Strengthening Banking Customer Relationships Using the Power of Data

For bankers all over the world, top of the priority list for 2022 is to improve customer relationships. The business benefits are striking – added revenue opportunities, improved customer experience (CX), increased competitive advantage, and heightened profitability. 

To fully capture these and other benefits, banks are delving into data, both internal and external, seeking to unlock insights about their customers and prospects. For many, the examination of their internal data is sobering, and comes up well short of what’s needed. 

Customer Data in Banking – Much Work to Do 

Why do banks struggle to consistently realize the value locked in their data? 

It’s certainly not for a lack of desire. As Gartner Group observed in summing up banking priorities in its 2020 CIO Survey, “Data analytics and artificial intelligence (AI) were at the top of the list and roughly equal for financial services CIOs. Here’s the problem — that’s nothing new.” 

As we start 2022, we’re now nearly two years removed from that Gartner survey, and progress in effectively leveraging data still remains slow. 

In a Deloitte study, less than one-third of retail bank customers felt that their banks make them personalized and highly relevant offers. Can you imagine customers in any other retail sector saying that? Neither can we. 

Existing Challenges with Banks’ Customer Data 

This range of issues was the subject of an enlightening discussion between Ruwan Wijetunga and Sunil Patel of Earnix partner Finity Consulting, as part of the recent Earnix Excelerate 2021 Summit. Here’s just some of what they had to say. 

Problem #1 – Misjudging Consumer Preferences

Consumers are increasingly willing to share their personal data, or allow banks to mine it, if in return they are presented with the right products at the right time, products that will help them create wealth, reach their goals, and optimize their financial health. 

In support of this notion, Sunil Patel cited recent research by Bain that indicates “customers who were once cautious about putting their personal data online, that’s changing now and [they] are even more willing to do that for complex transactions…So, if they’re going to get something for that, i.e., a quicker process, a better outcome, people are more than happy to do that.” 

Sunil adds that open banking standards and practices will further accelerate data sharing and value creation for customers. 

Problem #2 – Too Much Data, And Not Applied Well 

The problem with data in banking is not one of scarcity – instead, we are grappling with an overabundance. The data we already own is more than enough to whet the appetites of data scientists, actuaries, risk analysts, and pricing teams, and to keep them busy for quite some time. 

As Ruwan Wijetunga put it, “lenders have got so much data, but I’m not too sure that they’re effectively using the data that they’ve got.” 

Problem #3 – Where the Data is Stored

Per Wijetunga, “The biggest challenge they [banks] have is a lot of this data is in legacy systems. It’s siloed, it’s unstructured, it’s not enriched with external data sources, and when you’ve got those sorts of challenges, it makes it really difficult to use all of that data for personalization, whether it’s pricing or whether it’s lending.”  

The Road to a Solution 

In their session, Wijetunga and Patel also shared some best-practices thoughts gleaned from their successful client engagements. It provides a good roadmap for your journey to better data utilization: 

  1. Get Your Data in Shape – Patel observes “we very frequently talk to clients about offering some sophistication when it comes to AI and data, and we find that the minute we get in there, their data’s not in a position to actually utilize those techniques.”

His advice: “Pay attention to your data architecture and your data processes. Make sure that your data’s enriched, it’s clear that it’s sitting in the right formats, so that you can use the data science techniques that are out in the market today. Look at your foundations, make sure that they’re there, and then you can utilize some of these techniques.” 

  1. Start by Focusing on a Single, High-Priority Problem – As Wijetunga pointed out, “From a solution perspective I think pricing is a really good place to start.”

And take it in phases: “With personalized pricing, I think the first step is establishing that technical price, building AI and machine learning models. I think it’s really important to understand firstly, the customer data, so you can personalize that price. And secondly, your internal costs that are associated with this particular customer. That is really, really critical – to build your models around those costs.” 

  1. Dig Deeper into Customer Behavior – Ruwan suggested this as the next step: “I think understanding whether the price that you set for your products will be attractive to your customers is critical to drive conversion. We recommend with our clients that you should always do price testing on a portion of your portfolio. It’ll allow you to adapt your pricing to either maximize revenue or conversion or to maximize your margin.”
  1. Factor in Competitive Intelligence – to complete the picture, Wijetunga suggested competition is the final ingredient: “So once you understand what your internal costs are, then you understand how elastic your customers are to price, the third element is understanding what your competitors are doing.”

 

At least part of this intelligence gathering can be automated: “We can use bots to scrape competitor websites or OEM websites or banking websites. And it pulls all of that data in. And what it does is it allows you to assess the price points of particular people or particular demographics,” sound advice for completing the picture for personalized pricing. 

Next Steps 

Whether you’re just starting this journey toward building better customer relationships, or you need a boost to accelerate your progress, Earnix is ready to help. 

Earnix, in partnership with DataRobot, delivers an AI-driven pricing and personalization solution that empowers business analysts and data scientists to build and deploy state-of-the-art machine learning models in a fraction of the time, when compared with traditional analytical modeling processes. 

The end result will be speed, control, and accuracy of insight, all critical to putting your data to work to improve relationships. Now is the time. 

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