Innovative automation processes are finally gaining traction in debt collection, as companies increasingly distance themselves from costly and unmanageable call centers. And now, with an eye on continuous process improvement, a new focus on experimentation is enhancing the way these companies recover revenue and create a more effective user experience. Experimentation engines – whereby various collection scenarios and features are tested and evaluated based on real-time data – empower creative and customized contact and offer strategies that improve liquidation as well as customer satisfaction.
Typical Challenges for the Call Center Model
The traditional debt collection call center model faces multiple challenges. Because of their commission compensation model, collection agents often use aggressive tactics on the phone, pushing for an immediate lump sum payment, or a short-term installment option to speed payment. Even if the consumer picks up the phone at all (which in today’s smartphone culture is becoming far less likely), they feel pressured and may commit to a plan they simply can’t afford. The result is an installment plan that breaks, many times after the first payment, and consumers often charge back the phone payment because they felt antagonized about being pressured to begin with. The call center cost structure also cannot afford to support highly customized plans with irregular payment schedules, missing out on another segment of consumers. All of these add up to a significant disadvantage given today’s consumers and their financial needs.
Flip the System on It Head with a Machine Learning Based Approach
The modern approach to debt collection is omnichannel, digital-first, consumer-centric and leverages data and experimentation to determine the best course of action based on consumer preference and behavior.
TrueAccord’s system communicates with consumers automatically through a wide range of digital channels, including email, text and social channels. And because it’s digital-first and fully reactive to consumer behavior and preferences, it’s a far less aggressive, much more personalized collection environment that delivers superior results when competing with call centers. Historical data collected over several years, combined with machine learning algorithms that evaluate individual behavior and preferences, enables this highly targeted and personalized treatment. Two to three email interactions per week serve as a baseline, with added channels in support and reactive communications responding to consumer interactions when needed.
This approach is also highly collaborative, focused on educating consumers and treating them the way they want to be treated. When they’re ready to commit to a plan, they just view payment options online and choose the one that makes the most sense. The result is higher liquidation rates in the long run, higher payer rates, and higher consumer satisfaction that leads to fewer complaints.
Machine Learning Drives the Experimentation Engine
The most important asset in the TrueAccord model is the data collected and analyzed over time that enables us to accurately predict what messages people respond to, what payment offers work best, and for which type of consumer. This complex data-driven system is part of our DNA and entails a lot of moving parts that allow us to truly understand what resonates with each consumer.
The driving force behind the system’s ever evolving performance is an experimentation engine that allows us to test various scenarios to see how collection processes work and how they can be improved. Since digital-first channels are highly instrumented and offer real time tracking on our website, we can learn in short cycles and continuously improve. To launch an experiment, we establish a hypothesis we want to test, monitor what’s happening in the conversion funnel at each touchpoint, see how each product or plan is being used and where consumers are dropping off. Even when an experiment fails, we learn from the data and make future iterations in a continually improving system. We partner strategically with our clients to customize experiments for their product lines and make experimentation-based optimization an ongoing process.
A few sample experiments:
Aligning Payments to Income
The number one reason payment plans fail is consumers don’t have enough money on their card or in their bank account. Our hypothesis was that if you align debt payments with paydays, consumers are more likely to have funds available, and payment plan breakage is reduced. The experiment tested three scenarios: one as a control, one defaulting to payments on Fridays and one where consumers used a date-picker to align with their actual payday. After testing and analysis, we found that the date-picker approach worked best, lowering breakage without negatively impacting conversion.
Self-service Payment Experiences Reduce Costs and Breakage
Consumers with debt often can’t always predict when they’ll be paid or how much. Our hypothesis was that by allowing them to self-service their payment plans and make modifications along the way (based on changes in their lives), we would reduce the need for interaction and improve the customer experience while reducing breakage. This experiment was also a success, reducing breakage rate, and also lowering call rates because before its launch, consumers had to call to change their plan. By making the desired functionality readily available, we were able to increase payment plan success rate and save agent time.
Even Failures Are a Learning Experience
One hypothesis we tested was that customers that dropped off our radar after not choosing a plan could be enticed to sign up for a new plan if offered longer payment plans. After sending texts and emails based on their behavior, we found that new sign ups simply didn’t materialize by just offering longer payment plans with referring to the consumer’s specific life situation. The offers had a high open and click rates, but not sign ups. This indicated that we were on the right track but needed to iterate and come up with an alternative solution.
An experimentation engine allows every company to test their own hypotheses to see if their customized solutions work or not. A digital-first, highly instrumented experience allows us to run dozens of experiments concurrently, learning from each experiment so we can progressively improve our experience and results. Even when experiments fails, they unearth insights that can be used to improve performance next time as part of follow on experiments. In the world of debt collection, testing and continuous improvement means better results in the long run.