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• A Guide to Ethical Web Analytics in 2024

  17 juin 2024, par Erin

  User data is more valuable and sought after than ever. 

  Ninety-four percent of respondents in Cisco’s Data Privacy Benchmark Study said their customers wouldn’t buy from them if their data weren’t protected, with 95% saying privacy was a business imperative. 

  Unfortunately, the data collection practices of most businesses are far from acceptable and often put their customers’ privacy at risk. 

  But it doesn’t have to be this way. You can ethically collect valuable and insightful customer data—you just need the right tools.

  In this article, we show you what an ethical web analytics solution can look like, why Google Analytics is a problem and how you can collect data without risking your customers’ privacy.

  What is ethical web analytics ?

  Ethical web analytics put user privacy first. These platforms prioritise privacy and transparency by only collecting necessary data, avoiding implicit user identification and openly communicating data practices and tracking methods. 

  Ethical tools adhere to data protection laws like GDPR as standard (meaning businesses using these tools never have to worry about fines or disruptions). In other words, ethical web analytics refrain from exploiting and profiting from user behaviour and data. 

  Unfortunately, most traditional data solutions collect as much data as possible without users’ knowledge or consent.

  Why does digital privacy matter ?

  Digital privacy matters because companies have repeatedly proven they will collect and use data for financial gain. It also presents security risks. Unsecured user data can lead to identity theft, cyberattacks and harassment. 

  Big tech companies like Google and Meta are often to blame for all this. These companies collect millions of user data points — like age, gender, income, political beliefs and location. Worse still, they share this information with interested third parties.

  After public outrage over data breaches and other privacy scandals, consumers are taking active steps to disallow tracking where possible. IAPP’s Privacy and Consumer Trust Report finds that 68% of consumers across 19 countries are somewhat or very concerned about their digital privacy. 

  There’s no way around it : companies of all sizes and shapes need to consider how they handle and protect customers’ private information. 

  Why should you use an ethical web analytics tool ?

  When companies use ethical web analytics tools they can build customer trust, boost their brand reputation, improve data security practices and future proof their website tracking solution. 

  Boost brand reputation

  The fallout from a data privacy scandal can be severe. 

  Just look at what happened to Facebook during the Cambridge Analytica data scandal. The eponymous consulting firm harvested 50 million Facebook profiles and used that information to target people with political messages. Due to the instant public backlash, Facebook’s stock tanked, and use of the “delete Facebook” hashtag increased by 423% in the following days.

  That’s because consumers care about data privacy, according to Deloitte’s Connected Consumer Study :

  • Almost 90 percent agree they should be able to view and delete data companies collect 
  • 77 percent want the government to introduce stricter regulations
  • Half feel the benefits they get from online services outweigh data privacy concerns.

  If you can prove you buck the trend by collecting data using ethical methods, it can boost your brand’s reputation. 

  Build trust with customers

  At the same time, collecting data in an ethical way can help you build customer trust. You’ll go a long way to changing consumer perceptions, too. Almost half of consumers don’t like sharing data, and 57% believe companies sell their data. 

  This additional trust should generate a positive ROI for your business. According to Cisco’s Data Privacy Benchmark Study, the average company gains $180 for every $100 they invest in privacy. 

  Improve data security

  According to IBM’s Cost of a Data Breach report, the average cost of a data breach is nearly $4.5 million. This kind of scenario becomes much less likely when you use an ethical tool that collects less data overall and anonymises the data you do collect. 

  Futureproof your web analytics solution

  The obvious risk of not complying with privacy regulations is a fine — which can be up to €20 million, or 4% of worldwide annual revenue in the case of GDPR.

  It’s not just fines and penalties you risk if you fail to comply with privacy regulations like GDPR. For some companies, especially larger ones, the biggest risk of non-compliance with privacy regulations is the potential sudden need to abandon Google Analytics and switch to an ethical alternative.

  If Data Protection Authorities ban Google Analytics again, as has happened in Austria, France, and other countries, businesses will be forced to drop everything and make an immediate transition to a compliant web analytics solution.

  When an organisation’s entire marketing operation relies on data, migrating to a new solution can be incredibly painful and time-consuming. So, the sooner you switch to an ethical tool, the less of a headache the process will be. 

  The problem with Google Analytics

  Google Analytics (GA) is the most popular analytics platform in the world, but it’s a world away from being an ethical tool. Here’s why :

  You don’t have data ownership

  Google Analytics is attractive to businesses of all sizes because of its price. Everyone loves getting something for free, but there’s still a cost — your and your customers’ data.

  That’s because Google combines the data you collect with information from the millions of other websites it tracks to inform its advertising efforts. It may also use your data to train large language models like Gemini. 

  It has a rocky history with GDPR laws

  Google and EU regulators haven’t always got along. For example, the German Data Protection Authority is investigating 200,000 pending cases against websites using GA. The platform has also been banned and added back to the EU-US Data Privacy Framework several times over the past few years. 

  You can use GA to collect data about EU customers right now, but there’s no guarantee you’ll be able to do so in the future. 

  It requires a specific setup to remain compliant

  While you can currently use GA in a GDPR-compliant way — owing to its inclusion in the EU-US Data Privacy Framework — you have to set it up in a very specific way. That’s because the platform’s compliance depends on what data you collect, how you inform users and the level of consent you acquire. You’ll still need to include an extensive privacy policy on your website. 

  What does ethical web analytics look like ?

  An ethical web analytics solution should put user privacy first, ensure compliance with regulations like GDPR, give businesses 100% control of the data they collect and be completely transparent about data collection and storage practices. 

  

  100% data ownership

  You don’t fully control customer data when you use Google Analytics. The search giant uses your data for its own advertising purposes and may also use it to train large language models like Gemini. 

  When you choose an ethical web analytics alternative like Matomo, you can ensure you completely own your data.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Respects user privacy

  It’s possible to track and measure user behaviour without collecting personally identifiable information (PII). Just look at the ethical web analytics tools we’ve reviewed below. 

  These platforms respect user privacy and conform to strict privacy regulations like GDPR, CCPA and HIPAA by incorporating some or all of the following features :

  • Opt-out mechanisms to let users refuse tracking
  • IP addresses anonymisation and other data anonymisation techniques
  • DoNotTrack options
  • Shorter expiration dates for tracking cookies

  In Matomo’s case, it’s all of the above. Better still, you can check our privacy credentials yourself. Our software’s source code is open source on GitHub and accessible to anyone at any time. 

  Compliant with government regulations

  While Google’s history with data regulations is tumultuous, an ethical web analytics platform should follow even the strictest privacy laws, including GDPR, HIPAA, CCPA, LGPD and PECR.

  But why stop there ? Matomo has been approved by the French Data Protection Authority (CNIL) as one of the few web analytics tools that French sites can use to collect data without tracking consent. So you don’t need an annoying consent banner popping up on your website anymore. 

  Try Matomo for Free

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

  Start for free

  No credit card required

  Complete transparency 

  Ethical web analytics tools will be upfront about their data collection practices, whether that’s in the U.S., EU, or on your own private servers. Look for a solution that refrains from collecting personally identifiable information, shows where data is stored, and lets you alter tracking methods to increase privacy even further. 

  Some solutions, like Matomo, will increase transparency further by providing open source software. Anyone can find our source code on GitHub to see exactly how our platform tracks and stores user data. This means our code is regularly examined and reviewed by a community of developers, making it more secure, too.

  Ethical web analytics solutions

  There are several options for an ethical web analytics tool. We list three of the best providers below. 

  Matomo

  Matomo is an open source web analytics tool and privacy-focused Google Analytics alternative used by over one million sites globally. 

  

  Matomo is fully compliant with prominent global privacy regulations like GDPR, CCPA and HIPAA, meaning you never have to worry about collecting consent when tracking user behaviour. 

  The data you collect is completely accurate since Matomo doesn’t use data sampling and is 100% yours. We don’t share data with third parties but can prove it. Our product source code is publicly available on GitHub. As a community-led project, you can download and install it yourself for free. 

  With Matomo, you get a full range of web analytics capabilities and behavioural analytics. That includes your standard metrics (think visitors, traffic sources, bounce rates, etc.), advanced features to analyse user behaviour like A/B Testing, Form Analytics, Heatmaps and Session Recordings. 

  Migrating to Matomo is easy. You can even import historical Google Analytics data to generate meaningful insights immediately. 

  Try Matomo for Free

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

  Start for free

  No credit card required

  Fathom

  Fathom Analytics is a lightweight privacy-focused analytics solution that launched in 2018. It aims to be an easy-to-use Google Analytics alternative that doesn’t compromise privacy. 

  

  Like Matomo, Fathom complies with all major privacy regulations, including GDPR and CCPA. It also provides 100% accurate, unsampled reports and doesn’t share your data with third parties. 

  While Fathom provides fairly comprehensive analytics reports, it doesn’t have some of Matomo’s more advanced features. That includes e-commerce tracking, heatmaps, session recordings, and more. 

  Plausible

  Plausible Analytics is another open source Google Analytics alternative that was built and hosted in the EU. 

  

  Launched in 2019, Plausible is a newer player in the privacy-focused analytics market. Still, its ultra-lightweight script makes it an attractive option for organisations that prioritise speed over everything else. 

  Like Matomo and Fathom, Plausible is GDPR and CCPA-compliant by design. Nor is there any cap on the amount of data you collect or any debate over whether the data is accurate (Plausible doesn’t use data sampling) or who owns the data (you do). 

  Matomo makes it easy to migrate to an ethical web analytics alternative

  There’s no reason to put your users’ privacy at risk, especially when there are so many benefits to choosing an ethical tool. Whether you want to avoid fines, build trust with your customers, or simply know you’re doing the right thing, choosing a privacy-focused, ethical solution like Matomo is taking a massive step in the right direction. 

  Making the switch is easy, too. Matomo is one of the few options that lets you import historical Google Analytics data, so starting from scratch is unnecessary. 

  Get started today by trying Matomo for free for 21-days. No credit card required. 

  Try Matomo for Free

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• FFmpeg unable to save file on Android : Output file #0 does not contain any stream

  16 mai 2022, par John M.

  I'm having difficulty when saving a RTSP stream into internal storage using ffmpeg-kit -i rtsp://192.168.100.60:554/stream1 -c:v copy -c:a copy -t 5 /data/user/0/com.arthenica.ffmpegkit.test/files/test.mp4. The command is called using FFmpegKit.executeAsync(command, ...). There're issues with the circular_buffer_size option, but I read that the warnings could be ignored. The command runs fine on desktop using ffmpeg. Please find below the log.

  


  Any advice would be appreciated.

  


  ffmpeg started on 2022-05-16 at 15:27:54
Report written to "/data/user/0/com.arthenica.ffmpegkit.test/cache/ffreport.txt"
Log level: 48
Command line:
ffmpeg-kit -i rtsp://192.168.100.60:554/stream1 -c:v copy -c:a copy -t 5 /data/user/0/com.arthenica.ffmpegkit.test/files/test.mp4
ffmpeg version v4.5-dev-3393-g30322ebe3c Copyright (c) 2000-2021 the FFmpeg developers
  built with Android (7155654, based on r399163b1) clang version 11.0.5 (https://android.googlesource.com/toolchain/llvm-project 87f1315dfbea7c137aa2e6d362dbb457e388158d)
  configuration: --cross-prefix=i686-linux-android- --sysroot=/files/android-sdk/ndk/22.1.7171670/toolchains/llvm/prebuilt/linux-x86_64/sysroot --prefix=/storage/light/projects/ffmpeg-kit/prebuilt/android-x86-lts/ffmpeg --pkg-config=/usr/bin/pkg-config --enable-version3 --arch=i686 --cpu=i686 --target-os=android --disable-neon --disable-asm --disable-inline-asm --ar=i686-linux-android-ar --cc=i686-linux-android16-clang --cxx=i686-linux-android16-clang++ --ranlib=i686-linux-android-ranlib --strip=i686-linux-android-strip --nm=i686-linux-android-nm --extra-libs='-L/storage/light/projects/ffmpeg-kit/prebuilt/android-x86-lts/cpu-features/lib -lndk_compat' --disable-autodetect --enable-cross-compile --enable-pic --enable-jni --enable-optimizations --enable-swscale --disable-static --enable-shared --enable-pthreads --enable-v4l2-m2m --disable-outdev=fbdev --disable-indev=fbdev --enable-small --disable-xmm-clobber-test --disable-debug --enable-lto --disable-neon-clobber-test --disable-programs --disable-postproc --  libavutil      57. 13.100 / 57. 13.100
  libavcodec     59. 15.102 / 59. 15.102
  libavformat    59. 10.100 / 59. 10.100
  libavdevice    59.  1.100 / 59.  1.100
  libavfilter     8. 21.100 /  8. 21.100
  libswscale      6.  1.102 /  6.  1.102
  libswresample   4.  0.100 /  4.  0.100
Splitting the commandline.
Reading option '-i' ... matched as input url with argument 'rtsp://192.168.100.60:554/stream1'.
Reading option '-c:v' ... matched as option 'c' (codec name) with argument 'copy'.
Reading option '-c:a' ... matched as option 'c' (codec name) with argument 'copy'.
Reading option '-t' ... matched as option 't' (record or transcode "duration" seconds of audio/video) with argument '5'.
Reading option '/data/user/0/com.arthenica.ffmpegkit.test/files/test.mp4' ... matched as output url.
Finished splitting the commandline.
Parsing a group of options: global .
Successfully parsed a group of options.
Parsing a group of options: input url rtsp://192.168.100.60:554/stream1.
Successfully parsed a group of options.
Opening an input file: rtsp://192.168.100.60:554/stream1.
[tcp @ 0xa21d3a80] No default whitelist set
[tcp @ 0xa21d3a80] Original list of addresses:
[tcp @ 0xa21d3a80] Address 192.168.100.60 port 554
[tcp @ 0xa21d3a80] Interleaved list of addresses:
[tcp @ 0xa21d3a80] Address 192.168.100.60 port 554
[tcp @ 0xa21d3a80] Starting connection attempt to 192.168.100.60 port 554
[tcp @ 0xa21d3a80] Successfully connected to 192.168.100.60 port 554
[rtsp @ 0xb25b5a00] SDP:
v=0
o=- 14665860 31787219 1 IN IP4 192.168.100.60
s=Session streamed by "MERCURY RTSP Server"
t=0 0
a=smart_encoder:virtualIFrame=1
m=video 0 RTP/AVP 96
c=IN IP4 0.0.0.0
b=AS:4096
a=range:npt=0-
a=control:track1
a=rtpmap:96 H264/90000
a=fmtp:96 packetization-mode=1; profile-level-id=4D4020; sprop-parameter-sets=Z01AIJWgFAF34maIAAADAAgAAAMBlCA=,aO48gA==

Failed to parse interval end specification ''
[rtsp @ 0xb25b5a00] video codec set to: h264
[rtsp @ 0xb25b5a00] RTP Packetization Mode: 1
[rtsp @ 0xb25b5a00] RTP Profile IDC: 4d Profile IOP: 40 Level: 20
[rtsp @ 0xb25b5a00] Extradata set to 0xb25abdf0 (size: 35)
[rtp @ 0xb25abe60] No default whitelist set
[udp @ 0xb25abed0] No default whitelist set
[udp @ 0xb25abed0] 'circular_buffer_size' option was set but it is not supported on this build (pthread support is required)
[udp @ 0xb25abed0] end receive buffer size reported is 524288
[udp @ 0xb25abf40] No default whitelist set
[udp @ 0xb25abf40] 'circular_buffer_size' option was set but it is not supported on this build (pthread support is required)
[udp @ 0xb25abf40] end receive buffer size reported is 524288
[rtsp @ 0xb25b5a00] setting jitter buffer size to 500
[rtsp @ 0xb25b5a00] hello state=0
Failed to parse interval end specification ''
[h264 @ 0x907e5b00] nal_unit_type: 7(SPS), nal_ref_idc: 3
[h264 @ 0x907e5b00] nal_unit_type: 8(PPS), nal_ref_idc: 3
[h264 @ 0x907e5b00] nal_unit_type: 7(SPS), nal_ref_idc: 3
[h264 @ 0x907e5b00] nal_unit_type: 8(PPS), nal_ref_idc: 3
[rtsp @ 0xb25b5a00] Could not find codec parameters for stream 0 (Video: h264, none): unspecified size
Consider increasing the value for the 'analyzeduration' (0) and 'probesize' (5000000) options
Input #0, rtsp, from 'rtsp://192.168.100.60:554/stream1':
  Metadata:
    title           : Session streamed by "MERCURY RTSP Server"
  Duration: N/A, start: 0.000000, bitrate: N/A
  Stream #0:0, 0, 1/90000: Video: h264, none, 90k tbr, 90k tbn
Successfully opened the file.
Parsing a group of options: output url /data/user/0/com.arthenica.ffmpegkit.test/files/test.mp4.
Applying option c:v (codec name) with argument copy.
Applying option c:a (codec name) with argument copy.
Applying option t (record or transcode "duration" seconds of audio/video) with argument 5.
Successfully parsed a group of options.
Opening an output file: /data/user/0/com.arthenica.ffmpegkit.test/files/test.mp4.
Output #0, mp4, to '/data/user/0/com.arthenica.ffmpegkit.test/files/test.mp4':
Output file #0 does not contain any stream


  


• A Guide to Bank Customer Segmentation

  18 juillet 2024, par Erin

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

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

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

  What is customer segmentation ?

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

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

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

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

  

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

  

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

  Try Matomo for Free

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

  Start for free

  No credit card required

  Why is customer segmentation important for banks ?

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

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

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

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

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

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

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

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

  Prevent switching with segment analysis

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

  

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

  Keep customers active with segment targeting

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

  Improve the quality of services and products

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

  Personalise customer experiences by layering segments

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

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

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

  Maximise customer lifetime value

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

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

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

  Balancing bank customer segmentation with privacy and marketing regulations

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

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

  Regulators are clamping down on finance

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

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

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

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

  Customer data in the hands of agencies and big tech

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

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

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

  Banks need a privacy-centric alternative to Google Analytics

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

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

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

  

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

  Try Matomo for Free

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

  Start for free

  No credit card required

  Bank customer segmentation examples

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

  Visit-based segmentation

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

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

  

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

  Demographic segmentation

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

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

  

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

  Interaction-based segmentation

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

  

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

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

  Purchase-based segmentation

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

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

  Get reliable website analytics for your bank customer segmentation needs

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

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

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

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