How TrueAccord Scaled its Email Sends to Millions a Month

By on May 1st, 2018 in Data Science, Engineering and Data, Product and Technology
TrueAccord Blog

Scaling to sending millions of emails a month is a difficult task, and sending debt collection emails is exponentially harder. To prevent spam and abuse, email providers and infrastructure vendors developed tools and tactics that can easily hurt, blacklist, and eliminate not only the “bad guys” but also the uninitiated sender. Still, we scaled to sending millions of emails a month while enjoying high open and click-through rates that allow us to service consumers the way they want to be serviced (we use other channels as well, but focus on email here). We learned important lessons about scale along the way, through trial and error and calculated planning, and we’re sharing them today.

Challenges With Scaling Email

Email is one of the most penalized communication platforms. There are no filters or blockers or spam buttons when receiving a phone call, or picking up a letter from your mailbox, but email is equipped to keep the bad guys out and let the good guys in. ISPs (email inbox providers) design algorithms to keep the user happy and engaged, and an inbox full of spam is not very pleasant. Unfortunately, sometimes the algorithm gets it wrong, and what is actually an email with good intentions from a trusted sender gets filtered through as spam.

To further complicate the issue for email senders, each ISP has a different set of rules and regulations they filter for. What may be an acceptable email in Google is flagged as spam in Yahoo and vise versa. There is no clear rule book to refer to when attempting to scale emails to a very high volume. The algorithms are also always changing to react to real spammer behavior,  further complicating any attempt to create one clear step-by-step process for success.

The signals for spam prevention algorithms touch on many aspects of emails but include content, design, time, volume, and frequency of sending emails, consumer engagement, digital signatures, and many more. Getting everything right is complex, but if you get any of these wrong, you may find yourself indefinitely blacklisted and banned from emailing.

TrueAccord’s Unique Perspective

Operating in the debt collection space further complicates scaling emails. Even if consumers agreed to be contacted via email, they do not necessarily welcome them, leading to lower inbox placements than eCommerce brands. Despite this enormous hurdle, TrueAccord has similar engagement rates to that of eCommerce companies with up to 30% open rate and 14+% click through rates. IT took a lot of work and careful attention to detail to get us there.

TrueAccord uses machine learning algorithms to pick the best email to send to a specific person at the right time in their debt collections process. The team customizes content, time, and frequency of emails, slowly ramping up scale while monitoring performance. In addition, a lot of TrueAccord contact attempts are reactive, made in response to consumer action or feedback. Contacting consumers in context adds credibility and attracts consumer attention while they are still engaged, further improving their response rates. This close attention to detail coupled with engaging content and data driven targeting makes a significant difference. TrueAccord increases consumer engagement and signals to ISPs that its emails are legitimate, creating a virtuous cycle that improves inbox placement and consumer exposure to emails, again improving engagement.

Our Top Tips for Emails at Scale

We’ve polled our product and deliverability experts to offer you our top tips to follow when building a scalable email program. If you follow these you’ll have a better chance to replicate our success and experience engagement rates that will support, rather than hurt, your long term inbox placement.

Create Valuable Content

The most important aspect to scaling email is writing good content that looks reputable and is well designed. It’s important to earn the consumer’s trust and stay away from using words and phrases that trigger spam engines. TrueAccord accomplishes this by personalizing every email, and sending the right email at the right time during the debt collections process, while also passing every email through a robust approval process to maintain quality.

Consult Experts

Because consumer engagement and open rates are a cornerstone of our business practice, we work closely with a team of email deliverability experts and providers. They provide specific industry knowledge concerning each ISP and assist in the warm up strategy for each domain and IP address. Experts help audit deliverability programs as well as deal with ISP-specific challenges and knowhow.

Segment Domains and IP addresses

Utilizing segmented domains and IP addresses allows for growth and scale while limiting the risk to your reputation from a single mistake, which is one of the biggest traps for new email programs. TrueAccord segments email sends to manage sender reputation and distribute potential issues across multiple domains and IP so none of them see too many bounces or receive too many spam complaints, nor have a too high proportion of unopened emails.

Start Methodically and Slow

Scaling your program too early is heavily penalized even among high engagement senders. Most established companies who add an email strategy to an existing customer base make this mistake, which often cannot be undone. TrueAccord places strict limits on email volume growth to make sure ISPs don’t flag our systems.

This is especially important when starting out with a new client. When a new portfolio is added we will send a small group of several hundred test emails for a few days to measure general deliverability and bounce rates. This test cycle provides insight into the appropriate strategy to use for this specific portfolio. If bounce rates are normal, we can begin to send emails freely, but if the levels are higher than expected we’ll utilize high risk mitigation strategies.  

Measure Measure Measure

Set, measure, track. Data is the life blood of a scalable email program because you must track performance in of multiple indicators across multiple segments to detect any developing issue. TrueAccord created smart alerts that highlight engagement, spam issues, email features and other indicators across IPs, domains, receiver domains and several others. Together they provide us with a realistic view of how the program is doing as it scales, and where we may have opportunities for improvement.

It’s taken TrueAccord two years of trial and error and obsessing over data to scale to millions of emails sent each month. Our email scale will continue to grow as our consumer base and business grows, and we are confident that this strategy will support our growth.

How We Created Heartbeat

By on April 24th, 2018 in Data Science, Engineering and Data, Industry Insights, Machine Learning, Product and Technology
TrueAccord Blog

Sophie Benbenek, TrueAccord’s Head of Data Science, discusses the early days of building our machine learning based engine, Heartbeat, and how it has evolved since. Hear about our approach to machine learning, how we move from heuristics to statistical models, and other anecdotes from the early days of TrueAccord.

Designing A Pilot With TrueAccord

By on April 17th, 2018 in Industry Insights
TrueAccord Blog

TrueAccord beats traditional agency performance, and does so by using a machine learning based, digital first system. Since our system learns from individual consumer behavior, it requires specific pilot design to provide the right amount of data for the algorithms to tune themselves. In this episode, our Head of Client Services and Head of Data Science discuss the optimal pilot structure to make the best use of our platform.

Scaling TrueAccord’s Infrastructure

By on April 12th, 2018 in Data Science, Engineering and Data, Industry Insights, Machine Learning, Product and Technology
TrueAccord Blog

TrueAccord’s machine learning based system handles millions of consumer interactions a month and is growing fast. In this podcast, hear our Head of Engineering Mike Higuera talk about scaling challenges, prioritizing work on bugs vs. features, and other pressing topics he’s had to deal with while building our system.

TrueAccord and Legal Risks: 2017 in Review (with 2018 update!)

By on April 10th, 2018 in Industry Insights
TrueAccord Blog

Our CCO and In House Counsel discuss 2017 litigation and complaints trends for the industry in general and TrueAccord specifically. We discuss the reasons why TrueAccord’s legal risk and exposure are so much smaller than the industry’s average.

Bonus addition: TrueAccord’s CCO, Tim Collins, reviewing trends in WebRecon’s January 2018 litigation and complaints trends report.

Conversion At TrueAccord: Tuning A Machine Learning Engine

By on April 3rd, 2018 in Data Science, Engineering and Data, Industry Insights, Machine Learning, Product and Technology
TrueAccord Blog

TrueAccord’s system is machine learning based, but every new product type requires a little bit of tuning to beat the competition. Hear our CSO and VP of Finance in this short podcast about the Conversion Team and what it does to make sure TrueAccord stays ahead of competition.

 

Changing The Direction Of Debt Collection

By on March 30th, 2018 in Industry Insights
TrueAccord Blog

“I think no matter what direction you look at it from, debt collection in the United States is just broken. Because it takes consumers who want to pay, who could pay and turns them into customers that can’t,” noted TrueAccord CEO and Co-Founder Ohad Samet.

As an innovator looking to fix the broken system by using data as a substitute for “draconian collection methods,” Samet’s position on this issue is expected.

But it’s a position he shares with an unexpected regulatory source: CFPB Acting Director Mick Mulvaney.

Read more in this link from PYMNTS.

Using an Experimentation Engine to Improve the Debt Collection User Experience

By on March 27th, 2018 in Data Science, Engineering and Data, Industry Insights, Machine Learning, Product and Technology
TrueAccord Blog

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.

Building An Experimentation Engine

By on March 20th, 2018 in Data Science, Engineering and Data, Product and Technology, Testing
TrueAccord Blog

TrueAccord beats the competition on many levels, and does that through rigorous testing and improvement. Hear a talk from our CTO Paul Lucas and Director of Product Roger Lai on our approach to experimentation.

 

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