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.

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.

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.

 

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.

 

To download a transcript of this post, click here.

Sending Emails At Scale

By on March 13th, 2018 in Industry Insights, Product and Technology
TrueAccord Blog

Sending emails to millions of consumers is a hard problem. Sending them to millions of consumers in debt is harder. Learn what makes this a hard problem and how TrueAccord solves it as we scale email delivery to millions of consumers.

To download the episode’s transcript, click here

TrueAccord’s 2018 Customer Survey: Net Promoter Score and Digital Trends

By on February 27th, 2018 in Company News, Product and Technology, User Experience
TrueAccord Blog

 

We just posted our 2018 Customer Survey and the results are incredibly interesting.

Consumers in debt are definitely feeling more like TrueAccord customers, giving us a Net Promoter Score of 40, a new record for us and for the industry. We have also uncovered several interesting trends in customer preferences – not new, but definitely eye opening.

Click here to download the infographic summarizing our findings.

Podcast: Creating a Positive Impact in Debt Collection Using Technology and Building Consumer-Centric Experiences

By on January 29th, 2018 in Compliance, Debt Collection, Industry Insights, Machine Learning, Product and Technology

Our CEO, Ohad Samet’s, recorded a podcast with Lend Academy discussing the positive impact technology is creating in the collections space and the need for more innovation. Will discuss TrueAccord’s unique approach to debt collection using data-driven, digital communications to create deeply personalized consumer experiences.  The podcast also covers the current state of the collections industry and where it’s likely headed as regulatory pressure, consumer preferences and compliance requirements converge.  Will cover how TrueAccord is using machine learning to deliver deeply personalized and engaging experiences for consumers while achieving higher recovery rates across various debt types.

Tune in and learn:

  • The state of the debt collection industry today and where it’s headed
  • How the use of machine learning is personalizing the debt collections experience for greater conversions
  • Why code-driven compliance outperforms traditional collections practices by reducing risk to organizations
  • How understanding consumers’ preferences for easy, self-service options with flexibility empowers  more consumers to pay off their debt and get on a path to financial health 

If you’re rather read the transcript, download it here.

How TrueAccord Thinks About Experimentation

By on October 16th, 2017 in Data Science, Industry Insights, Machine Learning, Product and Technology, Testing

Experimentation in the movies sometimes gets a bad rap – you think of mad scientists blowing up labs or aliens arriving to probe unsuspecting humans or accidental AI monsters. It leaves the imagination to form an image of experimenters as cold-hearted, calculating and removed from reality. Real world experimentation is typically much more mundane, but the stereotypes often linger. This is unfortunate. The primary goal of experimentation (if you’re not a mad scientist) is: Does this thing work like I think it does? Does this feature deliver the results or benefits it is supposed to? If not why?  This makes it an extremely powerful tool for designing products that work and are actually good for customers.

At TrueAccord we believe that experimentation is an integral part of designing a product that fulfills our mission toreinvent the debt collections space by delivering great customer experiences that empower consumers to regain control of their financial health and help them to better manage their financial future.Whenever possible we launch experiments, not outright features. This strategy has three main and essential benefits:

  • Tests our instincts are right or our models are functional

  • Allows us to gain valuable insights into who our customers are and what they need

  • Mitigates potential negative effects

Test Our Instincts: How do you ensure your team is actually moving the product forward? Only investing energy in features and experiences that will create an effective and positive debt collection experience? Experimentation. The TrueAccord team is full of clever people with clever ideas, but we know it’s important not to found our product on untested hunches. By testing our instincts before taking another step in the same direction, we make sure we invest energy where it matters and wait to develop our knowledge base before proceeding in directions we clearly do not yet understand.

Customer Insights: Understanding why your product works is often more important than understanding if it works. The real benefits of an experimentation infrastructure are in its ability to provide diversified and descriptive data as well as the emphasis on stopping to take a look. At TrueAccord we know it’s essential to understand if we’re looking at the problem the right way and if not what we’ve missed: Do we understand our customers’ needs?

Example:

We launched a new “better” email format that we rolled out as a variation across a spread of existing email content. After a 3 month run, we asserted that it was indeed performing significantly better in terms of both average open and click rate. This was surprising. We hadn’t changed anything that should have affected opens.

New base template content saw an open rate increase of ~10%!        First Email: New base template and Second Email: Control

Upon further investigation, we realized that the new format unintentionally changed the email preview from displaying the start of our email content to consistently showing a formally-worded disclaimer! We then launched another experiment to ensure our findings were correct.

Mitigates Negative Effects: It’s easy in any industry to get blindsided by simple outcome metrics, especially in debt collection where the end objective is repayment. At TrueAccord we would consider it a failure if our product worked, but it worked for the wrong reasons – if our collections system converted, but didn’t provide a good experience for the consumer. Experimentation is our first wall of defense against treading down this path.

Example:

After researching existing accounts, we realized there was a need for more self-service tools in payment plan management. We developed a new payment plan account page and rolled out an experiment that automatically redirected some customers to this page any time they viewed the website while their plan was active.

We found that this did decrease payment plan breakage and increase liquidation, but because our system was set up to detect other types of impact we discovered it also increased outreach to our engagement team in the category of “Website Help”. Consumers were confused as to why they were not landing on the pages they expected upon navigating to our website. We had the right idea, but our implementation was not ideal for the consumer.

Experiment vs Control: % of inbound engagement team communication by category (total # of inbound communications was approx. the same) 

Experimentation is not foolproof, getting these benefits comes from having an infrastructure that allows you to assess if what you built is useful and, if designed correctly, understand why. Indeed, through experimentation, we’ve grown our product to function effectively over diverse areas of debt and over the past few months alone improved the number of people who complete their plans by almost 4%, with a few simple experiments. Every small change compounds, and at TrueAccord’s scale, this means many more people who pay without experiencing any disruption. !  Check back soon for how we designed an experimentation structure that allows us to reap the benefits described above and fuel our collections product forward.