Common pitfalls of collection strategies: part two of three

Common pitfalls of collection strategies: part two of three

There are multiple reasons for adopting one strategy over the other. Every strategy has built in areas of weakness that cause it to make less money than possible, but all have common mistakes – some shared and some unique. Strategies shouldn’t be stagnant, and as new tools present themselves, strategists can continue to fine tune their strategy and improve returns.

In the following three post series, adapted from our free eBook Building a Collection and Recovery Strategy, we’ll review the top three pitfalls we see with common collection strategies. They are:

  • Under-charging when selling or over-paying when outsourcing
  • Only focusing on a small percentage of customers in an outsourced strategy
  • Losing customer relationships in a sell or litigation heavy strategy

In this second part, we’ll touch on another one of the common mistakes we see: having a narrow focus when collecting.

Traditional debt collection is difficult to scale, and heavily relies on humans making phone calls. Collection agents try to stay away from accounts that require a lot of interaction to recover. Because of the high cost of a call, agencies focus on calling accounts with the highest yield for them. Agents are also human, and humans – especially those making commission – want the home run, not the singles and doubles. Low balance accounts, accounts that cannot currently pay or ones that require a long time to convert will get less attention. That creates underserved segments and lower returns for the lender.

We think the issue is the limited set of tools offered to collection strategists. The low margin problem is inherent to call centers, and most debt collectors are exactly that: specialized call centers. They have no ability to provide the type of flexibility required by a lender that doesn’t have homogenous portfolios with high average balances. A lot of money is left uncollected in the long tail.

Want to use our tools to optimize your strategy? Visit our website to learn more.

Augmenting your debt collection strategy with automation: part one

Augmenting your strategy with automation: part one of three

Automation and digitization offer new tools for the collection strategist, augmenting the traditional building blocks for your debt collection strategy. These new tools, introducing flexibility and sophistication that are usually attributed to other parts of the business, can mitigate common pitfalls.

In this series, adapted from our free eBook Automating Debt Collection 101, we’ll review the three major areas where automation and digitization can boost a collection strategy:

  • Early contacts and improved segmentation
  • Persistent communication
  • Improved customer satisfaction

In this first part, we’ll focus on using automation to facilitate early contacts and improved segmentation.

Automated collections are scalable. This means communicating with all customers as early as possible in the collection cycle, quickly working to resolution with those who can pay, and a more robust debt collection strategy. In traditional call-center collections, up to 50% of meaningful interactions are made within the first 30 days of communication. With an automated strategy, most of that value can be captured in a much more cost-effective manner, in a much shorter time span. No more guessing who to call first because everyone can be contacted at scale.

Further, automated and digital collections create a wealth of data that cannot be gleaned form calls. User clicks and browsing, time and day of activity and more. The data can be used to segment accounts to those who are engaged, those who’ll respond better to phones, and those who should be sold or handled in other ways. It allows much more flexible recall criteria than placing for a set number of months, no matter what happens with the account. This means giving accounts the treatment they need at the right time, improving liquidation as well as cost to collect thanks to the scale of operations.

Want to use our tools to optimize your strategy? Visit our website to learn more.

Why we chose a 7 year exercise window (and other startup thoughts)

Why we chose a 7 year exercise window (and other startup thoughts)

I wrote this post to talk about how we view startup culture at TrueAccord. In a way, it’s also an indirect response to a16z’s post about employee equity. We believe people who worked hard deserve their share, much like investors who put in money do.

Recently we announced to our team that we’re adopting a new policy to let employees who stayed with the company for two years keep their unexercised options for up to 7 years. This is considered very pro-employee (though we think it’s not that extreme) and I want to give context to why we did it – because the move is rooted in our deep beliefs regarding what a startup company should be for its team members.

I started my startup career at FraudSciences (FSC), in March of 2005. It was an amazing ride, ending with a $169m acquisition by PayPal. It gave me a lot – insights, experience, enough cushion to skip a few months of work when I started my next company. Interestingly, though, while being a part of FSC’s success, I never got a good look behind the scenes. I rarely spoke to investors, was not exposed to leadership dynamics, and saw almost none of the sausage making. There was almost no discussion of the “meta” of building a startup. FSC was amazing, but upon starting my own company, I only had what I could learn on my own.

Learning how to Startup together

When we started TrueAccord I had a lot more experience. It helped me and my co-founders align on what we wanted company culture to be like; much as TrueAccord is working to be a force for good in a traditionally problematic industry, we wanted it to be more than a workplace for team members. We wanted it to be a stepping stone, a company that’s not only good to work at but also to be from. One whose team members go on to start their own successful companies, and perpetuate this world view. That seems to be the best way to help our community – train its future leaders, forged in the fire of actual startup making. It also creates incredibly effective team members, committed to the goal not by blind admiration to founders or a cult-like culture but by being actually, tangibly vested in the company building process.

To reach the best learning and vesting environment, we had to overcome three different issues: one, team members often don’t have enough information to understand what’s going on. Two, there’s little to no time to reflect on and re-evaluate strategy outside of a small group of people. Finally, only a small subset of the team typically feels truly vested in the business through meaningful equity ownership.

This is what we did.

Radical transparency

The first step to understanding why things happen a certain way is knowing what’s going on. When an organization is opaque, knowledge accumulation starts being equated with power, and it becomes too easy to spread misinformation. We wanted people to know what’s going on. Keeping operations transparent also keeps leadership honest – when you don’t keep secrets, you become a lot more calculated in your decisions. A great example is compensation: while I don’t recommend that team members share what they make, I’m not worried about them doing so because as a leadership team, we stand behind every compensation decision we make. Being transparent doesn’t mean we never make mistakes – but we own them openly with the team, because that makes us stronger.

We adopted radical transparency (though we don’t find it radical): unless it shouldn’t be shared, knowledge is open to all. We share company progress, financials, thoughts about strategy, reasons for various decisions and more. We discuss them openly in all hands meetings and distribute them internally. All team members know what the founders’ share of the company is, how much is in the options pool, what terms investors got, what our terms are with our largest clients and so on.

Transparency has its limitations. We don’t share personal matters, or legal matters outside of what can be shared. We also don’t overshare details about fickle processes like enterprise sales (outside of major milestones) nor do we share notes from every meeting. These are trinkets that less experienced employee mistake for knowledge. Still, everything meaningful is available, and team members are encouraged to ask questions and be informed.

Thinking about work

Once you have information, what do you do with it? It’s not only knowing what’s happening that matters, but also putting it in perspective. What should our strategy be? What are our market conditions? How should we change going forward? These are important questions that are rarely asked outside the executive team.

It’s important to understand: we didn’t adopt holacracy. We didn’t abolish management. We have clear areas of responsibility with accountability and authority owned by the same person or function. That doesn’t mean that we can’t challenge ourselves to recognize our mistakes as a team and get better.

Our group reflection exercises take shape in a few forums:

  1. A chat channel to discuss industry news and how they influence our industry and company. Anything from changes in the funding environment to how recent case law influences our operations in certain market segments.
  2. Monthly breakfasts where attendees discuss company strategy and raise pressing issues, and monthly sessions with smaller functional group with open agenda.
  3. Q&A sessions with the Leadership team, preceded by collecting anonymous questions, so we can raise the most difficult issues the team wants to discuss.

There’s still more work to do here. I’m happy that no question we’ve been asked, even the toughest ones, was surprising. I always feel prepared to answer tough questions because these are the topics my leadership team struggles with on a daily basis. On the other hand, we have to continue learning how to raise strategic concerns as a team and think about work beyond the daily grind. It all starts from leadership teasing out tough questions by challenging themselves in public. What were our biggest mistakes? Why do we continue working in this or that segment? Once that snowball’s big enough, it starts rolling.

Sharing the upside

You don’t need f-you money to start a company, but “ramen profitability” limits talented potential founders with families and responsibilities from working on their ideas. I didn’t “score” anything big from FSC but it allowed my then co founders and me to approach Signifyd and Analyzd with ease of mind, and refuse the first acquisition offer that came our way. We want that for our team members; the feeling that 1) they own enough of the company, right now, for it to matter for them financially and 2) that they get to keep that value. That means two things:

First, we aimed to be generous with option grants. Our founding team has equal holdings and we wanted team members to benefit from the pool as much as possible, too. I estimate that, after the next round, employees could cumulatively own more equity than founders do, and I think it is healthy.

Second, when someone spends time creating value for the company, we don’t want them to leave without it or feel trapped because they need too much money to exercise their options. That’s why the board decided to let anyone who worked at TrueAccord for more than two years have a 7 year exercise window for their vested options (this is the “Pinterest Model”).

We’re still grappling with ownership, control and upside. Maybe there’s a way to distribute upside in hindsight (after a liquidity event) that optimizes taxes, but doesn’t undermine control for the founders while the company is still running. As an investor, I wouldn’t automatically support a structure that allows founders to give out more equity while maintaining their key holder rights in the company. So it gets complicated really quickly, but I think we’re striking the right balance at TrueAccord given the constraints.

Bottom line

Part of creating a new company is setting its culture. I think culture shows in what you do with and for team members, more than massages or unique color schemes. For us at TrueAccord, turning the company building experience into a learning experience that everyone can benefit from, driving a positive change through the way we do things, is a key cultural component. If you like these ideas, feel free to steal them or come join us (we’re careful with our money and hire for very specific positions).

If you’d made decisions that you believe are helping your company deal with these issues effectively – let us know. We’d love to learn from you!

Common pitfalls of collection strategies: part one of three

Common pitfalls of collection strategies: part one of three

There are multiple reasons for adopting one collections strategy over the other. Every collections strategy has built in areas of weakness that cause it to make less money than possible. Strategies shouldn’t be stagnant, and as new tools present themselves, strategists can continue to fine tune their strategy and improve returns.

In the following three post series, adapted from our free eBook Building a Collection and Recovery Strategy, we’ll review the top three pitfalls we see with common collection strategies. They are:

  • Under-charging when selling or over-paying when outsourcing
  • Only focusing on a small percentage of customers in an outsourced strategy
  • Losing customer relationships in a sell or litigation heavy strategy

In this first part, we’ll focus on mis-pricing your portfolio when debt sales are part of your collections strategy.

When selling debt or outsourcing, the lender’s interface with vendors is almost deceivingly simple. Companies tend to mix high yielding accounts with low yielding ones – and end up recovering less from the former so they can get rid of the latter. That is often the result of a rudimentary segmentation and pricing strategy at the seller. Even when segmenting, collection strategists often settle on a simple champion/challenger model for each segment to get the best price or lowest contingency rate.

This limited model is based on two issues:

  • First, the assumption that collection services are commoditized and don’t offer any unique technique, so price is the only differentiator. If everyone is the same, why segment?
  • Second, there isn’t a lot of data feedback in collections to allow proper behavior-based segmentation. Collection agencies aren’t set up to provide high quality data feedback, and debt buyers will often not want to share. The only mode of operation is to sell or place and forget about the debt for a while.

Want to use our tools to optimize your strategy? Visit our website to learn more.

Sign of the Times: Synchrony Bank Expects More Charge-offs

Sign of the times

News hit this week that Synchrony bank increased its charge-off guidance to 4.5-4.8%. This is expected, as multiple banks expect higher charge-off rates due to the change in credit cycle and deteriorating consumer trust. The “good years” of cheap credit fueled by low (and negative) interest rates seem to be over, and lenders are tightening their belts.

We see that with the unfortunate crunch in interest in Marketplace-style consumer loans, but expect the effect across multiple types of loans. Synchrony, whose portfolio skews towards lower FICO-score customers, may just be the canary in the coal mine. Shares of other large issues tumbled accordingly this week, although not to the same magnitude as Synchrony’s.

The three technology keys to automating debt collection

The three technology keys to automating debt collection

You may have already downloaded our free eBook, Automating Debt Collection 101. This is an excerpt.

Flipping the traditional butts-on-seats model on its head and teaching a machine how to do a human’s job is not an easy process. We’re talking about domain-expert based automation. This is a grueling, operational process of understanding why some people pay and others won’t, and translating it into algorithms that grow with the data they accumulate. To realize the benefits of automation, you’ll need to pay attention to three elements:

Data Infrastructure

The key in this process is defining our key performance indicators. One can’t start this task if data are unavailable, corrupt or fragmented. Most collection teams use a tapestry of systems – for scrubbing bankruptcies, for calling, a mail processing system, a payment processing interface and so on. That leads to a fragmented data store, which makes it impossible to know which actions were taken on a debt and attribute success to any of them. You can’t improve what you can’t measure.

Your first step is creating a unified data store for all your data.

Feedback Loop

Extracting knowledge from domain experts can be frustrating. Often they decide intuitively and cannot explain their reasoning. It takes training, ongoing conversation, and an iterative process to structure their knowledge. The feedback loop includes three steps:

  1. Interviewing your experts: presenting several cases that were successfully converted and those that weren’t, and asking what they have in common.
  2. Implementation: the resulting model is validated against data trends.
  3. Deployment: the model is deployed to your system, and agents can comment on its performance in real time and compare it to the way they would act under similar conditions.

Creating a feedback loop between your agents and data scientists is incredibly important. Without it, your data scientists are guessing, and your agents work without guidance, their knowledge untapped.

Increasing Relevance

The human brain is an incredible machine, and it offers intuitive connections that computers can’t make. Whenever faced with new information, even the slightest addition, the brain recalculates its route and makes new assumption about the person they are interacting with. A machine can’t replicate the brain’s ability but it can mimic it – with some help.

Use your experts’ understanding of a customer’s response to inform the way you send your initial communication, as well as using responses you get from them to inform your next communication. While deploying follow up flows based on browsing patterns, we realized some flows converted up to 7 times better than a regular message.

Find pockets of customers who don’t get personalized treatments and create those responses.

Bottom Line

Consumers are increasingly reliant on credit to fund their consumption – whether short or long term. This leads to defaults, and to debt collection being a part of any business’ tool box. As you grow, using automation or an automated solution like TrueAccord is the right way to minimize your costs while increasing your performance, scalability and customer satisfaction.

Interested to learn more? Pick up our free eBook: Automating Debt Collection 101

On American Banker: CFPB Needs a Rule to Regulate Debt Collection

CFPB

Debt collection is a regulated industry, but as our thought piece on American Banker states, there’s room for more – positive – guidance.

In the last few years, collection suit numbers have soared and the CFPB has responded by closing or fining what they call “lawsuit mills.”

Still, most collection agencies follow the law and will still find a technological way to file large volumes of lawsuits without violating federal measures. Consumers will still end up losing by being subjected to aggressive yet absolutely legal tactics in the collection process.

Read more here.

Two approaches (and a third) to automating the debt collection process

Two approaches (and a third) to automating the debt collection process

Debt collection and account receivable departments often start with one person contacting late customers and evolve from there. Even third party collection agencies grow this way as they get more business. As a result, most collection departments are comprised of large teams of operators trying to negotiate with customers. Data science teams that are tasked with improving performance and profitability usually approach the task in one of two ways: process automation or agent-independent decision automation.

Process Automation is the effort to automate manual tasks done by collection agents, replacing them with an automated process or a self-service portal. This may mean skip tracing, logging payments, or queuing up phone numbers to call. The data science team acquires data sources or builds a process that replaces manual work with automated one, reducing the amount of time an agent spends per case. It’s about optimizing agent time on the phone, making sure that every action an agent takes is a high yield one, while busy work is replaced by some level of automation.

Decision automation means trying to teach a machine how to make the same quality of decision an agent makes in the collection process. For example: how to talk to debtors, what to tell them, how to respond to their issues. Because most agents have a hard time explaining in detail why they made one decision and not the other (they “just know”), often data science teams treat agents as an unreliable source of information. The team determines what they are trying to optimize – for example, right-party contact or the number of calls ending with a payment. They then build models that optimize these metrics, but without asking agents for feedback – only looking at long-term liquidation results.

While both approaches are important and are often used at TrueAccord as well, there’s a third one that often gets overlooked because data scientists and agents don’t interact often: Agent Dependent Decision Automation, or Expert Based Automation.

Interested to learn more? Pick up our free eBook: Automating Debt Collection 101

Industry experts talk progress, offer same old solutions

TrueAccord Blog

We’re all about innovation at TrueAccord, and so were pleased to find this article from AccountsRecovery.net titled “Industry Experts Share Tips On How Agencies Should Modernize“. We were a bit surprised to find old ideas reiterated, with a focus on problems and compliance challenges rather than solutions.

This study was sponsored by Castel, a provider of solutions to call centers; furthermore, the experts interviewed have built successful businesses using call center technology. Therefore the focus on call solutions makes sense. We also understand that the TCPA is vague, that consent is an issue and that consumer attorneys are putting compliance teams on edge. As a licensed agency, we’re in the same boat. However times are changing, and it’s high time that we embrace the change.

What technology solutions is the discussion raising? Mostly solutions to manually dial phone numbers, maybe a way to place a voice mail without a ring. The discussion offers little other options other than the iterated duo: those who don’t believe in technology, and those who deem it too risky.

The school of “technology won’t work”

The collection industry has been around for decades, and many of the businesses that comprise it were started long ago by “old school” collectors. That’s why the following quote didn’t surprise us.

“We’ve looked at technology like online chat interface,” said Christian Lehr with Healthcare Collections in Phoenix. “But we haven’t moved forward because it’s a business decision, not a compliance decision. I’m not sure it is the best way to serve the consumer. Much like with emails or text messages, it can be hard to understand context. And there is a time lag for communication. We may be able to serve the consumer faster on a phone call.”

As a company that uses machine learning to develop hybrid collection systems that collect better than call centers, we understand the sentiment but beg to differ. We also have the data to back this disagreement. Not only are email, text and website more effective for collections (from 30% better to 5 times better for low balance debts), consumers prefer them. More than 50% of TrueAccord’s traffic is from mobile devices; more than 35% of payments are made on a mobile device; 25% of interactions with our system happen in non-FDCPA hours. If this isn’t a “better way to service customers”, what is?

The school of “we need permission”

Collectors have been trained by regulators and lawyers to be very compliance minded. This makes them pessimistic about any new technology that hasn’t been tested by courts and lawyers. We hear a lot of the following from agency leaders.

“The only way we can move forward and success is to embrace technologies that are available to us,” Strausser said. “We should be looking at contemporary means of communication and exploring how to pull the trigger when and we are granted approval.”

We’d like to challenge this approach from two directions.

First, when thinking about texting and emails, compliance minded collectors are worried agents on the floor are going to abuse these new tools. However email and texts can be pre-written, optimized, and sent at exactly the right moment. They actually present a much stronger compliance framework when handled properly.

Second, collectors won’t adopt new technologies without explicit approval form the CFPB, but hold on to old call center technology even though the FTC clearly signals it’s all but forbidden. Is explicit approval, which the CFPB rarely provides, the thing to stop us – or can we have an honest analysis of the FDCPA to show us what reasonably can and cannot be done? Are we holding on to old and challenged technology due to inertia?

Bottom line: progress

There is much to do in debt collection. Consumer expectations, client requirements and regulatory pressure are mounting. The right thing to do is take a hard look at the old ways of doing business, and realize that the days of hiring to fight turnover and living off thin margins are almost over. Technology can help us service consumers at scale, provide great customer service, and get results that are better than anything we’d forecast based on old paradigms. We are excited to partner with some of the biggest financial institutions in investigating this possible future.

 

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.