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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.

• Developing A Shader-Based Video Codec

  22 juin 2013, par Multimedia Mike — Outlandish Brainstorms

  Early last month, this thing called ORBX.js was in the news. It ostensibly has something to do with streaming video and codec technology, which naturally catches my interest. The hype was kicked off by Mozilla honcho Brendan Eich when he posted an article asserting that HD video decoding could be entirely performed in JavaScript. We’ve seen this kind of thing before using Broadway– an H.264 decoder implemented entirely in JS. But that exposes some very obvious limitations (notably CPU usage).

  But this new video codec promises 1080p HD playback directly in JavaScript which is a lofty claim. How could it possibly do this ? I got the impression that performance was achieved using WebGL, an extension which allows JavaScript access to accelerated 3D graphics hardware. Browsing through the conversations surrounding the ORBX.js announcement, I found this confirmation from Eich himself :

  You’re right that WebGL does heavy lifting.

  As of this writing, ORBX.js remains some kind of private tech demo. If there were a public demo available, it would necessarily be easy to reverse engineer the downloadable JavaScript decoder.

  But the announcement was enough to make me wonder how it could be possible to create a video codec which effectively leverages 3D hardware.

  Prior Art
  In theorizing about this, it continually occurs to me that I can’t possibly be the first person to attempt to do this (or the ORBX.js people, for that matter). In googling on the matter, I found various forums and Q&A posts where people asked if it were possible to, e.g., accelerate JPEG decoding and presentation using 3D hardware, with no answers. I also found a blog post which describes a plan to use 3D hardware to accelerate VP8 video decoding. It was a project done under the banner of Google’s Summer of Code in 2011, though I’m not sure which open source group mentored the effort. The project did not end up producing the shader-based VP8 codec originally chartered but mentions that “The ‘client side’ of the VP8 VDPAU implementation is working and is currently being reviewed by the libvdpau maintainers.” I’m not sure what that means. Perhaps it includes modifications to the public API that supports VP8, but is waiting for the underlying hardware to actually implement VP8 decoding blocks in hardware.

  What’s So Hard About This ?
  Video decoding is a computationally intensive task. GPUs are known to be really awesome at chewing through computationally intensive tasks. So why aren’t GPUs a natural fit for decoding video codecs ?

  Generally, it boils down to parallelism, or lack of opportunities thereof. GPUs are really good at doing the exact same operations over lots of data at once. The problem is that decoding compressed video usually requires multiple phases that cannot be parallelized, and the individual phases often cannot be parallelized. In strictly mathematical terms, a compressed data stream will need to be decoded by applying a function f(x) over each data element, x₀ .. x_n. However, the function relies on having applied the function to the previous data element, i.e. :

  f(xn) = f(f(xn-1))
  

  What happens when you try to parallelize such an algorithm ? Temporal rifts in the space/time continuum, if you’re in a Star Trek episode. If you’re in the real world, you’ll get incorrect, unusuable data as the parallel computation is seeded with a bunch of invalid data at multiple points (which is illustrated in some of the pictures in the aforementioned blog post about accelerated VP8).

  Example : JPEG
  Let’s take a very general look at the various stages involved in decoding the ubiquitous JPEG format :

  
  
  

  What are the opportunities to parallelize these various phases ?
  

  • Huffman decoding (run length decoding and zig-zag reordering is assumed to be rolled into this phase) : not many opportunities for parallelizing the various Huffman formats out there, including this one. Decoding most Huffman streams is necessarily a sequential operation. I once hypothesized that it would be possible to engineer a codec to achieve some parallelism during the entropy decoding phase, and later found that On2′s VP8 codec employs the scheme. However, such a scheme is unlikely to break down to such a fine level that WebGL would require.
  • Reverse DC prediction : JPEG — and many other codecs — doesn’t store full DC coefficients. It stores differences in successive DC coefficients. Reversing this process can’t be parallelized. See the discussion in the previous section.
  • Dequantize coefficients : This could be very parallelized. It should be noted that software decoders often don’t dequantize all coefficients. Many coefficients are 0 and it’s a waste of a multiplication operation to dequantize. Thus, this phase is sometimes rolled into the Huffman decoding phase.
  • Invert discrete cosine transform : This seems like it could be highly parallelizable. I will be exploring this further in this post.
  • Convert YUV -> RGB for final display : This is a well-established use case for 3D acceleration.

  Crash Course in 3D Shaders and Humility
  So I wanted to see if I could accelerate some parts of JPEG decoding using something called shaders. I made an effort to understand 3D programming and its associated math throughout the 1990s but 3D technology left me behind a very long time ago while I got mixed up in this multimedia stuff. So I plowed through a few books concerning WebGL (thanks to my new Safari Books Online subscription). After I learned enough about WebGL/JS to be dangerous and just enough about shader programming to be absolutely lethal, I set out to try my hand at optimizing IDCT using shaders.

  Here’s my extremely high level (and probably hopelessly naive) view of the modern GPU shader programming model :

  
  
  

  The WebGL program written in JavaScript drives the show. It sends a set of vertices into the WebGL system and each vertex is processed through a vertex shader. Then, each pixel that falls within a set of vertices is sent through a fragment shader to compute the final pixel attributes (R, G, B, and alpha value). Another consideration is textures : This is data that the program uploads to GPU memory which can be accessed programmatically by the shaders).

  These shaders (vertex and fragment) are key to the GPU’s programmability. How are they programmed ? Using a special C-like shading language. Thought I : “C-like language ? I know C ! I should be able to master this in short order !” So I charged forward with my assumptions and proceeded to get smacked down repeatedly by the overall programming paradigm. I came to recognize this as a variation of the scientific method : Develop a hypothesis– in my case, a mental model of how the system works ; develop an experiment (short program) to prove or disprove the model ; realize something fundamental that I was overlooking ; formulate new hypothesis and repeat.

  First Approach : Vertex Workhorse
  My first pitch goes like this :

  • Upload DCT coefficients to GPU memory in the form of textures
  • Program a vertex mesh that encapsulates 16×16 macroblocks
  • Distribute the IDCT effort among multiple vertex shaders
  • Pass transformed Y, U, and V blocks to fragment shader which will convert the samples to RGB

  So the idea is that decoding of 16×16 macroblocks is parallelized. A macroblock embodies 6 blocks :

  
  
  

  It would be nice to process one of these 6 blocks in each vertex. But that means drawing a square with 6 vertices. How do you do that ? I eventually realized that drawing a square with 6 vertices is the recommended method for drawing a square on 3D hardware. Using 2 triangles, each with 3 vertices (0, 1, 2 ; 3, 4, 5) :

  
  
  

  A vertex shader knows which (x, y) coordinates it has been assigned, so it could figure out which sections of coefficients it needs to access within the textures. But how would a vertex shader know which of the 6 blocks it should process ? Solution : Misappropriate the vertex’s z coordinate. It’s not used for anything else in this case.

  So I set all of that up. Then I hit a new roadblock : How to get the reconstructed Y, U, and V samples transported to the fragment shader ? I have found that communicating between shaders is quite difficult. Texture memory ? WebGL doesn’t allow shaders to write back to texture memory ; shaders can only read it. The standard way to communicate data from a vertex shader to a fragment shader is to declare variables as “varying”. Up until this point, I knew about varying variables but there was something I didn’t quite understand about them and it nagged at me : If 3 different executions of a vertex shader set 3 different values to a varying variable, what value is passed to the fragment shader ?

  It turns out that the varying variable varies, which means that the GPU passes interpolated values to each fragment shader invocation. This completely destroys this idea.

  Second Idea : Vertex Workhorse, Take 2
  The revised pitch is to work around the interpolation issue by just having each vertex shader invocation performs all 6 block transforms. That seems like a lot of redundant. However, I figured out that I can draw a square with only 4 vertices by arranging them in an ‘N’ pattern and asking WebGL to draw a TRIANGLE_STRIP instead of TRIANGLES. Now it’s only doing the 4x the extra work, and not 6x. GPUs are supposed to be great at this type of work, so it shouldn’t matter, right ?

  I wired up an experiment and then ran into a new problem : While I was able to transform a block (or at least pretend to), and load up a varying array (that wouldn’t vary since all vertex shaders wrote the same values) to transmit to the fragment shader, the fragment shader can’t access specific values within the varying block. To clarify, a WebGL shader can use a constant value — or a value that can be evaluated as a constant at compile time — to index into arrays ; a WebGL shader can not compute an index into an array. Per my reading, this is a WebGL security consideration and the limitation may not be present in other OpenGL(-ES) implementations.

  Not Giving Up Yet : Choking The Fragment Shader
  You might want to be sitting down for this pitch :

  • Vertex shader only interpolates texture coordinates to transmit to fragment shader
  • Fragment shader performs IDCT for a single Y sample, U sample, and V sample
  • Fragment shader converts YUV -> RGB

  Seems straightforward enough. However, that step concerning IDCT for Y, U, and V entails a gargantuan number of operations. When computing the IDCT for an entire block of samples, it’s possible to leverage a lot of redundancy in the math which equates to far fewer overall operations. If you absolutely have to compute each sample individually, for an 8×8 block, that requires 64 multiplication/accumulation (MAC) operations per sample. For 3 color planes, and including a few extra multiplications involved in the RGB conversion, that tallies up to about 200 MACs per pixel. Then there’s the fact that this approach means a 4x redundant operations on the color planes.

  It’s crazy, but I just want to see if it can be done. My approach is to pre-compute a pile of IDCT constants in the JavaScript and transmit them to the fragment shader via uniform variables. For a first order optimization, the IDCT constants are formatted as 4-element vectors. This allows computing 16 dot products rather than 64 individual multiplication/addition operations. Ideally, GPU hardware executes the dot products faster (and there is also the possibility of lining these calculations up as matrices).

  I can report that I actually got a sample correctly transformed using this approach. Just one sample, through. Then I ran into some new problems :

  Problem #1 : Computing sample #1 vs. sample #0 requires a different table of 64 IDCT constants. Okay, so create a long table of 64 * 64 IDCT constants. However, this suffers from the same problem as seen in the previous approach : I can’t dynamically compute the index into this array. What’s the alternative ? Maintain 64 separate named arrays and implement 64 branches, when branching of any kind is ill-advised in shader programming to begin with ? I started to go down this path until I ran into…

  Problem #2 : Shaders can only be so large. 64 * 64 floats (4 bytes each) requires 16 kbytes of data and this well exceeds the amount of shader storage that I can assume is allowed. That brings this path of exploration to a screeching halt.

  Further Brainstorming
  I suppose I could forgo pre-computing the constants and directly compute the IDCT for each sample which would entail lots more multiplications as well as 128 cosine calculations per sample (384 considering all 3 color planes). I’m a little stuck with the transform idea right now. Maybe there are some other transforms I could try.

  Another idea would be vector quantization. What little ORBX.js literature is available indicates that there is a method to allow real-time streaming but that it requires GPU assistance to yield enough horsepower to make it feasible. When I think of such severe asymmetry between compression and decompression, my mind drifts towards VQ algorithms. As I come to understand the benefits and limitations of GPU acceleration, I think I can envision a way that something similar to SVQ1, with its copious, hierarchical vector tables stored as textures, could be implemented using shaders.

  So far, this all pertains to intra-coded video frames. What about opportunities for inter-coded frames ? The only approach that I can envision here is to use WebGL’s readPixels() function to fetch the rasterized frame out of the GPU, and then upload it again as a new texture which a new frame processing pipeline could reference. Whether this idea is plausible would require some profiling.

  Using interframes in such a manner seems to imply that the entire codec would need to operate in RGB space and not YUV.

  Conclusions
  The people behind ORBX.js have apparently figured out a way to create a shader-based video codec. I have yet to even begin to reason out a plausible approach. However, I’m glad I did this exercise since I have finally broken through my ignorance regarding modern GPU shader programming. It’s nice to have a topic like multimedia that allows me a jumping-off point to explore other areas.

• Top 4 CRO Tools to Boost Your Conversion Rates in 2024

  31 octobre 2023, par Erin

  Are you tired of watching potential customers leave your website without converting ? You’ve spent countless hours creating an engaging website, but those high bounce rates keep haunting you.

  The good news ? The solution lies in the transformative power of Conversion Rate Optimisation (CRO) tools. In this guide, we’ll dive deep into the world of CRO tools. We will equip you with strategies to turn those bounces into conversions.

  Why are conversion rate optimisation tools so crucial ?

  CRO tools can be assets in digital marketing, playing a pivotal role in enhancing online businesses’ performance. CRO tools empower businesses to improve website conversion rates by analysing user behaviour. You can then leverage this user data to optimise web elements.

  Improving website conversion rates is paramount because it increases revenue and customer satisfaction. A study by VentureBeat revealed an average return on investment (ROI) of 223% thanks to CRO tools.

  173 marketers out of the surveyed group reported returns exceeding 1,000%. Both of these data points highlight the impact CRO tools can have.

  

  Coupled with CRO tools, certain testing tools and web analytics tools play a crucial role. They offer insight into user behaviour patterns, enabling businesses to choose effective strategies. By understanding what resonates with users, these tools help inform data-driven decisions. This allows businesses to refine online strategies and enhance the customer experience.

  CRO tools enhance user experiences and ensure business sustainability. Integrating these tools is crucial for staying ahead. CRO and web analytics work together to optimise digital presence. 

  Real-world examples of CRO tools in action

  In this section, we’ll explore real case studies showcasing CRO tools in action. See how businesses enhance conversion rates, user experiences, and online performance. These studies reveal the practical impact of data-driven decisions and user-focused strategies.

  

  Case study : How Matomo’s Form Analytics helped Concrete CMS 3x leads

  Concrete CMS, is a content management system provider that helps users build and manage websites. They used Matomo’s Form Analytics to uncover that users were getting stuck at the address input stage of the onboarding process. Using these insights to make adjustments to their onboarding form, Concrete CMS was able to achieve 3 times the amount of leads in just a few days.

  Read the full Concrete CMS case study.

  Best analytics tools for enhancing conversion rate optimisation in 2023

  Jump to the comparison table to see an overview of each tool.

  1. Matomo

  

  Matomo stands out as an all-encompassing tool that seamlessly combines traditional web analytics features (like pageviews and bounce rates) with advanced behavioural analytics capabilities, providing a full spectrum of insights for effective CRO.

  Key features

  • Heatmaps and Session Recordings :
    These features empower businesses to see their websites through the eyes of their visitors. By visually mapping user engagement and observing individual sessions, businesses can make informed decisions, enhance user experience and ultimately increase conversions. These tools are invaluable assets for businesses aiming to create user-friendly websites.

  • Form Analytics :
    Matomo’s Form Analytics offers comprehensive tracking of user interactions within forms. This includes covering input fields, dropdowns, buttons and submissions. Businesses can create custom conversion funnels and pinpoint form abandonment reasons. 

  • Users Flow :
    Matomo’s Users Flow feature tracks visitor paths, drop-offs and successful routes, helping businesses optimise their websites. This insight informs decisions, enhances user experience, and boosts conversion rates.

  • Surveys plugin :
    The Matomo Surveys plugin allows businesses to gather direct feedback from users. This feature enhances understanding by capturing user opinions, adding another layer to the analytical depth Matomo offers.

  • A/B testing :
    The platform allows you to conduct A/B tests to compare different versions of web pages. This helps determine which performs better in conversions. By conducting experiments and analysing the results within Matomo, businesses can iteratively refine their content and design elements.

  • Funnels :
    Matomo’s Funnels feature empower businesses to visualise, analyse and optimise their conversion paths. By identifying drop-off points, tailoring user experiences and conducting A/B tests within the funnel, businesses can make data-driven decisions that significantly boost conversions and enhance the overall user journey on their websites.

  Pros

  • Starting at $19 per month, Matomo is an affordable CRO solution.
  • Matomo guarantees accurate data, eliminating the need to fill gaps with artificial intelligence (AI) or machine learning. 
  • Matomo’s open-source framework ensures enhanced security, privacy, customisation, community support and long-term reliability. 

  Cons

  • The On-Premise (self-hosted) version is free, with additional charges for advanced features.
  • Managing Matomo On-Premise requires servers and technical know-how.

  Try Matomo for Free

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

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  2. Google Analytics

  

  Google Analytics provides businesses and website owners valuable insights into their online audience. It tracks website traffic, user interactions and analyses conversion data to enhance the user experience.

  While Google Analytics may not provide the extensive CRO-specific features found in other tools on this list, it can still serve as a valuable resource for basic analysis and optimisation of conversion rates.

  Key features

  • Comprehensive Data Tracking :
    Google Analytics meticulously tracks website traffic, user behaviour and conversion rates. These insights form the foundation for CRO efforts. Businesses can identify patterns, user bottlenecks and high-performing areas.

  • Real-Time Reporting :
    Access to real-time data is invaluable for CRO efforts. Monitor current website activity, user interactions, and campaign performance as they unfold. This immediate feedback empowers businesses to make instant adjustments, optimising web elements and content for maximum conversions.

  • User flow analysis
    Visualise and understand how visitors navigate through your website. It provides insights into the paths users take as they move from one page to another, helping you identify the most common routes and potential drop-off points in the user journey.

  • Event-based tracking :
    GA4’s event-based reporting offers greater flexibility and accuracy in data collection. By tracking various interactions, including video views and checkout processes, businesses can gather more precise insights into user behaviour. 

  • Funnels :
    GA4 offers multistep funnels, path analysis, custom metrics that integrate with audience segments. These user behaviour insights help businesses to tailor their websites, marketing campaigns and user experiences.

  Pros

  • Flexible audience management across products, regions or brands allow businesses to analyse data from multiple source properties. 
  • Google Analytics integrates with other Google services and third-party platforms. This enables a comprehensive view of online activities.
  • Free to use, although enterprises may need to switch to the paid version to accommodate higher data volumes.

  Cons

  • Google Analytics raises privacy concerns, primarily due to its tracking capabilities and the extensive data it collects.
  • Limitations imposed by thresholding can significantly hinder efforts to enhance user experience and boost conversions effectively.
  • Property and sampling limits exist. This creates problems when you’re dealing with extensive datasets or high-traffic websites. 
  • The interface is difficult to navigate and configure, resulting in a steep learning curve.

  3. Contentsquare

  

  Contentsquare is a web analytics and CRO platform. It stands out for its in-depth behavioural analytics. Contentsquare offers detailed data on how users interact with websites and mobile applications.

  Key features

  • Heatmaps and Session Replays :
    Users can visualise website interactions through heatmaps, highlighting popular areas and drop-offs. Session replay features enable the playback of user sessions. These provide in-depth insights into individual user experiences.

  • Conversion Funnel Analysis :
    Contentsquare tracks users through conversion funnels, identifying where users drop off during conversion. This helps in optimising the user journey and increasing conversion rates.

  • Segmentation and Personalisation :
    Businesses can segment their audience based on various criteria. Segments help create personalised experiences, tailoring content and offers to specific user groups.

  • Integration Capabilities :
    Contentsquare integrates with various third-party tools and platforms, enhancing its functionality and allowing businesses to leverage their existing tech stack.

  Pros

  • Comprehensive support and resources.
  • User-friendly interface.
  • Personalisation capabilities.

  Cons

  • High price point.
  • Steep learning curve.

  4. Hotjar

  

  Hotjar is a robust tool designed to unravel user behaviour intricacies. With its array of features including visual heatmaps, session recordings and surveys, it goes beyond just identifying popular areas and drop-offs.

  Hotjar provides direct feedback and offers an intuitive interface, enabling seamless experience optimisation.

  Key features

  • Heatmaps :
    Hotjar provides visual heatmaps that display user interactions on your website. Heatmaps show where users click, scroll, and how far they read. This feature helps identify popular areas and points of abandonment.

  • Session Recordings :
    Hotjar allows you to record user sessions and watch real interactions on your site. This insight is invaluable for understanding user behaviour and identifying usability issues.

  • Surveys and Feedback :
    Hotjar offers on-site surveys and feedback forms that can get triggered based on user behaviour. These tools help collect qualitative data from real users, providing valuable insights.

  • Recruitment Tool :
    Hotjar’s recruitment tool lets you recruit participants from your website for user testing. This feature streamlines the process of finding participants for usability studies.

  • Funnel and Form Analysis :
    Hotjar enables the tracking of user journeys through funnels. It provides insights into where users drop off during the conversion process. It also offers form analysis to optimise form completion rates.

  • User Polls :
    You can create customisable polls to engage with visitors. Gather specific feedback on your website, products, or services.

  Pros

  • Starting at $32 per month, Hotjar is a cost-effective solution for most businesses. 
  • Hotjar provides a user-friendly interface that is easy for the majority of users to pick up quickly.

  Cons

  • Does not provide traditional web analytics and requires combining with another tool, potentially creating a less streamlined and cohesive user experience, which can complicate conversion rate optimization efforts.
  • Hotjar’s limited integrations can hinder its ability to seamlessly work with other essential tools and platforms, potentially further complicating CRO.

  Comparison Table

  Please note : We aim to keep this table accurate and up to date. However, if you see any inaccuracies or outdated information, please email us at marketing@matomo.org

  To make comparing these tools even easier, we’ve put together a table for you to compare features and price points :

  

  Conclusion

  CRO tools and web analytics are essential for online success. Businesses thrive by investing wisely, understanding user behaviour and using targeted strategies. The key : generate traffic and convert it into leads and customers. The right tools and strategies lead to remarkable conversions and online success. Each click, each interaction, becomes an opportunity to create an engaging user journey. This careful orchestration of data and insight separates thriving businesses from the rest.

  Are you ready to embark on a journey toward improved conversions and enhanced user experiences ? Matomo offers analytics solutions meticulously designed to complement your CRO strategy. Take the next step in your CRO journey. Start your 21-day free trial today—no credit card required.

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