How can computers collect better than humans?

TrueAccord Blog

When we started working on our patented collection engine, Heartbeat, the industry told us: you’ll fail. Computers can’t collect. Humans do. The best you can do with automated communications is to drive inbound calls, so human collectors can “seal the deal”. Fast forward 18 months since our launch, and Heartbeat beats call-center based agencies in a growing number of segments.  It turns out that computers collect debt pretty well. How come?

Debt collection is a numbers’ game. Consumers are ready and able to pay at different times, react to different stimuli, and need varying levels of support in the process. Teaching a machine to respond to these needs was historically more expensive than hiring humans, but as technology improves and compliance requirements grow, this is changing rapidly.

Humans are great at acting on intuition and responding to a changing situation. We act well based on partial information, guesses, slight changes in tone of voice and intonation. Good sales people do so without thinking. Humans are great at identifying and understanding corner cases and responding to complex inquiries. Machines can’t learn these things unless explicitly taught, and many of these skills are nuanced and complicated. Machines are “robotic”, for better and worse, and can’t have empathy.

Humans do have downsides, too. We are susceptible to biases. We make decisions based on the few past examples we remember and ones that fit what we believe. Collectors fixate on high balance accounts, worry about missing their goals, fight with their significant other and lose focus. Machines do not. Machines don’t forget a thing, and they always take as much data as available into consideration. Machines don’t talk back or get angry.

Historical attempts failed because they either tried to replace humans with even lower-paid humans, or tried to automate and get rid of humans altogether. We realized that a hybrid approach was the best one: machines make accurate decisions based on historical data when available, and learn from humans when not. Humans understand corner cases. We had to create a combination of a strong engine, and a team of experts to continuously improve it.

How does that work? When Hearbeat doesn’t “know’ what to do with a customer, it defers to our team of experts in San Francisco. They resolve the issue for the customer, and also give enough input so Heartbeat will know how to deal with the same situation in the future. The combination allows us to hit incredible productivity rates, while beating other “robotic” and passive “payment gateway” solutions.

Can machines collect? They can, and apparently many who are in debt prefer their targeted approach. When you think about the user experience, the ease of use and the automation, it’s actually not that surprising.

How We Raised Click through Rate by 50% with a Simple Change

TrueAccord Blog

The technical and analytical vision behind TrueAccord is to add data-driven decisions to the communication model in debt colelction. Digital communication enables better data collection, and better understanding of customer behavior patterns. We can collect and observe open, click and browsing patterns that sometimes do more to explain how to engage with a customer than explicit communication. By connecting customer behavior to their mental state, and responding to that state with corrent language, we were able to substantially increase engagement on our collection communication.

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Using a complex model for measuring success in interactions with customers

greatest-action

The TrueAccord product can be compared to an automated marketing and sales campaign, focused on identifying payment intent and acting on it. The system classifies customers (we use “customer” to refer to those in debt) by their most probable reason for non-payment, and the “voice” we think is going to drive that to action. Then, it has to decide, out of the hundreds of content items we have, which goes out to which customer, through what channel, and when. Of course, we’d like to learn from history and send the combination most likely to succeed. This raises the important question: what’s “success” in our context?

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Beware Of The Disposable-Business Customer

TrueAccord Blog

disposable-business-customersI’d like to share a disturbing phenomenon we’ve observed in our data, one that probably impacts many of you who provide services to online businesses.

When a new customer is entered into our system, we crawl for additional information about them on the web as well as look for them elsewhere in our system. As we grow and see more companies and the people who owe them money, we start to see chains of debts that are linked to one another. Deeper linking checks revealed that the individuals connected with these debts owned multiple websites, some of them dysfunctional, and often with web hits that indicated some kind of foul play.

As we dug deeper, the picture cleared. There is a large number of individuals, operating online, starting and closing businesses in a highly irresponsible manner bordering on fraud. These individuals, by and large (as far as we can see) unrelated to one another, tend to serially open businesses with hardly defensible business models, contract the majority of the work for them to third parties, charge unsuspecting consumers large amount of money, and move on to the next business. We are unable to determine, at this time, whether this is pure fraudulent behavior or just extreme irresponsibility, but the pattern exists. We see it across fashion, insurance, financial planning and several other services.

As a business owner, you should be aware of this pattern, especially if you are providing services to online businesses. Design, eCommerce, Hosting and other related services are especially vulnerable since they are key in starting a new online property. TrueAccord customers can ask for access to our Data Furnishing program, that can provide data to help identify these individuals before they are able to do business with you. We recommend that, before you take on new customers for large projects, you run a manual search of their web history, previous businesses, and other owned domains. You may (or may not) be surprised to discover a few of those in your own customer roster.