- Retailers have made tremendous strides in customer personalization, but their playbook is wrong for financial institutions, whose emphasis needs to be on deepening the relationship, not on making the next sale.
- Unlike those of retailers, banking products usually aren’t bought on impulse and come with long-term commitments that can have a direct impact on an FI’s profitability.
- Because relationships don’t generally follow an orderly, linear path, technologies that use recursive learning, ideally fueled by AI, can help marketers quickly build those relationships to scale.
In the fast-paced world of digital marketing, personalization has emerged as a critical strategy for engaging customers and driving sales. Major retailers have pioneered many of these efforts, leveraging real-time data and recommendation engines to trigger highly personalized shopping experiences that maximize in-the-moment shopping–cart conversions. It might be tempting for bank marketers to think they should replicate the retail model. But not so fast. Financial institutions face an altogether different set of circumstances and challenges, so their approach to personalization needs to be relationship-first.
The Retail Playbook vs. The Banking Paradigm
Retailers operate in an environment with vast arrays of products and frequent, discrete purchasing events. With hundreds of thousands or even millions of SKUs, they constantly aim to increase the basket size and conversion rate during each visit through sophisticated algorithms that analyze past behaviors and predict future purchases. Their personalization strategies are designed to get customers to return for another shopping session and, once they do, to maximize the value of the session.
Banks, on the other hand, have a fundamentally different product set and customer-relationship dynamic. The range of products they offer – checking and savings accounts, credit cards, loans and investment services – is comparatively limited. More important, banking products typically aren’t bought on impulse; they’re chosen thoughtfully, and they usually come with long-term commitments. And unlike with retailers, how consumers use an FI’s products has a direct impact on its profitability. As a result, personalization in banking needs to focus on fostering long-term, profitable relationships rather than merely driving transactions in the near term. That’s why the focus of banking personalization should be on creating relationships that are seamless, supportive and trust-based.
The Opportunity Cost Of Transactional Personalization
Attempting to mimic retail personalization strategies can lead to several pitfalls for banks and credit unions:
- Misaligned objectives. In banking, focusing on short-term gains and transactional success can lead to missed opportunities for deeper engagement and relationship building. FIs need to think beyond the immediate transaction and consider how the next several interactions will strengthen the overall bond.
- Customer trust. Banking relationships are built on trust and reliability. Aggressive, retail-like personalization tactics can undermine both by making customers feel as if they’re constantly being “sold” rather than being genuinely understood and supported.
- Relevance and value. Retail personalization often involves bombarding customers with a multitude of product suggestions. In banking, this can quickly become overwhelming, irrelevant and ultimately counterproductive. Customers expect their institution to provide personalized advice and solutions that are useful and timely, rather than a barrage of product pitches, which they tend to tune out quickly.
Unlocking Relevant Relationships
Unlike online retailers, banking institutions need to leverage personalization to understand each customer’s unique financial situation, needs and goals and to provide personalized advice and solutions that help meet them. But this is easier said than done. The marketing-technology infrastructure in use today is built for deterministic decision-making driven by a specific customer context – in other words, rules-based decisioning.
Using rules-based marketing means bank marketers have to try to pre-engineer personalization. Analytical teams first need to determine how to segment customers and then quantify the most critical relationship-building opportunities for each segment. Creative teams then generate distinct executions for each segment opportunity, and digital marketing teams program test cells and work with analytical teams to read results. Only then can they scale the most opportune approaches.
Not surprisingly, this method rapidly hits a point of diminishing returns – and keeps digital relationship building stuck in Automaton Land. That’s because real relationships generally don’t follow an orderly, linear path. A near-endless variety of contexts drive people to engage with a bank’s products and services along their journey (Figure 1). FIs of all stripes desperately need more marketing interactions that evoke and foster a sense of connection. Results will follow and, in time, profits as well.
Figure 1: New Customer Journeys
Seasoning relationships digitally is difficult.
Advances in marketing software offer a path out of this linear logjam, with continuous test-and-learn capabilities that make decisions smarter and more relevant over time. Instead of prefabricating journeys and interactions, new technologies, ideally fueled by AI, engage recursive learning that can enable marketers to be more effective to scale (Figure 2).
Figure 2: Scalable Recursive Learning
Artificial intelligence has helped close the feedback loop
and adapts actions in response to the feedback.
Armed with these models, teams can invest their time and energy in building a robust library of treatments for any objective – such as driving primacy during the first 60 days, growing deposits at the lowest possible cost of funds or improving financial fitness. The executional components within the library flex different variables from the topic being communicated to positioning, proof-point emphasis, tone, imagery, copy length, sequencing and incentives. A sophisticated machine-learning engine can then draw from the treatment library to run tests with micro-segments at a scale that’s simply not possible for human teams. It can also observe downstream behavior to learn what’s working, or not, and automatically adapt campaign messaging going forward. Such an approach dramatically improves the relevance of each customer interaction without requiring a scale-up in personnel.
A growing body of evidence shows that efforts in true relationship-building marketing are worth the effort. FIs that establish digital empathy with their customers through personalization enjoy benefits at every stage of the customer lifecycle – from reducing the cost of acquisition to improving primacy and driving relationship depth. Banks that have made the switch away from rules to more dynamic continuously learning enjoy significant increases in customer retention, deeper, more stable relationships over time and, most important, 2-3x revenue growth,.
To compete in today’s digital-first world, virtually all banks and credit unions will have to humanize their customer interactions to drive deeper, stickier relationships. By investing in the right technologies and adopting a customer-centric mindset, they can transform personalization from a tool that prompts transactions into a powerful engine for long-term relationship building and financial empowerment.
