Marketing Analytics in Banking: How to Be Effective and Compliant

• For every company, tracking marketing effectiveness should be a key part of their strategy. This is especially true for those with complex products or longer sales cycles. For these transactions, like applying for a bank loan, a customer might open many emails or return to a website more than once before making a decision. Banks and financial services have access to a wealth of data, but they are also heavily regulated. If done properly, banks can ethically and safely use this data to craft more targeted, effective marketing campaigns.

In this article, we’ll look at how banks use data to power their marketing initiatives, the common challenges faced in this sector and best practices for overcoming them.

Five marketing analytics techniques for banks

Marketing analytics is complex, but it doesn’t have to be daunting. By focusing on these five proven techniques, your bank can quickly enhance its marketing effectiveness and customer understanding.

Let’s look at some of the most common techniques marketing teams employ and how to apply them in the banking industry.

1. Customer segmentation

Customer segmentation allows banks, finserv and fintech companies to group their clients based on shared characteristics, enabling more targeted marketing and service approaches. While traditional methods relied heavily on basic demographic data, new technologies and data sources have expanded segmentation possibilities. Some examples of customer segmentation in the financial sector include:

• Demographic segments: Basic information such as age, income and location. Though still useful, this data alone doesn’t provide a complete picture of customer needs and behaviours.
• Behavioural segments: These focus on how customers interact with banking products and services. Examples include:
  • Transaction patterns: The types and frequency of financial transactions
  • Channel preferences: Whether a customer prefers digital, phone or in-person banking
• Product adoption: Which banking products a customer uses and how quickly they adopt new offerings
• Attitudinal segments: Groups customers based on their financial attitudes, risk tolerance and banking preferences. This might include categories like “tech-savvy savers” or “conservative investors”.
• Life stage segments: Recognises that financial needs often align with life events rather than age. For instance, first-time homebuyers or new parents may have similar banking needs regardless of their age or income level.
• Value-based segments: Considers the current and potential value of customers to the bank, helping prioritise retention and growth strategies.

Financial services providers sit on a wealth of first-party data (e.g., customer records, internal surveys, market research and web analytics tools like Matomo) just waiting to inform customer segmentation. To get the most out of your data, analysts and leaders should consider the following questions:

1. Data integration: How can we combine data from various sources to create a holistic view of our customers?
2. Segment fluidity: How often should we reassess our segments to account for changing customer behaviours?
3. Predictive potential: Can we use our segmentation model to anticipate future customer needs or behaviours?
4. Regulatory compliance: How do we ensure our segmentation practices adhere to relevant banking regulations?
5. Cross-selling opportunities: Which segments are most receptive to additional products, and what’s the best approach for each?
6. Digital engagement: How do different segments interact with our digital platforms, and how can we optimise these experiences?

Segmentation approaches offer financial institutions deeper insights into their customers’ needs and behaviours, paving the way for more effective and targeted marketing strategies.

2. Customer lifetime value analysis

Customer lifetime value (CLV) analysis helps banks understand the long-term value each customer brings to their institution. CLV considers key performance indicators (KPIs) like

• Revenue per customer,
• Revenue sources and,
• Customer acquisition cost (CAC). 

According to Gartner, CLV analysis is one of the top five marketing techniques used by 25% of marketers. It helps to pinpoint which customers spend more on your products and services and shows ways to turn lower CLV customers into higher ones. It also helps to compare different customer segments so financial institutions can refine their acquisition and targeting priorities.

3. Sentiment analysis

Sentiment analysis is tracking how customers feel about your products, advertising campaigns and services based on various sources, including:

• Social media comments and mentions
• Customer surveys and reviews
• Call centre interactions
• Mobile app store reviews
• Online banking feedback

X user @patrickb_93 showing positive sentiment at Rabobank’s newly added dark mode feature on iOS. (Image Source)

Banks use sentiment analysis to:

1. Identify trending issues or concerns
2. Measure the success of new product launches or features
3. Gauge reactions to marketing campaigns
4. Detect early warning signs of customer dissatisfaction
5. Inform product development and improvement

Real-world applications in financial services:

• Tracking sentiment around a new fee structure to assess customer acceptance
• Analysing feedback on a recent ad campaign to refine messaging
• Monitoring social media during a system outage to manage customer communications
• Evaluating sentiment toward competitors to identify opportunities for differentiation

By systematically analysing customer sentiment, banks, fintechs and finserv can:

• Proactively address issues before they escalate
• Tailor marketing messages to resonate with customer feelings
• Improve products and services based on customer feedback
• Mitigate negative sentiment during crises, such as data breaches or service disruptions

Sentiment analysis, when combined with other analytics techniques, provides a more complete picture of customer perceptions and experiences. This insight allows banks to make data-driven decisions that improve customer satisfaction and loyalty.

4. Campaign performance analysis

Campaign performance analysis is looking at the results of marketing campaign efforts. During and after campaigns, you should track KPIs that can inform you of their effectiveness, such as conversion rates, click-through rates (CTR), ROI and retention rates. 

The specific KPIs to track will depend on the purpose of your campaign. If your goal is new customers, you will want to track conversion rates and CAC. If it’s brand awareness, you might measure social media engagement or website visits.

Data gathered this way can be used to tweak future campaigns for even better results.

There are some potential snags marketers might run into that they should be aware of, like:

• Inaccurate data caused by data siloing, outdated data or data collected by tools that rely on data sampling, like Google Analytics
• Analysing data obtained through means that violate banking industry or data protection regulations
• Not accounting for the long-term payoff of campaigns down the line that can affect CLV or increase brand awareness
• Lack of in-house expertise producing weaker insights

Some of these issues require company-wide efforts to fix them, but others, like data sampling and maintaining compliance, can be solved by using Matomo for web and mobile analytics.

5. Channel optimisation

Channel optimisation is finding the best ways to use your marketing channels with data from tools like Matomo. Each channel will require its own approach based on factors like audience demographics and customer behaviour.

For example, a study found that Gen Z and Millennial audiences strongly prefer social media marketing over TV advertisements, but Baby Boomers are the opposite, and Gen X go either way.

It’s not enough to market on a specific channel either – teams need to know what their customers want from them there. The same study found that 59% of customers will sign up for newsletters if they receive discounts or special offers.

As you analyse your data, you’ll see how these techniques overlap. When evaluating a recent campaign’s performance, you might see an uptick in Gen Z and Millennial customer acquisition after posting an absurdist TikTok video. 

You can gather more data via sentiment analysis on the comments to help you understand audience reactions to your efforts.

While applying these techniques may boost performance, you might stumble on some common roadblocks to higher ROI.

Four common challenges marketers face in the financial services industry 

Everyone faces challenges in marketing. Some are common to every marketer, but some are unique to heavily regulated industries like banking and healthcare. 

Let’s discuss these challenges before covering ways to overcome them.

1. Staying competitive while staying compliant

Like healthcare, banking is a heavily regulated industry. Unless you’re using compliance-focused software like Matomo, it’s hard to stay compliant.

In the US, there is increased scrutiny of regulation violations in the banking sector, with banks seeing a tripling of fines imposed from 2022 to 2023. 

In the EU, GDPR violations have resulted in European banks paying millions in penalties over the types of data processed, how it’s processed and how consent is handled. 

One Spanish bank received a €6m fine for GDPR violations like complicating the consent process and not sufficiently informing customers of where their personal data was going. This bank almost certainly could have improved marketing outcomes with that data, but they didn’t follow the rules and violated customer trust. 

Fines and revenue lost to reputational damage will cancel out any potential ROI from knowing a customer segment responds best to 5% cash-back deals.

2. Lack of technological cohesion

The longer a bank has been around, the more time it’s had to collect software from different vendors that may – or may not – play well together.

This tech stack chaos can cause data to silo between departments, leading to marketing teams drawing conclusions from different datasets than customer support or sales. Or it can lead to manual data analysis, which comes with the risk of inaccurate or incomplete information caused by human error. These data problems compound over time when the employees who originally knew how to make these differences work leave the company or retire.

It can be challenging for companies to address this situation when each disparate tool handles a critical part of the business, but a poorly integrated ecosystem runs the risk of lowering productivity, increasing the need for manual data entry and more.

3. Working with clean and accurate data

According to Gartner, poor data quality costs companies nearly USD $13m a year. To minimise this cost and to have data worth analysing, you need to know it’s clean and accurate. This means setting quality standards like checking that it’s up to date, removing duplicates and checking data accuracy.

Some tools, like Google Analytics (GA), perform data sampling after a certain threshold, creating estimates instead of reflecting facts. This leaves you with inaccurate reports that waste marketing spend. 

These same tools might also rely on third-party cookies, which lead to incomplete data when they get blocked by privacy-focused browsers or add-ons. Global marketing agency CRO:NYX Digital ran into this issue when they ran ads on Brave, and Google Analytics couldn’t track traffic due to its use of third-party cookies.

4. Skill gaps and increased responsibility

As stated earlier, marketers claim that data and analytics are their biggest skill gap. 

The same MarketingWeek survey showed that just roughly a third were given opportunities to gain the skills they lack. Furthermore, a 2023 ABA Banking Journal report revealed that banks aren’t hiring new marketing employees but are adding responsibilities. 

Marketers lack adequate skills, aren’t getting trained and, in the banking sector, are getting added responsibilities without additional headcount.

These problems may seem overwhelming, but you’re not alone. Many institutions with long-standing, complex systems have faced – and overcome – these issues, providing a blueprint for the companies that come after. You can learn from those who figured it out.

Five marketing analytics best practices for banks and finserv

The best marketing teams address these challenges and make it look easy. Are they just inherently that good? Of course not.

These teams get results by following the best practices that they picked up during their career. You can follow them, too.

1. Pick the right tool for your team’s needs

Your software should not only enable you to draw accurate conclusions from your data but also solve problems you may face. Matomo does all this.

Because we built it with privacy and ethical web analytics in mind, Matomo makes it easier for you to:

• Be compliant: Some companies fear compliance laws, but they’re our bread and butter. Matomo is GDPR compliant and is easy to configure for HIPAA, CCPA, PIPL, LOPDGDD and LSSI compliance.
• Integrate: Matomo offers over 100 integrations and counting! Need something special? Matomo is open source and our helpful developers are on standby to see how we can help you connect to your existing systems.
• Gather complete and accurate data: Without data sampling, your reports will be more precise. We also use first-party cookies by default, so we work well with browsers like Brave. After switching to Matomo, CRO:NYX Digital saw Google Analytics’ reliance on third-party cookies had caused a mindblowing 11,700% discrepancy in tracked visitors recorded.
• Do your job effectively: While you may be in the early stages of making your marketing data-driven, Matomo won’t complicate things – it’s flexible and simple to use. Default settings produce accurate and insightful reports, but customisation combines ease and depth for an insightful, user-friendly experience.

2. Establish high data quality standards

Your data should be accurate, up-to-date, free of duplicates, complete and respect the laws of the countries you operate in. You should assess risk areas where quality concerns may arise, like misconfigured software, data silos and not performing data validation. Using software like Matomo helps with accuracy, completeness and compliance. Integrating Matomo and your other software can lessen the risk of bad data generated by manual entry. In cases where manual entry may still be necessary, databases or other software are able to set restrictions to prevent submissions with blank fields, duplicate entries or similar issues that may sabotage your analyses.

Commitment to quality should be an organisational effort. Software and web development teams can create prompts for users to check the accuracy of their information at yearly intervals to keep data up-to-date. Sales, customer service and marketing departments can share relevant insights. All teams can agree to use the same software and databases to prevent siloing.

3. A/B testing

A/B testing is trying out two forms of something, like marketing emails or mobile banking screen layouts, and measuring the difference in outcomes between them. It is an effective method of optimising your user experience and is performed by groups like marketers, software developers and product managers. 

If you’re unsure where to start applying the techniques discussed earlier, gather data from email A/B tests for channel optimisation. Tech Report shows that 59% of companies conduct A/B testing with emails and that subject line variation makes the most significant difference in performance.

4. Personalisation

Personalisation means shaping your ads, promotions and products to fit each customer’s wants. By looking at the customer data you’ve collected, you’re able to learn what resonates with specific segments, reach them through the proper channels and see your campaign performance KPIs soar. 

A McKinsey report shows that personalisation cuts CAC in half, increases revenue between 5-15% and ROI by 10-30%. The same report shows that 76% of customers get frustrated when they don’t get personalisation.

For example, if a bank’s data indicates that their customers with student accounts often shop at specific retailers, they might offer cash-back deals for purchases made there. Similarly, banks can offer cards with low or no penalties for withdrawals or purchases made overseas for customers who travel often. 

5. Continuously learn from the data you collect

It’s not enough to collect more accurate data—banks must also close the skill gap. 

Applying these techniques and best practices is a great start. You can use Matomo for A/B testing, channel optimisation, customer segmentation, campaign performance analysis and more. Bring in data from other sources and invest in skill development and you’ll be well on your way to maturing your analytics capabilities. Then, keep striving for more, as there is always room to improve in data analysis.

Dig deeper into your analytics data with Matomo

To provide outstanding service, you must identify what customers want, where to find them and how to speak with them. With Matomo, you get a complete picture of customers’ behaviour and preferences while earning their trust with advanced privacy protections. 

With this data, you can find new and improved ways to reach them in the places that matter to them most with offers that will keep them loyal and increase their lifetime value.

Matomo is the leading open-source web analytics platform used on more than 1 million websites and apps in over 150 countries, available in more than 50 languages. 

With Matomo, you can:

• Collect 100% accurate data without the need for AI or ML to fill in the blanks
• Use a tag manager to manage and enhance your website’s performance whilst saving time and money
• Seamlessly migrate from Google Analytics, saving years’ worth of data without loss of key insights
• Get peace of mind with GDPR compliance and privacy assured for your customers
• Keep complete control with 100% data ownership so your clients know that no uninvited third parties are looking in

Contact us today to arrange a free demo.