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• vsrc_movie : rename video movie specific callbacks, prefix them with "movie"

  18 août 2011, par Stefano Sabatini

  vsrc_movie : rename video movie specific callbacks, prefix them with "movie"

• 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.

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  Why is customer segmentation important for banks ?

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

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

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

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

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

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

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

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

  Prevent switching with segment analysis

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

  

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

  Keep customers active with segment targeting

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

  Improve the quality of services and products

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

  Personalise customer experiences by layering segments

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

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

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

  Maximise customer lifetime value

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

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

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

  Balancing bank customer segmentation with privacy and marketing regulations

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

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

  Regulators are clamping down on finance

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

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

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

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

  Customer data in the hands of agencies and big tech

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

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

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

  Banks need a privacy-centric alternative to Google Analytics

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

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

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

  

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

  Try Matomo for Free

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

  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.

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

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

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• Google Analytics Privacy Issues : Is It Really That Bad ?

  2 juin 2022, par Erin

  If you find yourself asking : “What’s the deal with Google Analytics privacy ?”, you probably have some second thoughts. 

  Your hunch is right. Google Analytics (GA) is a popular web analytics tool, but it’s far from being perfect when it comes to respecting users’ privacy. 

  This post helps you understand tremendous Google Analytics privacy concerns users, consumers and regulators expressed over the years.

  In this blog, we’ll cover :

  What Does Google Analytics Collect About Users ? 

  To understand Google Analytics privacy issues, you need to know how Google treats web users’ data. 

  By default, Google Analytics collects the following information : 

  • Session statistics — duration, page(s) viewed, etc. 
  • Referring website details — a link you came through or keyword used. 
  • Approximate geolocation — country, city. 
  • Browser and device information — mobile vs desktop, OS usage, etc. 

  Google obtains web analytics data about users via two means : an on-site Google Analytics tracking code and cookies.

  A cookie is a unique identifier (ID) assigned to each user visiting a web property. Each cookie stores two data items : unique user ID and website name. 

  With the help of cookies, web analytics solutions can recognise returning visitors and track their actions across the website(s).

  

  • First party cookies are generated by one website and collect user behaviour data from said website only. 
  • Third-party cookies are generated by a third-party website object (for example, an ad) and can track user behaviour data across multiple websites. 

  As it’s easy to imagine, third-party cookies are a goldmine for companies selling online ads. Essentially, they allow ad platforms to continue watching how the user navigates the web after clicking a certain link. 

  Yet, people have little clue as to which data they are sharing and how it is being used. Also, user consent to tracking across websites is only marginally guaranteed by existing Google Analytics controls. 

  Why Third-Party Cookie Data Collection By GA Is Problematic 

  Cookies can transmit personally identifiable information (PII) such as name, log in details, IP address, saved payment method and so on. Some of these details can end up with advertisers without consumers’ direct knowledge or consent.

  Regulatory frameworks such as General Data Protection Regulation (GDPR) in Europe and California Consumer Privacy Act (CCPA) emerged as a response to uncontrolled user behaviour tracking.

  Under regulatory pressure, Big Tech companies had to adapt their data collection process.

  Apple was the first to implement by-default third-party blocking in the Safari browser. Then added a tracking consent mechanism for iPhone users starting from iOS 15.2 and later. 

  Google, too, said it would drop third-party cookie usage after The European Commission and UK’s Competition and Markets Authority (CMA) launched antitrust investigations into its activity. 

  To shake off the data watchdogs, Google released a Privacy Sandbox — a set of progressive tech, operational and compliance changes for ensuring greater consumer privacy. 

  Google’s biggest promise : deprecate third-party cookies usage for all web and mobile products. 

  Originally, Google promised to drop third-party cookies by 2022, but that didn’t happen. Instead, Google delayed cookie tracking depreciation for Chrome until the second half of 2023. 

  Why did they push back on this despite hefty fines from regulators ?

  Because online ads make Google a lot of money.

  In 2021, Alphabet Inc (parent company of Google), made $256.7 billion in revenue, of which $209.49 billion came from selling advertising. 

  Lax Google Analytics privacy enforcement — and its wide usage by website owners — help Google make those billions from collecting and selling user data. 

  How Google Uses Collected Google Analytics Data for Advertising 

  Over 28 million websites (or roughly 85% of the Internet) have Google Analytics tracking codes installed. 

  Even if one day we get a Google Analytics version without cookies, it still won’t address all the privacy concerns regulators and consumers have. 

  Over the years, Google has accumulated an extensive collection of user data. The company’s engineers used it to build state-of-the-art deep learning models, now employed to build advanced user profiles. 

  Deep learning is the process of training a machine to recognise data patterns. Then this “knowledge” is used to produce highly-accurate predictive insights. The more data you have for model training — the better its future accuracy will be. 

  Google has amassed huge deposits of data from its collection of products — GA, YouTube, Gmail, Google Docs and Google Maps among others. Now they are using this data to build a third-party cookies-less alternative mechanism for modelling people’s preferences, habits, lifestyles, etc. 

  Their latest model is called Google Topics. 

  This comes only after Google’s failed attempt to replace cookie-based training with Federated Learning of Cohorts (FLoC) model. But the solution wasn’t offering enough user transparency and user controls among other issues.

  

  Google Topics promises to limit the granularity of data advertisers get about users. 

  But it’s still a web user surveillance method. With Google Topics, the company will continue collecting user data via Chrome (and likely other Google products) — and share it with advertisers. 

  Because as we said before : Google is in the business of profiting off consumers’ data. 

  Two Major Ways Google Takes Advantage of Customer Data

  Every bit of data Google collects across its ecosystem of products can be used in two ways :

  • For ad targeting and personalisation 
  • To improve Google’s products 

  The latter also helps the former. 

  Advanced Ad Personalisation and Targeting

  GA provides the company with ample data on users’ 

  • Recent and frequent searches 
  • Location history
  • Visited websites
  • Used apps 
  • Videos and ads viewed 
  • Personal data like age or gender 

  The company’s privacy policy explicitly states that :
  

  

  Google also admits to using collected data to “measure the effectiveness of advertising” and “personalise content and ads you see on Google.” 

  But there are no further elaborations on how exactly customers’ data is used — and what you can do to prevent it from being shared with third parties. 

  In some cases, Google also “forgets” to inform users about its in-product tracking.

  Journalists from CNBC and The New York Times independently concluded that Google monitors users’ Gmail activity. In particular, the company scans your inbox for recent purchases, trips, flights and bills notifications. 

  While Google says that this information isn’t sold to advertisers (directly), they still may use the “saved information about your orders in other Google services”. 

  Once again, this means you have little control or knowledge of subsequent data usage. 

  Improving Product Usability 

  Google has many “arms” to collect different data points — from user’s search history to frequently-travelled physical routes. 

  They also reserve the right to use these insights for improving existing products. 

  Here’s what it means : by combining different types of data points obtained from various products, Google can pierce a detailed picture of a person’s life. Even if such user profile data is anonymised, it is still alarmingly accurate. 

  Douglas Schmidt, a computer science researcher at Vanderbilt University, well summarised the matter : 

  “[Google’s] business model is to collect as much data about you as possible and cross-correlate it so they can try to link your online persona with your offline persona. This tracking is just absolutely essential to their business. ‘Surveillance capitalism’ is a perfect phrase for it.”

  Google Data Collection Obsession Is Backed Into Its Business Model 

  OK, but Google offers some privacy controls to users ? Yes. Google only sees and uses the information you voluntarily enter or permit them to access. 

  But as the Washington Post correspondent points out :

  “[Big Tech] companies get to set all the rules, as long as they run those rules by consumers in convoluted terms of service that even those capable of decoding the legalistic language rarely bother to read. Other mechanisms for notice and consent, such as opt-outs and opt-ins, create similar problems. Control for the consumer is mostly an illusion.”

  Google openly claims to be “one of many ad networks that personalise ads based on your activity online”. 

  The wrinkle is that they have more data than all other advertising networks (arguably combined). This helps Google sell high-precision targeting and contextually personalised ads for billions of dollars annually.

  Given that Google has stakes in so many products — it’s really hard to de-Google your business and minimise tracking and data collection from the company.
  

  They are also creating a monopoly on data collection and ownership. This fact makes regulators concerned. The 2021 antitrust lawsuit from the European Commission says : 

  “The formal investigation will notably examine whether Google is distorting competition by restricting access by third parties to user data for advertising purposes on websites and apps while reserving such data for its own use.”

  In other words : By using consumer data to its unfair advantage, Google allegedly shuts off competition.

  But that’s not the only matter worrying regulators and consumers alike. Over the years, Google also received numerous other lawsuits for breaching people’s privacy, over and over again. 

  Here’s a timeline : 

  • 2019 : UK citizens issued a class action suit against Google for imposing cookies to override users’ privacy settings in the Safari browser. 
  • 2020 : US citizens pushed for a $5 billion class-action suit for tracking their activity through browsers set in “private” mode.
  • 2022 : Another class-action lawsuit in the US for deceptive privacy controls and unconsented location data tracking by Google mobile apps. 
  • 2022 : Google reached a $100 million class-action settlement for breaching Illinois biometrics privacy laws in Google Photos. 

  Separately, Google has a very complex history with GDPR compliance. 

  How Google Analytics Contributes to the Web Privacy Problem 

  Google Analytics is the key puzzle piece that supports Google’s data-driven business model. 

  If Google was to release a privacy-focused Google Analytics alternative, it’d lose access to valuable web users’ data and a big portion of digital ad revenues. 

  Remember : Google collects more data than it shares with web analytics users and advertisers. But they keep a lot of it for personal usage — and keep looking for ways to share this intel with advertisers (in a way that keeps regulators off their tail).

  For Google Analytics to become truly ethical and privacy-focused, Google would need to change their entire revenue model — which is something they are unlikely to do.

  Where does this leave Google Analytics users ? 

  In a slippery territory. By proxy, companies using GA are complicit with Google’s shady data collection and usage practice. They become part of the problem.

  In fact, Google Analytics usage opens a business to two types of risks : 

  • Reputational. 77% of global consumers say that transparency around how data is collected and used is important to them when interacting with different brands. That’s why data breaches and data misuse by brands lead to major public outrages on social media and boycotts in some cases. 
  • Legal. EU regulators are on a continuous crusade against Google Analytics 4 (GA4) as it is in breach of GDPR. French and Austrian watchdogs ruled the “service” illegal. Since Google Analytics is not GDPR compliant, it opens any business using it to lawsuits (which is already happening).

  But there’s a way out.

  Choose a Privacy-Friendly Google Analytics Alternative 

  Google Analytics is a popular web analytics service, but not the only one available. You have alternatives such as Matomo. 

  Our guiding principle is : respecting privacy.

  Unlike Google Analytics, we leave data ownership 100% in users’ hands. Matomo lets you implement privacy-centred controls for user data collection.

  Plus, you can self-host Matomo On-Premise or choose Matomo Cloud with data securely stored in the EU and in compliance with GDPR.

  The best part ? You can try our ethical alternative to Google Analytics for free. No credit card required ! Start your free 21-day trial now. 

  21 day free trial. No credit card required.

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  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  

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