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• Overcoming Fintech and Finserv’s Biggest Data Analytics Challenges

  13 septembre 2024, par Daniel Crough — Banking and Financial Services, Marketing, Security

  Data powers innovation in financial technology (fintech), from personalized banking services to advanced fraud detection systems. Industry leaders recognize the value of strong security measures and customer privacy. A recent survey highlights this focus, with 72% of finance Chief Risk Officers identifying cybersecurity as their primary concern.

  Beyond cybersecurity, fintech and financial services (finserv) companies are bogged down with massive amounts of data spread throughout disconnected systems. Between this, a complex regulatory landscape and an increasingly tech-savvy and sceptical consumer base, fintech and finserv companies have a lot on their plates.

  How can marketing teams get the information they need while staying focused on compliance and providing customer value ? 

  This article will examine strategies to address common challenges in the finserv and fintech industries. We’ll focus on using appropriate tools, following effective data management practices, and learning from traditional banks’ approaches to similar issues.

  What are the biggest fintech data analytics challenges, and how do they intersect with traditional banking ?

  Recent years have been tough for the fintech industry, especially after the pandemic. This period has brought new hurdles in data analysis and made existing ones more complex. As the market stabilises, both fintech and finserve companies must tackle these evolving data issues.

  Let’s examine some of the most significant data analytics challenges facing the fintech industry, starting with an issue that’s prevalent across the financial sector :

  1. Battling data silos

  In a recent survey by InterSystems, 54% of financial institution leaders said data silos are their biggest barrier to innovation, while 62% said removing silos is their priority data strategy for the next year.

  

  Data silos segregate data repositories across departments, products and other divisions. This is a major issue in traditional banking and something fintech companies should avoid inheriting at all costs.

  Siloed data makes it harder for decision-makers to view business performance with 360-degree clarity. It’s also expensive to maintain and operationalise and can evolve into privacy and data compliance issues if left unchecked.

  To avoid or remove data silos, develop a data governance framework and centralise your data repositories. Next, simplify your analytics stack into as few integrated tools as possible because complex tech stacks are one of the leading causes of data silos.

  Use an analytics system like Matomo that incorporates web analytics, marketing attribution and CRO testing into one toolkit.

  

  Matomo’s support plans help you implement a data system to meet the unique needs of your business and avoid issues like data silos. We also offer data warehouse exporting as a feature to bring all of your web analytics, customer data, support data, etc., into one centralised location.

  Try Matomo for free today, or contact our sales team to discuss support plans.

  2. Compliance with laws and regulations

  A survey by Alloy reveals that 93% of fintech companies find it difficult to meet compliance regulations. The cost of staying compliant tops their list of worries (23%), outranking even the financial hit from fraud (21%) – and this in a year marked by cyber threats.

  

  Data privacy laws are constantly changing, and the landscape varies across global regions, making adherence even more challenging for fintechs and traditional banks operating in multiple markets. 

  In the US market, companies grapple with regulations at both federal and state levels. Here are some of the state-level legislation coming into effect for 2024-2026 :

  • Oregon – Oregon Consumer Privacy Act (July 1, 2024)
  • Florida – Florida Digital Bill of Rights (July 1, 2024)
  • Texas – Texas Data Privacy and Security Act (July 1, 2024)
  • Montana – Montana Consumer Data Privacy Act (October 1, 2024)
  • Delaware – Delaware Personal Privacy Act (January 1, 2025)
  • Iowa – Iowa Consumer Data Protection Act (January 1, 2025)
  • New Jersey – SB 332 (January 15, 2025)
  • Tennessee – Tennessee Information Protection Act (July 1, 2025)
  • Indiana – Indiana Consumer Data Protection Act ( January 1, 2026)

  Other countries are also ramping up regional regulations. For instance, Canada has Quebec’s Act Respecting the Protection of Personal Information in the Private Sector and British Columbia’s Personal Information Protection Act (BC PIPA).

  Ignorance of country- or region-specific laws will not stop companies from suffering the consequences of violating them.

  The only answer is to invest in adherence and manage business growth accordingly. Ultimately, compliance is more affordable than non-compliance – not only in terms of the potential fines but also the potential risks to reputation, consumer trust and customer loyalty.

  This is an expensive lesson that fintech and traditional financial companies have had to learn together. GDPR regulators hit CaixaBank S.A, one of Spain’s largest banks, with multiple multi-million Euro fines, and Klarna Bank AB, a popular Swedish fintech company, for €720,000.

  To avoid similar fates, companies should :

  1. Build solid data systems
  2. Hire compliance experts
  3. Train their teams thoroughly
  4. Choose data analytics tools carefully

  Remember, even popular tools like Google Analytics aren’t automatically safe. Find out how Matomo helps you gather useful insights while sticking to rules like GDPR.

  3. Protecting against data security threats

  Cyber threats are increasing in volume and sophistication, with the financial sector becoming the most breached in 2023.

  

  The cybersecurity risks will only worsen, with WEF estimating annual cybercrime expenses of up to USD $10.5 trillion globally by 2025, up from USD $3 trillion in 2015.

  While technology brings new security solutions, it also amplifies existing risks and creates new ones. A 2024 McKinsey report warns that the risk of data breaches will continue to increase as the financial industry increasingly relies on third-party data tools and cloud computing services unless they simultaneously improve their security posture.

  The reality is that adopting a third-party data system without taking the proper precautions means adopting its security vulnerabilities.

  In 2023, the MOVEit data breach affected companies worldwide, including financial institutions using its file transfer system. One hack created a global data crisis, potentially affecting the customer data of every company using this one software product.

  The McKinsey report emphasises choosing tools wisely. Why ? Because when customer data is compromised, it’s your company that takes the heat, not the tool provider. As the report states :

  “Companies need reliable, insightful metrics and reporting (such as security compliance, risk metrics and vulnerability tracking) to prove to regulators the health of their security capabilities and to manage those capabilities.”

  Don’t put user or customer data in the hands of companies you can’t trust. Work with providers that care about security as much as you do. With Matomo, you own all of your data, ensuring it’s never used for unknown purposes.

  

  4. Protecting users’ privacy

  With security threats increasing, fintech companies and traditional banks must prioritise user privacy protection. Users are also increasingly aware of privacy threats and ready to walk away from companies that lose their trust.

  Cisco’s 2023 Data Privacy Benchmark Study reveals some eye-opening statistics :

  • 94% of companies said their customers wouldn’t buy from them if their data wasn’t protected, and 
  • 95% see privacy as a business necessity, not just a legal requirement.

  Modern financial companies must balance data collection and management with increasing privacy demands. This may sound contradictory for companies reliant on dated practices like third-party cookies, but they need to learn to thrive in a cookieless web as customers move to banks and service providers that have strong data ethics.

  This privacy protection journey starts with implementing web analytics ethically from the very first session.

  

  The most important elements of ethically-sound web analytics in fintech are :

  1. 100% data ownership : Make sure your data isn’t used in other ways by the tools that collect it.
  2. Respecting user privacy : Only collect the data you absolutely need to do your job and avoid personally identifiable information.
  3. Regulatory compliance : Stick with solutions built for compliance to stay out of legal trouble.
  4. Data transparency : Know how your tools use your data and let your customers know how you use it.

  Read our guide to ethical web analytics for more information.

  5. Comparing customer trust across industries 

  While fintech companies are making waves in the financial world, they’re still playing catch-up when it comes to earning customer trust. According to RFI Global, fintech has a consumer trust score of 5.8/10 in 2024, while traditional banking scores 7.6/10.

  

  This trust gap isn’t just about perception – it’s rooted in real issues :

  • Security breaches are making headlines more often.
  • Privacy regulations like GDPR are making consumers more aware of their rights.
  • Some fintech companies are struggling to handle fraud effectively.

  According to the UK’s Payment Systems Regulator, digital banking brands Monzo and Starling had some of the highest fraudulent activity rates in 2022. Yet, Monzo only reimbursed 6% of customers who reported suspicious transactions, compared to 70% for NatWest and 91% for Nationwide.

  So, what can fintech firms do to close this trust gap ?

  • Start with privacy-centric analytics from day one. This shows customers you value their privacy from the get-go.
  • Build and maintain a long-term reputation free of data leaks and privacy issues. One major breach can undo years of trust-building.
  • Learn from traditional banks when it comes to handling issues like fraudulent transactions, identity theft, and data breaches. Prompt, customer-friendly resolutions go a long way.
  • Remember : cutting-edge financial technology doesn’t make up for poor customer care. If your digital bank won’t refund customers who’ve fallen victim to credit card fraud, they’ll likely switch to a traditional bank that will.

  The fintech sector has made strides in innovation, but there’s still work to do in establishing trustworthiness. By focusing on robust security, transparent practices, and excellent customer service, fintech companies can bridge the trust gap and compete more effectively with traditional banks.

  6. Collecting quality data

  Adhering to data privacy regulations, protecting user data and implementing ethical analytics raises another challenge. How can companies do all of these things and still collect reliable, quality data ?

  Google’s answer is using predictive models, but this replaces real data with calculations and guesswork. The worst part is that Google Analytics doesn’t even let you use all of the data you collect in the first place. Instead, it uses something called data sampling once you pass certain thresholds.

  In practice, this means that Google Analytics uses a limited set of your data to calculate reports. We’ve discussed GA4 data sampling at length before, but there are two key problems for companies here :

  1. A sample size that’s too small won’t give you a full representation of your data.
  2. The more visitors that come to your site, the less accurate your reports will become.

  For high-growth companies, data sampling simply can’t keep up. Financial marketers widely recognise the shortcomings of big tech analytics providers. In fact, 80% of them say they’re concerned about data bias from major providers like Google and Meta affecting valuable insights.

  This is precisely why CRO:NYX Digital approached us after discovering Google Analytics wasn’t providing accurate campaign data. We set up an analytics system to suit the company’s needs and tested it alongside Google Analytics for multiple campaigns. In one instance, Google Analytics failed to register 6,837 users in a single day, approximately 9.8% of the total tracked by Matomo.

  In another instance, Google Analytics only tracked 600 visitors over 24 hours, while Matomo recorded nearly 71,000 visitors – an 11,700% discrepancy.

  

  Financial companies need a more reliable, privacy-centric alternative to Google Analytics that captures quality data without putting users at potential risk. This is why we built Matomo and why our customers love having total control and visibility of their data.

  Unlock the full power of fintech data analytics with Matomo

  Fintech companies face many data-related challenges, so compliant web analytics shouldn’t be one of them. 

  With Matomo, you get :

  • An all-in-one solution that handles traditional web analytics, behavioural analytics and more with strong integrations to minimise the likelihood of data siloing
  • Full compliance with GDPR, CCPA, PIPL and more
  • Complete ownership of your data to minimise cybersecurity risks caused by negligent third parties
  • An abundance of ways to protect customer privacy, like IP address anonymisation and respect for DoNotTrack settings
  • The ability to import data from Google Analytics and distance yourself from big tech
  • High-quality data that doesn’t rely on sampling
  • A tool built with financial analytics in mind

  Don’t let big tech companies limit the power of your data with sketchy privacy policies and counterintuitive systems like data sampling. 

  Start your Matomo free trial or request a demo to unlock the full power of fintech data analytics without putting your customers’ personal information at unnecessary risk.

• OCPA, FDBR and TDPSA – What you need to know about the US’s new privacy laws

  22 juillet 2024, par Daniel Crough

  On July 1, 2024, new privacy laws took effect in Florida, Oregon, and Texas. People in these states now have more control over their personal data, signaling a shift in privacy policy in the United States. Here’s what you need to know about these laws and how privacy-focused analytics can help your business stay compliant.

  Consumer rights are front and centre across all three laws

  The Florida Digital Bill of Rights (FDBR), Oregon Consumer Privacy Act (OCPA), and Texas Data Privacy and Security Act (TDPSA) grant consumers similar rights.

  Access : Consumers can access their personal data held by businesses.

  Correction : Consumers can correct inaccurate data.

  Deletion : Consumers may request data deletion.

  Opt-Out : Consumers can opt-out of the sale of their personal data and targeted advertising.

  Oregon Consumer Privacy Act (OCPA)

  The Oregon Consumer Privacy Act (OCPA), signed into law on June 23, 2023, and effective as of July 1, 2024, grants Oregonians new rights regarding their personal data and imposes obligations on businesses. Starting July 1, 2025, authorities will enforce provisions that require data protection assessments, and businesses must recognize universal opt-out mechanisms by January 1, 2026. In Oregon, the OCPA applies to business that :

  • Either conduct business in Oregon or offer products and services to Oregon residents

  • Control or process the personal data of 100,000 consumers or more, or

  • Control or process the data of 25,000 or more consumers while receiving over 25% of their gross revenues from selling personal data.

  Exemptions include public bodies like state and local governments, financial institutions, and insurers that operate under specific financial regulations. The law also excludes protected health information covered by HIPAA and other specific federal regulations.

  Business obligations

  Data Protection Assessments : Businesses must conduct data protection assessments for high-risk processing activities, such as those involving sensitive data or targeting children.

  Consent for Sensitive Data : Businesses must secure explicit consent before collecting, processing, or selling sensitive personal data, such as racial or ethnic origin, religious beliefs, health information, biometric data, and geolocation.

  Universal Opt-out : Starting January 1, 2025, businesses must acknowledge universal opt-out mechanisms, like the Global Privacy Control, that allow consumers to opt out of data collection and processing activities.

  Enforcement

  The Oregon Attorney General can issue fines up to $7,500 per violation. There is no private right of action.

  Unique characteristics of the OCPA

  The OCPA differs from other state privacy laws by requiring affirmative opt-in consent for processing sensitive and children’s data, and by including nonprofit organisations under its scope. It also requires global browser opt-out mechanisms starting in 2026.

  Florida Digital Bill of Rights (FDBR)

  The Florida Digital Bill of Rights (FDBR) became law on June 6, 2023, and it came into effect on July 1, 2024. This law targets businesses with substantial operations or revenues tied to digital activities and seeks to protect the personal data of Florida residents by granting them greater control over their information and imposing stricter obligations on businesses. It applies to entities that :

  • Conduct business in Florida or provide products or services targeting Florida residents,

  • Have annual global gross revenues exceeding $1 billion,

  • Receive 50% or more of their revenues from digital advertising or operate significant digital platforms such as app stores or smart speakers with virtual assistants.

  Exemptions include governmental entities, nonprofits, financial institutions covered by the Gramm-Leach-Bliley Act, and entities covered by HIPAA.

  Business obligations

  Data Security Measures : Companies are required to implement reasonable data security measures to protect personal data from unauthorised access and breaches.

  Handling Sensitive Data : Explicit consent is required for processing sensitive data, which includes information like racial or ethnic origin, religious beliefs, and biometric data.

  Non-Discrimination : Entities must ensure they do not discriminate against consumers who exercise their privacy rights.

  Data Minimisation : Businesses must collect only necessary data.

  Vendor Management : Businesses must ensure that their processors and vendors also comply with the FDBR, regarding the secure handling and processing of personal data.

  Enforcement

  The Florida Attorney General can impose fines of up to $50,000 per violation, with higher penalties for intentional breaches.

  Unique characteristics of the FDBR

  Unlike broader privacy laws such as the California Consumer Privacy Act (CCPA), which apply to a wider range of businesses based on lower revenue thresholds and the volume of data processed, the FDBR distinguishes itself by targeting large-scale businesses with substantial revenues from digital advertising. The FDBR also emphasises specific consumer rights related to modern digital interactions, reflecting the evolving landscape of online privacy concerns.

  Texas Data Privacy and Security Act (TDPSA)

  The Texas Data Privacy and Security Act (TDPSA), signed into law on June 16, 2023, and effective as of July 1, 2024, enhances data protection for Texas residents. The TDPSA applies to entities that :

  • Conduct business in Texas or offer products or services to Texas residents.

  • Engage in processing or selling personal data.

  • Do not fall under the classification of small businesses according to the U.S. Small Business Administration’s criteria, which usually involve employee numbers or average annual receipts. 

  The law excludes state agencies, political subdivisions, financial institutions compliant with the Gramm-Leach-Bliley Act, and entities compliant with HIPAA.

  Business obligations

  Data Protection Assessments : Businesses must conduct data protection assessments for processing activities that pose a heightened risk of harm to consumers, such as processing for targeted advertising, selling personal data, or profiling.

  Consent for Sensitive Data : Businesses must get explicit consent before collecting, processing, or selling sensitive personal data, such as racial or ethnic origin, religious beliefs, health information, biometric data, and geolocation.

  Companies must have adequate data security practices based on the personal information they handle.

  Data Subject Access Requests (DSARs) : Businesses must respond to consumer requests regarding their personal data (e.g., access, correction, deletion) without undue delay, but no later than 45 days after receipt of the request.

  Sale of Data : If businesses sell personal data, they must disclose these practices to consumers and provide them with an option to opt out.

  Universal Opt-Out Compliance : Starting January 1, 2025, businesses must recognise universal opt-out mechanisms like the Global Privacy Control, enabling consumers to opt out of data collection and processing activities.

  Enforcement

  The Texas Attorney General can impose fines up to $25,000 per violation. There is no private right of action.

  Unique characteristics of the TDPSA

  The TDPSA stands out for its small business carve-out, lack of specific thresholds based on revenue or data volume, and requirements for recognising universal opt-out mechanisms starting in 2025. It also mandates consent for processing sensitive data and includes specific measures for data protection assessments and privacy notices.

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  Privacy notices across Florida, Oregon, and Texas

  All three laws include a mandate for privacy notices, though there are subtle variations in their specific requirements. Here’s a breakdown of these differences :

  FDBR privacy notice requirements

  Clarity : Privacy notices must clearly explain the collection and use of personal data.

  Disclosure : Notices must inform consumers about their rights, including the right to access, correct, delete their data, and opt-out of data sales and targeted advertising.

  Specificity : Businesses must disclose if they sell personal data or use it for targeted advertising.

  Security Practices : The notice should describe the data security measures in place.

  OCPA privacy notice requirements

  Comprehensive Information : Notices must provide information about the personal data collected, the purposes for processing, and any third parties that can access it.

  Consumer Rights : Must plainly outline consumers’ rights to access, correct, delete their data, and opt-out of data sales, targeted advertising, and profiling.

  Sensitive Data : To process sensitive data, businesses or entities must get explicit consent and communicate it.

  Universal Opt-Out : Starting January 1, 2026, businesses must recognise and honour universal opt-out mechanisms.

  TDPSA privacy notice requirements

  Detailed Notices : Must provide clear and detailed information about data collection practices, including the data collected and the purposes for its use.

  Consumer Rights : Must inform consumers of their rights to access, correct, delete their data, and opt-out of data sales and targeted advertising.

  High-Risk Processing : Notices should include information about any high-risk processing activities and the safeguards in place.

  Sensitive Data : To process sensitive data, entities and businesses must get explicit consent.

  What these laws mean for your businesses

  Businesses operating in Florida, Oregon, and Texas must now comply with these new data privacy laws. Here’s what you can do to avoid fines :

  1. Understand the Laws : Familiarise yourself with the specific requirements of the FDBR, OCPA, and TDPSA, including consumer rights and business obligations.

  2. Implement Data Protection Measures : Ensure you have robust data security measures in place. This includes conducting regular data protection assessments, especially for high-risk processing activities.

  3. Update Privacy Policies : Provide clear and comprehensive privacy notices that inform consumers about their rights and how their data is processed.

  4. Obtain Explicit Consent : For sensitive data, make sure you get explicit consent from consumers. This includes information like health, race, sexual orientation, and more.

  5. Manage Requests Efficiently : Be prepared to handle requests from consumers to access, correct, delete their data, and opt-out of data sales and targeted advertising within the stipulated timeframes.

  6. Recognise Opt-Out Mechanisms : For Oregon, businesses must be ready to implement and recognise universal opt-out mechanisms by January 1, 2026. In Texas, opt-out enforcement begins in 2026. In Florida, the specific opt-out provisions began on July 1, 2024.

  7. Stay Updated : Keep abreast of any changes or updates to these laws to ensure ongoing compliance. Keep an eye on the Matomo blog or sign up for our newsletter to stay in the know.

  Are we headed towards a more privacy-focused future in the United States ?

  Florida, Oregon, and Texas are joining states like California, Virginia, Colorado, Connecticut, Utah, Iowa, Indiana, Tennessee, and Montana in strengthening consumer privacy protections. This trend could signify a shift in US policy towards a more privacy-focused internet, underlining the importance of consumer data rights and transparent business practices. Even if these laws do not apply to your business, considering updates to your data and privacy policies is wise. Fortunately, there are tools and solutions designed for privacy and compliance to help you navigate these changes.

  Avoid fines and get better data with Matomo

  Most analytics tools don’t prioritize safeguarding user data. At Matomo, we believe everyone has the right to data sovereignty, privacy and amazing analytics. Matomo offers a solution that meets privacy regulations while delivering incredible insights. With Matomo, you get :

  100% Data Ownership : Keep full control over your data, ensuring it is used according to your privacy policies.

  Privacy Protection : Built with privacy in mind, Matomo helps businesses comply with privacy laws.

  Powerful Features : Gain insights with tools like heatmaps, session recordings, and A/B testing.

  Open Source : Matomo’s is open-source and committed to transparency and customisation.

  Flexibility : Choose to host Matomo on your servers or in the cloud for added security.

  No Data Sampling : Ensure accurate and complete insights without data sampling.

  Privacy Compliance : Easily meet GDPR and other requirements, with data stored securely and never sold or shared.

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  Disclaimer : This content is provided for informational purposes only and is not intended as legal advice. While we strive to ensure the accuracy and timeliness of the information provided, the laws and regulations surrounding privacy are complex and subject to change. We recommend consulting with a qualified legal professional to address specific legal issues related to your circumstances. 

• 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.

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  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.

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