There is growing demand by customers to communicate when and how they want. In a recent study conducted by Interactions, 43% of customers say that talking to a live agent is their least preferred method of communication. You don’t have to look much further than the growing number of young adults all around us to see how most connect with each other, with brands, and with service providers. While young adults are a good group to study, I know many others (like me) who have similar preferences. Although what drives our preferences may be different, our needs are similar. I’m a busy, working mom. When I’m not working, I want to maximize quality time with my family. If I can pay a bill online or get a question answered through chat or email, while also searching Pinterest for a dinner recipe, waiting out piano lessons or fielding homework questions, I’m not only SOLD, I’m also grateful and loyal.

You can hardly read an article about collections innovation without some reference to the increasing use of digital tools, often combined with some form of Artificial Intelligence (AI). The way that AI can be and is being used across service, collections and recovery varies greatly, but it’s growing in its ability to help the industry customize experiences and maximize results.  

I’ll hit on some of the widely-used and growing methods:

Process-level automation

In some cases, creditors and agencies are easing into AI by hiring companies to write what are essentially high-powered macros across multiple systems to automate processes that don’t require subjective review. In other words, robots are great at things that are consistent and repeatable. Beyond the obvious efficiency, the use of AI in this sense also helps reduce human error and improves service levels. It also frees up human bandwidth for those operational aspects that do require subjective judgment.

Strategy-level channel strategy

To date, companies have largely leaned on industry intelligence, case studies, and good old fashioned intuition to guide the evolution of omni-channel strategies. Where those strategies relied heavily on traditional, verbal communication, it has become clear that clean, fast digital communication drives results, lowers cost and also returns clear information on which to base future business decisions. Furthermore, AI is using data on consumer behavior at the portfolio level (auto, credit card, mortgage, student, etc.) to guide companies on which channel strategy mix makes the most financial sense, and which levers to pull (and when) based on the product, delinquency, balance, etc.

Customer-level interaction strategy

This is where it gets fun. It’s one thing to know, at a portfolio level, the smartest mix of omni-channel solutions based on ROI, but tailoring customer preference at the individual level based on what data reveals can be transformational. In today’s environment, we use models of varying levels of sophistication, combined with segmentation, to determine the channel type, length, and mix. If those tools could be used in combination with what what we learn from data gathered about customer preference, then we would be able to close the loop. For example, if a customer’s behavior indicates they’re more likely to prefer making a self-service payment rather than talking with an agent, then they should be directed first to a self-service application. This achieves a higher probability of a positive customer experience and payment, while also reducing cost.  

insideARM Perspective

The move to more digital forms of collections is happening---and fast. While preference is a key driver, the proliferation of scam callers and the threat of robocall blocking by carriers is putting pressure on traditional ways to successfully reach customers. Those who don’t get on board will find themselves at the end of the line when consumers are determining how to spread their limited funds across the companies asking for their time and payments.

Staying informed and current on what’s happening in the industry is every leader’s responsibility!

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