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• Lean Analytics in a Privacy-First Environment – Bootcamp with Timo Dechau

  29 novembre 2024, par Daniel Crough — Banking and Financial Services, GDPR, Marketing, Privacy, Videos, Featured Banking Content

  In a recent bootcamp, Timo Dechau walked attendees through his approach to data and measurement in privacy-focused analytics environments. He demonstrates how to shift from a chaotic, ‘track-it-all’ mentality to a focused method that prioritizes quality over quantity. This post will summarize some of his key privacy-first analytics ideas, but be sure to check out the on-demand video for more detail.

  Watch the bootcamp on demand

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  Unrestrained data collection leads to data bloat

  Marketing and the business world are experiencing a data problem. Analysts and business intelligence teams grapple with large amounts of data that aren’t always useful and are often incomplete. The idea that “more data is better” became a guiding principle in the early 2000s, encouraging companies to gather everything possible using all available data collection methods. This unrestrained pursuit often led to an unexpected problem : data bloat. Too much data, too little clarity. Digital marketers, analysts, and business leaders now try to navigate vast amounts of information that create more confusion than insight, especially when the data is incomplete due to privacy regulations.

  

  Cutting through the noise, focusing on what matters

  The “more data is better” mindset emerged when digital marketers were beginning to understand data’s potential. It seemed logical : more data should mean more opportunities to optimise, personalise, and drive results. But in practice, gathering every possible piece of data often leads to a cluttered, confusing pile of metrics that can mislead more than guide.

  This approach carries hidden costs. Excessive data collection burns resources, increases privacy concerns, and leaves teams unfocused. It’s easy to get lost trying to make sense of endless dashboards, metrics, and reports. More data doesn’t necessarily lead to better decisions ; it often just leads to more noise, hindering effective data management.

  Rethinking data management : From data overload to data mindfulness

  Data management has often prioritised comprehensive data gathering without considering the specific value of each data point. This approach has created more information, but not necessarily better insights.

  Data mindfulness is about taking a deliberate, focused approach to data collection and analysis. Instead of trying to collect everything, it emphasises gathering only what truly adds value. It’s about ensuring the data you collect serves a purpose and directly contributes to better insights and data-driven decision-making.

  Think of it like applying a “lean” methodology to data—trimming away the unnecessary and keeping only what is essential. Or consider embracing data minimalism to declutter your data warehouse, keeping only what truly sparks insight.

  

  Mindful data is ethical data

  Adopting a mindful approach to data can pay off in several ways :

  • Reduces overwhelm : When you reduce the clutter, you’re left with fewer, clearer metrics that lead to stronger decisions and actionable data insights.

  • Mitigates compliance risks : By collecting less, companies align better with privacy regulations and build trust with their customers. Privacy-first analytics and privacy-compliant analytics practices mean there’s no need for invasive tracking if it doesn’t add value—and customers will appreciate that.

  • Enhances data ethics : Focusing on the quality rather than the quantity of data collected ensures ethical data collection and management. Companies use data responsibly, respect user privacy, and minimise unnecessary data handling, strengthening customer relationships and brand integrity.

  • Improves data efficiency : Focused analytics means better use of resources. You’re spending less time managing meaningless metrics and more time working on meaningful insights. Many companies have found success by switching to a leaner, quality-first data approach, reporting sharper, more impactful results.

  Shifting towards simplicity and lean analytics

  If data mindfulness sounds appealing, here’s how you can get started :

  1. Ask the right questions. Before collecting any data, ask yourself : Why are we collecting this ? How will it drive value ? If you can’t answer these questions clearly, that data probably isn’t worth collecting. This is a key step in smart data management.

  2. Simplify metrics. Focus on the KPIs that truly matter for your business. Choose a handful of key metrics that reflect your goals rather than a sprawling list of nice-to-haves. Embracing data simplicity helps in targeting data collection effectively.

  3. Audit your current data. Review your existing data collection processes. Which metrics are you actively using to make decisions ? Eliminate any redundant or low-value metrics that create noise. Use ethical data management practices to ensure data efficiency and compliance. Understanding what is data management in this context is crucial.

  4. Implement lean analytics practices. Shift towards lean analytics by cutting down on unnecessary tracking. This can involve reducing reliance on multiple tracking scripts, simplifying your reporting, and setting up a streamlined dashboard focused on key outcomes. Embrace data reduction strategies to eliminate waste and boost effectiveness.

  Who should watch this bootcamp

  This bootcamp is perfect for data analysts, product managers, digital marketers and business leaders who are seeking a more streamlined approach to data measurement. If you’re interested in moving away from a chaotic “track-it-all” mentality and towards a focused, lean, and privacy-first analytics strategy, this workshop is for you.

  What you’ll discover

  • Practical steps : Learn actionable strategies to reduce data bloat and implement lean, privacy-first analytics in your organisation.

  • Real-life examples : Explore case studies of companies that have successfully adopted focused and privacy-first analytics.

  • Deep insights : Gain a deeper understanding of how to prioritise quality over quantity without sacrificing valuable insights.

  Watch the bootcamp on-demand

  For a comprehensive dive into these topics, watch the full workshop video or download the detailed transcript. Equip yourself with the knowledge and tools to transform your data management approach today.

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• What is Funnel Analysis ? A Complete Guide for Quick Results

  25 janvier 2024, par Erin

  Your funnel is leaking.

  You’re losing visitors.

  You’re losing conversions and sales.

  But you don’t know how it’s happening, where it’s happening, or what to do about it.

  The reason ? You aren’t properly analysing your funnels.

  If you want to improve conversions and grow your business, you need to understand how to properly assess your sales funnels to set yourself up for success.

  In this guide, we’ll show you what funnel analysis is, why it’s important, and what steps you need to take to leverage it to improve conversions.

  What is funnel analysis ?

  Every business uses sales funnels, whether they know it or not.

  But most people aren’t analysing them, costing them conversions.

  

  Funnel analysis is a marketing method to analyse the events leading to specific conversion points. 

  It aims to look at the entire journey of potential customers from the moment they first touch base with your website or business to the moment they click “buy.”

  It’s assessing what your audience is doing at every step of the journey.

  By assessing what actions are taking place at scale, you can see where you’re falling short in your sales funnel.

  You’ll see :

  1. Where prospects are falling off.
  2. Where people are converting well.

  By gaining this understanding, you’ll better understand the health of your website’s sales funnels and overall marketing strategy.

  With that knowledge, you can optimise your marketing strategy to patch those leaks, improve conversions and grow your business.

  Why funnel analysis is important

  Funnel analysis is critical because your funnel is your business.

  When you analyse your funnel, you’re analysing your business.

  You’re looking at what’s working and what’s not so you can grow revenue and profit margins.

  Funnel analysis lets you monitor user behaviour to show you the motivation and intention behind their decisions.

  Here are five reasons you need to incorporate funnel analysis into your workflow.

  

  1. Gives insights into your funnel problems

  The core purpose of funnel analysis is to look at what’s going on on your website.

  What are the most effective steps to conversion ?

  Where do users drop off in the conversion process ?

  And which pages contribute the most to conversion or drop-offs ?

  Funnel analysis helps you understand what’s going on with your site visitors. Plus, it helps you see what’s wrong with your funnel.

  If you aren’t sure what’s happening with your funnel, you won’t know what to improve to grow your revenue.

  2. Improves conversions

  When you know what’s going on with your funnel, you’ll know how to improve it.

  To improve your conversion funnel, you need to close the leaks. These are areas where website visitors are falling off.

  It’s the moment the conversion is lost.

  You need to use funnel analysis to give insight into these problem areas. Once you can see where the issue is, you can patch that leak and improve the percentage of visitors who convert.

  For example, if your conversion rate on your flagship product page has plateaued and you can’t figure out how to increase conversions, implementing a funnel analysis tactic like heatmaps will show you that visitors are spending time reading your product description. Still, they’re not spending much time near your call to action.

  

  This might tell you that you need to update your description copy or adjust your button (i.e. colour, size, copy). You can increase conversions by making those changes in your funnel analysis insights.

  3. Improves the customer experience

  Funnel analysis helps you see where visitors spend their time, what elements they interact with and where they fall off.

  One of the key benefits of analysing your funnel is you’ll be able to help improve the experience your visitors have on your website.

  For example, if you have informational videos on a specific web page to educate your visitors, you might use the Media Analytics feature in your web analytics solution to find out that they’re not spending much time watching them.

  This could lead you to believe that the content itself isn’t good or relevant to them.

  But, after implementing session recordings within your funnel analysis, you see people clicking a ton near the play button. This might tell you that they’re having trouble clicking the actual button on the video player due to poor UX.

  In this scenario, you could update the UX on your web page so the videos are easy to click and watch, no matter what device someone uses.

  With more video viewers, you can provide value to your visitors instead of leaving them frustrated trying to watch your videos.

  4. Grows revenue

  This is what you’re likely after : more revenue.

  More often than not, this means you need to focus on improving your conversion rate.

  Funnel analysis helps you find those areas where visitors are exiting so you can patch those leaks up and turn more visitors into customers.

  Let’s say you have a conversion rate of 1.7%.

  You get 50,000 visitors per month.

  Your average order is $82.

  Even if you increase your conversion rate by 10% (to 1.87%) through funnel analysis, here’s the monthly difference in revenue :

  Before : $69,700
  After : $76,670

  In one year, you’ll make nearly $80,000 in additional revenue from funnel analysis alone.

  Different types of funnel analysis

  There are a few different types of funnel analysis.

  How you define success in your funnel all comes down to one of these four pillars.

  Depending on your goals, business and industry, you may want to assess the different funnel analyses at different times.

  1. Pageview funnel analysis

  Pageview funnel analysis is about understanding how well your website content is performing. 

  It helps you enhance user experience, making visitors stay longer on your site. By identifying poor performing pages (pages with high exit rates), you can pinpoint areas that need optimisation for better engagement.

  2. Conversion funnel analysis

  Next up, we’re looking at conversion funnel analysis.

  This type of funnel analysis is crucial for marketers aiming to turn website visitors into action-takers. This involves tracking and optimising conversion goals, such as signing up for newsletters, downloading ebooks, submitting forms or signing up for free trials. 

  The primary goal of conversion funnel analysis is to boost your website’s overall conversion rates.

  3. E-commerce funnel analysis

  For businesses selling products online, e-commerce funnel analysis is essential. 

  It involves measuring whether your products are being purchased and finding drop-off points in the purchasing process. 

  By optimising the e-commerce funnel, you can enhance revenue and improve the overall efficiency of your sales process.

  How to conduct funnel analysis

  Now that you understand what funnel analysis is, why it’s important, and the different types of analysis, it’s time to show you how to do it yourself.

  To get started with funnel analysis, you need to have the right web analytics solution.

  Here are the most common funnel analysis tools and methods you can use :

  1. Funnel analytics

  If you want to choose a single tool to conduct funnel analysis, it’s an all-in-one web analytics tool, like Matomo.

  

  With Matomo’s Funnel Analytics, you can dive into your whole funnel and analyse each step (and each step’s conversion rate).

  

  For instance, if you look at the example above, you can see the proceed rate at each funnel step before the conversion page.

  This means you can improve each proceed rate, to drive more traffic to your conversion page in order to increase conversion rates.

  In the above snapshot from Matomo, it shows visitors starting on the job board overview page, moving on to view specific job listings. The goal is to convert these visitors into job applicants.

  However, a significant issue arises at the job view stage, where 95% of visitors don’t proceed to job application. To increase conversions, we need to first concentrate on improving the job view page.

  Try Matomo for Free

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

  Start for free

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  2. Heatmaps

  Heatmaps is a behaviour analytics tool that lets you see different visitor activities, including :

  • Mouse movement
  • How far down visitors scroll
  • Clicks

  You can see which elements were clicked on and which weren’t and how far people scroll down your page.

  

  A heatmap lets you see which parts of a page are getting the most attention and which parts go unnoticed by your users.

  For example, if, during your funnel analysis, you see that a lot of visitors are falling off after they land on the checkout page, then you might want to add a heatmap on your checkout page to see where and why people are exiting.

  3. Session recordings

  Want to see what individual users are doing and how they’re interacting with your site ?

  Then, you’ll want to check out session recordings.

  A session recording is a video playback of a visitor’s time on your website.

  

  It’s the most effective method to observe your visitors’ interactions with your site, eliminating uncertainty when identifying areas for funnel improvement.

  Session recordings instill confidence in your optimisation efforts by providing insights into why and where visitors may be dropping off in the funnel.

  4. A/B testing

  If you want to take the guesswork out of optimising your funnel and increasing your conversions, you need to start A/B testing.

  An A/B test is where you create two versions of a web page to determine which one converts better.

  

  For example, if your heatmaps and session recordings show that your users are dropping off near your call to action, it may be time to test a new version.

  You may find that by simply testing a different colour button, you may increase conversions by 20% or more.

  5. Form analytics

  Are you trying to get more leads to fill out forms on your site ?

  Well, Form Analytics can help you understand how your website visitors interact with your signup forms.

  You can view metrics such as starter rate, conversion rate, average hesitation time and average time spent.

  This information allows you to optimise your forms effectively, ultimately maximising your success.

  Let’s look at the performance of a form using Matomo’s Form Analytics feature below.

  

  In the Matomo example, our starter rate stands at a solid 60.1%, but there’s a significant drop to a submitter rate of 29.3%, resulting in a conversion rate of 16.3%.

  Looking closer, people are hesitating for about 16.2 seconds and taking nearly 1 minute 39 seconds on average to complete our form.

  This could indicate our form is confusing and requesting too much. Simplifying it could help increase sign-ups.

  See first-hand how Concrete CMS tripled their leads using Form Analytics in Matomo.

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• Banking Data Strategies – A Primer to Zero-party, First-party, Second-party and Third-party data

  25 octobre 2024, par Daniel Crough — Banking and Financial Services, Privacy

  Banks hold some of our most sensitive information. Every transaction, loan application, and account balance tells a story about their customers’ lives. Under GDPR and banking regulations, protecting this information isn’t optional – it’s essential.

  Yet banks also need to understand how customers use their services to serve them better. The solution lies in understanding different types of banking data and how to handle each responsibly. From direct customer interactions to market research, each data source serves a specific purpose and requires its own privacy controls.

  Before diving into how banks can use each type of data effectively, let’s look into the key differences between them :

  Data Type	What It Is	Banking Example	Legal Considerations
  First-party	Data from direct customer interactions with your services	Transaction records, service usage patterns	Different legal bases apply (contract, legal obligation, legitimate interests)
  Zero-party	Information customers actively provide	Stated preferences, financial goals	Requires specific legal basis despite being voluntary ; may involve profiling
  Second-party	Data shared through formal partnerships	Insurance history from partners	Must comply with PSD2 and specific data sharing regulations
  Third-party	Data from external providers	Market analysis, demographic data	Requires due diligence on sources and specific transparency measures

  What is first-party data ?

  

  First-party data reveals how customers actually use your banking services. When someone logs into online banking, withdraws money from an ATM, or speaks with customer service, they create valuable information about real banking habits.

  This direct interaction data proves more reliable than assumptions or market research because it shows genuine customer behaviour. Banks need specific legal grounds to process this information. Basic banking services fall under contractual necessity, while fraud detection is required by law. Marketing activities need explicit customer consent. The key is being transparent with customers about what information you process and why.

  Start by collecting only what you need for each specific purpose. Store information securely and give customers clear control through privacy settings. This approach builds trust while helping meet privacy requirements under the GDPR’s data minimisation principle.

  What is zero-party data ?

  

  Zero-party data emerges when customers actively share information about their financial goals and preferences. Unlike first-party data, which comes from observing customer behaviour, zero-party data comes through direct communication. Customers might share their retirement plans, communication preferences, or feedback about services.

  Interactive tools create natural opportunities for this exchange. A retirement calculator helps customers plan their future while revealing their financial goals. Budget planners offer immediate value through personalised advice. When customers see clear benefits, they’re more likely to share their preferences.

  However, voluntary sharing doesn’t mean unrestricted use. The ICO’s guidance on purpose limitation applies even to freely shared information. Tell customers exactly how you’ll use their data, document specific reasons for collecting each piece of information, and make it simple to update or remove personal data.

  Regular reviews help ensure you still need the information customers have shared. This aligns with both GDPR requirements and customer expectations about data management. By treating voluntary information with the same care as other customer data, banks build lasting trust.

  What is second-party data ?

  

  Second-party data comes from formal partnerships between banks and trusted companies. For example, a bank might work with an insurance provider to better understand shared customers’ financial needs.

  These partnerships need careful planning to protect customer privacy. The ICO’s Data Sharing Code provides clear guidelines : both organisations must agree on what data they’ll share, how they’ll protect it, and how long they’ll keep it before any sharing begins.

  Transparency builds trust in these arrangements. Tell customers about planned data sharing before it happens. Explain what information you’ll share and how it helps provide better services.

  Regular audits help ensure both partners maintain high privacy standards. Review shared data regularly to confirm it’s still necessary and properly protected. Be ready to adjust or end partnerships if privacy standards slip. Remember that your responsibility to protect customer data extends to information shared with partners.

  Successful partnerships balance improved service with diligent privacy protection. When done right, they help banks understand customer needs better while maintaining the trust that makes banking relationships work.

  What is third-party data ?

  

  Third-party data comes from external sources outside your bank and its partners. Market research firms, data analytics companies, and economic research organizations gather and sell this information to help banks understand broader market trends.

  This data helps fill knowledge gaps about the wider financial landscape. For example, third-party data might reveal shifts in consumer spending patterns across different age groups or regions. It can show how customers interact with different financial services or highlight emerging banking preferences in specific demographics.

  But third-party data needs careful evaluation before use. Since your bank didn’t collect this information directly, you must verify both its quality and compliance with privacy laws. Start by checking how providers collected their data and whether they had proper consent. Look for providers who clearly document their data sources and collection methods.

  Quality varies significantly among third-party data providers. Some key questions to consider before purchasing :

  • How recent is the data ?
  • How was it collected ?
  • What privacy protections are in place ?
  • How often is it updated ?
  • Which specific market segments does it cover ?

  Consider whether third-party data will truly add value beyond your existing information. Many banks find they can gain similar insights by analysing their first-party data more effectively. If you do use third-party data, document your reasons for using it and be transparent about your data sources.

  Creating your banking data strategy

  

  A clear data strategy helps your bank collect and use information effectively while protecting customer privacy. This matters most with first-party data – the information that comes directly from your customers’ banking activities.

  Start by understanding what data you already have. Many banks collect valuable information through everyday transactions, website visits, and customer service interactions. Review these existing data sources before adding new ones. Often, you already have the insights you need – they just need better organization.

  Map each type of data to a specific purpose. For example, transaction data might help detect fraud and improve service recommendations. Website analytics could reveal which banking features customers use most. Each data point should serve a clear business purpose while respecting customer privacy.

  Strong data quality standards support better decisions. Create processes to update customer information regularly and remove outdated records. Check data accuracy often and maintain consistent formats across your systems. These practices help ensure your insights reflect reality.

  Remember that strategy means choosing what not to do. You don’t need to collect every piece of data possible. Focus on information that helps you serve customers better while maintaining their privacy.

  Managing multiple data sources

  

  Banks work with many types of data – from direct customer interactions to market research. Each source serves a specific purpose, but combining them effectively requires careful planning and precise attention to regulations like GDPR and ePrivacy.

  First-party data forms your foundation. It shows how your customers actually use your services and what they need from their bank. This direct interaction data proves most valuable because it reflects real behaviour rather than assumptions. When customers check their balances, transfer money, or apply for loans, they show you exactly how they use banking services.

  Zero-party data adds context to these interactions. When customers share their financial goals or preferences directly, they help you understand the “why” behind their actions. This insight helps shape better services. For example, knowing a customer plans to buy a house helps you offer relevant savings tools or mortgage information at the right time.

  Second-party partnerships can fill specific knowledge gaps. Working with trusted partners might reveal how customers manage their broader financial lives. But only pursue partnerships when they offer clear value to customers. Always explain these relationships clearly and protect shared information carefully.

  Third-party data helps provide market context, but use it selectively. External market research can highlight broader trends or opportunities. However, this data often proves less reliable than information from direct customer interactions. Consider it a supplement to, not a replacement for, your own customer insights.

  Keep these principles in mind when combining data sources :

  • Prioritize direct customer interactions
  • Focus on information that improves services
  • Maintain consistent privacy standards across sources
  • Document where each insight comes from
  • Review regularly whether each source adds value
  • Work with privacy and data experts to ensure customer information is handled properly

  Enhance your web analytics strategy with Matomo

  

  The financial sector finds powerful and compliant web analytics increasingly valuable as it navigates data management and privacy regulations. Matomo provides a configurable privacy-centric solution that meets the requirements of banks and financial institutions.

  Matomo empowers your organisation to :

  • Collect accurate, GDPR-compliant web data
  • Integrate web analytics with your existing tools and platforms
  • Maintain full control over your analytics data
  • Gain insights without compromising user privacy

  Matomo is trusted by some of the world’s biggest banks and financial institutions. Try Matomo for free for 30 days to see how privacy-focused analytics can get you the insights you need while maintaining compliance and user trust.