Reducing churn by identifying high-value customers and upselling them – here’s how to use segmentation to enhance the experience for your highest-value users
Need to reduce churn and boost retention in FinTech?
Research shows it pays to focus your retention efforts on your top-tier, high-value customers – for most companies, 80% of revenue comes from the top 20% of customers. And Harvard Business School shows that increasing retention by just 5% among high-value customers can boost revenue by 25%-plus.
Here’s how to use segmentation tools to identify your high-value customers…
These are customers who contribute significantly to your revenue, either through consistent purchases, high transaction values, or long-term loyalty.
In FinTech, they are the customers with the highest potential lifetime value (LTV) for you, making them essential to focus on for retention and growth. In this post, we’ll explore how segmentation tools can help you identify and retain these high-value customers and reduce churn effectively.
Customer segmentation involves categorising customers into smaller, more manageable groups based on shared traits, such as demographics, purchasing behaviours, or engagement with your brand.
It allows you to isolate a specific segment/cohort of users, such as your high-value customers, and then focus on understanding them better and personalise interactions to improve their overall customer experience. With the right approach, you can maximise customer lifetime value while reducing churn.
See how to use data to boost customer loyalty.
Your analytics and deep analysis tools can help you find and isolate specific customer cohorts, based on specific parameters. Finding your high-value customer depends on your product and business model, but in general, a few good guidelines are to focus on:
The first and most obvious customer segmentation is based on how much money they spend with you. If you have a tiered subscription model, for example, your premium customers earn you the most income over any given period, so retaining them unlocks the most direct value.
If you’re earning based on usage (a handling or transaction fee) look at the cohort of customers that have generated the most revenue, and therefore fees, over a specific period. Retaining these, and maybe even boosting their spending gives you the most direct benefit.
If your sample is small, you’re changing strategies or, for whatever reason, you can’t/don’t want to use LTV, other metrics to use are frequency of purchases, average spend, referrals or even brand interactions. This helps you identify high-value segments that clearly derive value from your offering and are therefore a good market fit for you.
An approach with multiple uses, dividing into cohorts such as active users, dormant accounts and occasional buyers presents different opportunities. By segmenting customers based on their level of engagement, you can implement targeted re-engagement campaigns for inactive users or offer special deals to high-value, active customers.
See how to use customer data analytics to elevate experience, gather valuable feedback from segments and boost your customer experience in finance.
Once you’ve identified your high-value segments, you can tailor upselling and cross-selling strategies to meet their needs:
See how to use AI and machine learning for personalisation, how to implement personalisation and how to roll it out at scale.
Once you know what to look for, ensure you have the right tools in place to track and monitor smartly. Your most basic tools enable you to track and segment users, but then the next important automation step could be to get an in-depth analysis so you know you can trust your data to inform strategy. Tools to look at include:
Tools like Google Analytics, Segment and HubSpot are platforms that help track customer behaviours, interactions, and purchase histories, making it easier to group customers into meaningful segments.
Going a step further with Recency, Frequency and Monetary analysis (RFM), gives you a more precise view of segments based on when they purchased, how often they engage and then their LTV and projected future spend. This can help you identify high-value customers a little faster.
Normal Google Analytics doesn’t have this function built-in, but you can build your own RFM dashboard via Google Cloud’s Looker. Otherwise, HubSpot and Segment have RFM features, and so do Zoho Analytics, LoyaltyLion and Adobe Analytics.
See how to use analytics to improve app user engagement and build memorable experiences by using customer journey analytics.
Once you have your cohorts segmented and started employing strategies to boost their engagement and loyalty, be sure to track your progress.
It helps to have some key KPIs in place. For example, it helps to measure a high-value customer segment’s churn rate before you implement any retention/upsell strategies, and then compare them to the churn rate after.
You actually want to reduce that churn rate, otherwise, your strategy isn’t working and you should try another approach.
And remember to segment iteratively: Customer behaviours change over time, so update your segments regularly so that you know you're putting your effort into the right cohort at the right time.
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