-security best practices include access control, backing up data regularly and network and physical security of assets.
  • A fine line separates digital marketing analytics and misusing user data — many companies have gotten into big trouble for crossing it. (By big trouble, we mean millions of dollars in fines.) When it comes to digital marketing analytics, you should never cut corners when it comes to user privacy and data security. This balance involves understanding what data can be collected and what should be collected and respecting user boundaries and preferences.

  

  Learn more 

  We discussed a lot of facets of digital marketing analytics, namely how to develop a robust digital marketing strategy while prioritising data compliance. With Matomo, you can protect user data and respect user privacy while gaining invaluable insights into user behaviour. Save your organisation time and money by investing in a web analytics solution that gives you the best of both worlds. 

  If you’re ready to begin using ethical and robust digital marketing analytics on your website, try Matomo. Start your 21-day free trial now — no credit card required.

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• Cohort Analysis 101 : How-To, Examples & Top Tools

  13 novembre 2023, par Erin — Analytics Tips

  Imagine that a farmer is trying to figure out why certain hens are laying large brown eggs and others are laying average-sized white eggs.

  The farmer decides to group the hens into cohorts based on what kind of eggs they lay to make it easier to detect patterns in their day-to-day lives. After careful observation and analysis, she discovered that the hens laying big brown eggs ate more than the roost’s other hens.

  With this cohort analysis, the farmer deduced that a hen’s body weight directly corresponds to egg size. She can now develop a strategy to increase the body weight of her hens to sell more large brown eggs, which are very popular at the weekly farmers’ market.

  Cohort analysis has a myriad of applications in the world of web analytics. Like our farmer, you can use it to better understand user behaviour and reap the benefits of your efforts. This article will discuss the best practices for conducting an effective cohort analysis and compare the top cohort analysis tools for 2024. 

  What is cohort analysis ?

  By definition, cohort analysis refers to a technique where users are grouped based on shared characteristics or behaviours and then examined over a specified period.

  Think of it as a marketing superpower, enabling you to comprehend user behaviours, craft personalised campaigns and allocate resources wisely, ultimately resulting in improved performance and better ROI.

  Why does cohort analysis matter ?

  In web analytics, a cohort is a group of users who share a certain behaviour or characteristic. The goal of cohort analysis is to uncover patterns and compare the performance and behaviour of different cohorts over time.

  An example of a cohort is a group of users who made their first purchase during the holidays. By analysing this cohort, you could learn more about their behaviour and buying patterns. You may discover that this cohort is more likely to buy specific product categories as holiday gifts — you can then tailor future holiday marketing campaigns to include these categories. 

  Types of cohort analysis

  There are a few different types of notable cohorts : 

  1. Time-based cohorts are groups of users categorised by a specific time. The example of the farmer we went over at the beginning of this section is a great example of a time-based cohort.
  2. Acquisition cohorts are users acquired during a specific time frame, event or marketing channel. Analysing these cohorts can help you determine the value of different acquisition methods. 
  3. Behavioural cohorts consist of users who show similar patterns of behaviour. Examples include frequent purchases with your mobile app or digital content engagement. 
  4. Demographic cohorts share common demographic characteristics like age, gender, education level and income. 
  5. Churn cohorts are buyers who have cancelled a subscription/stopped using your service within a specific time frame. Analysing churn cohorts can help you understand why customers leave.
  6. Geographic cohorts are pretty self-explanatory — you can use them to tailor your marketing efforts to specific regions. 
  7. Customer journey cohorts are based on the buyer lifecycle — from acquisition to adoption to retention. 
  8. Product usage cohorts are buyers who use your product/service specifically (think basic users, power users or occasional users). 

  Best practices for conducting a cohort analysis 

  So, you’ve decided you want to understand your user base better but don’t know how to go about it. Perhaps you want to reduce churn and create a more engaging user experience. In this section, we’ll walk you through the dos and don’ts of conducting an effective cohort analysis. Remember that you should tailor your cohort analysis strategy for organisation-specific goals.

  

  1. Preparing for cohort analysis : 

  • • First, define specific goals you want your cohort analysis to achieve. Examples include improving conversion rates or reducing churn.
    • Choosing the right time frame will help you compare short-term vs. long-term data trends. 

  2. Creating effective cohorts : 

  • • Define your segmentation criteria — anything from demographics to location, purchase history or user engagement level. Narrowing in on your specific segments will make your cohort analysis more precise. 
    • It’s important to find a balance between cohort size and similarity. If your cohort is too small and diverse, you won’t be able to find specific behavioural patterns.

  3. Performing cohort analysis :

  • • • Study retention rates across cohorts to identify patterns in user behaviour and engagement over time. Pay special attention to cohorts with high retention or churn rates. 
      • Analysing cohorts can reveal interesting behavioural insights — how do specific cohorts interact with your website ? Do they have certain preferences ? Why ? 

  4. Visualising and interpreting data :

  • • Visualising your findings can be a great way to reveal patterns. Line charts can help you spot trends, while bar charts can help you compare cohorts.
    • Guide your analytics team on how to interpret patterns in cohort data. Watch for sudden drops or spikes and what they could mean. 

  5. Continue improving :

  • • User behaviour is constantly evolving, so be adaptable. Continuous tracking of user behaviour will help keep your strategies up to date. 
    • Encourage iterative analysis optimisation based on your findings. 

  

  The top cohort analysis tools for 2024

  In this section, we’ll go over the best cohort analysis tools for 2024, including their key features, cohort analysis dashboards, cost and pros and cons.

  1. Matomo

  

  Matomo is an open-source, GDPR-compliant web analytics solution that offers cohort analysis as a standard feature in Matomo Cloud and is available as a plugin for Matomo On-Premise. Pairing traditional web analytics with cohort analysis will help you gain even deeper insights into understanding user behaviour over time. 

  You can use the data you get from web analytics to identify patterns in user behaviour and target your marketing strategies to specific cohorts. 

  Key features

  • Matomo offers a cohorts table that lets you compare cohorts side-by-side, and it comes with a time series.
    • All core session and conversion metrics are also available in the Cohorts report.
  • Create custom segments based on demographics, geography, referral sources, acquisition date, device types or user behaviour. 
  • Matomo provides retention analysis so you can track how many users from a specific cohort return to your website and when. 
  • Flexibly analyse your cohorts with custom reports. Customise your reports by combining metrics and dimensions specific to different cohorts. 
  • Create cohorts based on events or interactions with your website. 
  • Intuitive, colour-coded data visualisation, so you can easily spot patterns.

  Pros

  • No setup is needed if you use the JavaScript tracker
  • You can fetch cohort without any limit
  • 100% accurate data, no AI or Machine Learning data filling, and without the use of data sampling

  Cons

  • Matomo On-Premise (self-hosted) is free, but advanced features come with additional charges
  • Servers and technical know-how are required for Matomo On-Premise. Alternatively, for those not ready for self-hosting, Matomo Cloud presents a more accessible option and starts at $19 per month.

  Price : 

  • Matomo Cloud : 21-day free trial, then starts at $19 per month (includes Cohorts).
  • Matomo On-Premise : Free to self-host ; Cohorts plugin : 30-day free trial, then $99 per year.

  2. Mixpanel

  

  Mixpanel is a product analytics tool designed to help teams better understand user behaviour. It is especially well-suited for analysing user behaviour on iOS and Android apps. It offers various cohort analytics features that can be used to identify patterns and engage your users. 

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Compare how different cohorts engage with your app with Mixpanel’s comparative analysis features.
  • Create interactive dashboards, charts and graphs to visualise data.
  • Mixpanel provides retention analysis tools to see how often users return to your product over time. 
  • Send targeted messages and notifications to specific cohorts to encourage user engagement, announce new features, etc. 
  • Track and analyse user behaviours within cohorts — understand how different types of users engage with your product.

  Pros

  • Easily export cohort analysis data for further analysis
  • Combined with Mixpanel reports, cohorts can be a powerful tool for improving your product

  Cons

  • With the free Mixpanel plan, you can’t save cohorts for future use
  • Enterprise-level pricing is expensive
  • Time-consuming cohort creation process

  Price : Free basic version. The growth version starts at £16/month.

  3. Amplitude

  

  Amplitude is another product analytics solution that can help businesses track user interactions across digital platforms. Amplitude offers a standard toolkit for in-depth cohort analysis.

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Conduct behavioural, time-based and retention analyses.
  • Create custom reports with custom data.
  • Segment cohorts further based on additional criteria and compare multiple cohorts side-by-side.

  Pros

  • Highly customisable and flexible
  • Quick and simple setup

  Cons

  • Steep learning curve — requires significant training 
  • Slow loading speed
  • High price point compared to other tools

  Price : Free basic version. Plus version starts at £40/month (billed annually).

  4. Kissmetrics

  

  Kissmetrics is a customer engagement automation platform that offers powerful analytics features. Kissmetrics provides behavioural analytics, segmentation and email campaign automation. 

  Key features

  • Create cohorts based on demographics, user behaviour, referral sources, events and specific time frames.
  • The user path tool provides path visualisation so you can identify common paths users take and spot abandonment points. 
  • Create and optimise conversion funnels.
  • Customise events, user properties, funnels, segments, cohorts and more.

  Pros

  • Powerful data visualisation options
  • Highly customisable

  Cons

  • Difficult to install
  • Not well-suited for small businesses
  • Limited integration with other tools

  Price : Starting at £21/month for 10k events (billed monthly).

  Improve your cohort analysis with Matomo

  When choosing a cohort analysis tool, consider factors such as the tool’s ease of integration with your existing systems, data accuracy, the flexibility it offers in defining cohorts, the comprehensiveness of reporting features, and its scalability to accommodate the growth of your data and analysis needs over time. Moreover, it’s essential to confirm GDPR compliance to uphold rigorous privacy standards. 

  If you’re ready to understand your user’s behaviour, take Matomo for a test drive. Paired with web analytics, this powerful combination can advance your marketing efforts. Start your 21-day free trial today — no credit card required.

• 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.