Data is so pervasive today it’s easy to forget it hasn’t always been that way. About 90 percent of data currently in existence have been created within the past two years alone. Moreover, many would consider financial services to be the leading sector in terms of data intensiveness.
There are many, many examples of how decision-makers within the financial services field can harness data, but here are four important ones.
People used to consider banks as institutions existing outside the retail sphere — and therefore commonly accepted waiting in lines, going through specific channels to access certain services and working around inconvenient business hours. But banking is now becoming increasingly indistinguishable from a retail experience in that customers expect immediate, personalized, multichannel experiences accessible at any time of day.
Bank managers need to keep their fingers directly on the pulse of customers’ wants, needs, complaints, and usage behaviors to stay competitive. Here’s how decision-makers within banks are using financial analytics to streamline the customer experience:
- Identifying relevant cross-sell and upsell opportunities based on consumer behavioral data.
- Tailoring the manner in which products and services are delivered, based upon highly specific client profiles containing demographic, behavioral and historical data.
- Improving the relevancy of marketing communications and content recommendations across channels.
Besides optimizing customer service, bankers are using financial analytics to identify the risk of default and delinquency on customer accounts — and even take pre-emptive action to help customers get back on track before both parties feel the effects.
Competition can be mighty intense in the world of lending, so it’s little surprise mortgage brokers are harnessing data analytics to improve lead generation — targeting prospects most likely to be in the market for certain homes based on home, price point, etc. This helps brokers refine the timing, relevancy, and personalization of their marketing missives.
Although acquisition is a major challenge, retention is even more elusive and valuable. So, lenders are looking for ways to smooth out the homebuying process from start to finish once a lead is in the funnel. Some mortgage brokers are giving customers access to advanced self-service analytics capabilities so they can dig into data on their own — filtering properties at a very detailed level, based on whichever criteria they hold dearest.
Insurance is all about trying to assess risk before claims happen, so one major analytics use case for insurers is using artificial intelligence to assess risk profiles of applicants — with the goal of improving the accuracy of their ability to underwrite policies.
As with the case of banking and mortgage lending, insurers can benefit from the data-driven ability to target potential customers with relevant and personalized communications. There’s also the possibility to use analytics to better understand claims and prioritize them to reduce the time to settlement.
Financial advisors are harnessing advanced analytics to improve the timeliness and relevance of advice they offer clients. As one expert notes, data visualizations, in particular, are an effective vehicle to help clients forecast their financial futures — as they’re much more engaging than spreadsheets, allowing clients to drill down into data to better understand trends and outcomes.
Financial advisors also use artificial intelligence and machine learning to predict life events clients may be about to experience, which can help them market more effectively to potential clients and existing ones.
It’s clear that acquiring and retaining customers — as well as optimizing profitability — depends increasingly on a company’s approach to data analytics, no matter the specific financial services sector in question.