The collections industry continues to expand its digital footprint as growing consumer preference for digital channels combines with stricter regulations on call volume and call rates. Digital communications are standard today, but a key law passed in 2014 by the New York State Department of Financial Services of New York limits third-party collectors’ abilities to connect with consumers via email.  We’ve seen the impact that digital communications can have on people’s lives, and you can help your fellow New Yorkers by sending the governor and your local official an email using the template at the end of this article! The law (23 NYCRR 1) Many existing collections laws are rooted in the Fair Debt Collection Practices Act (FDCPA) from 1977, long before emailing, text messaging, and direct voicemail technologies existed. In an age of growing prefernece for digital communication, New York’s 2015 law—§ 1.6 of 23 NYCRR 1—states that collectors may only contact consumers via email if they have: Voluntarily provided an electronic mail account to the debt collector which the consumer has affirmed is not an electronic mail account furnished or owned by the consumer’s employer; andConsented in writing to receive electronic mail correspondence from the debt collector in reference to a specific debt. A consumer’s electronic signature constitutes written consent under this section.  Shortly after the law took effect the New York Department of Financial Services compiled a list of answers to frequently asked questions. You can review them here. These laws were put in place to protect consumers from collectors excessively emailing them, but consumers are not required to opt-in for debt collectors trying to call them on the phone. In the State of Collections 2019 report published by TransUnion and Aite Group, one collections industry leader said that “right-party contact has fallen off a cliff,” and for many debt collectors, this means that their existing call-based strategy is suddenly becoming unviable.  On the other hand, we’ve found that consumers provide their email address opting into electronic communications with their creditors. In fact, 95% of accounts placed with TrueAccord come with an email address provided with the placement file. Of those we reach with our digital-first strategy, 65% of them open at least one email, and 35% click at least one link to begin the process of repayment. When debts go unpaid, some creditors and collectors turn to legal action, and New York is suffering a resurgence of lawsuits since the passage of the 2015 debt collection law. In fact, 2017 saw a 61% increase in debt collection suits according to the New York State Unified Court System. In other states across the country, TrueAccord has seen dramatic growth in consumer debt repayment using email and other digital channels as the primary mode of communication.  At TrueAccord up to 96% of accounts are resolved without speaking to an agent and nearly one-third of users prefer to manage their accounts outside of the “presumptively convenient” hours (8 am to 9 pm) legally outlined by the Fair Debt Collection Practices Act. Consumers understand the ease of this digital management system and regularly share their positive experiences with a digital-oriented collection strategy. Here are a few: I liked that the email system was used rather than phone calls. I found it easy to use, and it helped me to gather information, figure out a plan, and get the bill paid. It was a small balance, but during this time, it seemed bigger to me. Thank you for your service.This was the best way for me to take care of my outstanding debts since I’m always on the road. Thank you for taking your time with me and not blowing up my phone!TrueAccord has been friendly and helpful, and your systems are always up and running for me to use. You should be proud! The power of digital communication Digital channels give people the power to access and manage their debts on their own time without having to work directly with call-center agents. Moreover, it provides greater consumer protection by providing a paper trail of debt communications, unlike aggressive phone calls that consumers most likely wouldn’t be able to record. The more hassle-free options that folks have to pay, the more likely they are to get out of debt and avoid aggressive call-and-collect agencies. We want to encourage New Yorkers to make their preferences for easily accessible digital channels to be heard. Pay off your debts on your time, not on an emotionally charged phone call or in a courtroom.  Reach out to Governor Cuomo by clicking here with the template below and make your voice heard. Once you've sent your email, share this information using #CollectWithoutCalls and let the governor’s office know that digital is easier for everyone! Email template The following text may be used as a template for reaching Governor Cuomo or other elected officials in your state. Please replace any content in the parentheses with your own information. Subject: RE: 23 NYCRR 1 Dear Governor Cuomo, My name is (your first and last name) and I am a (family member/service provider/advocate/community member) who resides in your district. I feel that 23 NYCRR 1 concerning debt collection by third-party debt collectors and debt buyers places an undue burden on consumers in debt. It limits the ease and efficacy of digital communications and gives priority to intrusive and aggressive call-and-collect agencies. I prefer to use email and the internet to manage my own finances, and permitting 3rd-party collectors to email me directly (if / when) I am in debt gives me the ability to manage my accounts on my own time rather than at the collector’s discretion. Please read here for more information about consumer preferences and see the movement on social media. #CollectWithoutCalls Sincerely, (Your name) (Your city)

As more consumers shift away from physical stores and toward subscription services, online storefronts, and digital banking we have seen a growing number of chargebacks in the eCommerce space. In order to accommodate this, some companies even add costs associated with chargebacks to the price of their products (it’s estimated that for $100 in chargebacks, you are actually losing out of $240). Other companies, unable to recoup losses themselves, send chargebacks to collection agencies, and they may be waiting for too long. Here are three things your eCommerce business can do to better understand chargebacks and mitigate your losses. Analyze why the chargeback was filed Not all chargebacks are fraudulent. Consumers may file a chargeback due to a billing error or an unauthorized purchase was made on their card. Doing an in-depth analysis of accounts prevents potentially damaging your own reputation with a consumer by sending them to collections without first seeking resolution. This also provides you with an opportunity to then reach out to your customers and understand the reasons they renege on their payment so that you can possibly prepare for similar situations in the future. This also enables you to create a larger chargeback strategy. Being too lenient or allowing chargebacks to go unmanaged will leave you at a loss, being too strict may alienate otherwise loyal consumers, and investing time and money into smaller or more resistant accounts may lead to worse losses. Developing a routine for who to reach out to and how to reach them based on historical behavioral data can help you to best recoup your losses before needing to send accounts to collections. Start a conversation with your consumers These communications can open more details than internal analysis. A consumer may have provided incorrect measurements for a garment or accidentally order 200 of something when they meant to order 20 and prompted a service dispute. You can work to maintain a relationship with the consumer and protect your brand. Chargebacks aren’t always driven by malicious intent, and you might be able to resolve the issue together. Connecting with your consumer and resolving the situation is a best-case scenario post-chargeback. If you reach them and they continue to dispute the charge, cite your reasons and offer them an opportunity for response. When you are supported by analytics, you have valid grounds to inform them that the chargeback will be sent to collections; ensure that you have documented your communications with them and send the account to your debt collection partner. Find a debt collection agency before you think you’ll need one The collections industry is often seen as a last resort for eCommerce teams, but waiting until your financial situation feels dire means that the collectors you hired are pressured to perform (and behave) aggressively and quickly.  These collection strategies are often the foundation for the negative stigma surrounding the account recovery industry, and working with a debt collection partner that supports your brand and can collect chargebacks at a controlled, even rate provides you more flexibility. A more gradual effort can help you maximize your returns and protect your business. Digital-first collection strategies can not only support but can help build your brand reputation! Read more about how they can help. Partnering with the right collector can also, counterintuitively, help to prevent accounts from being sent to collections. The negative stigma of the collections industry extends to the minds of consumers, and many consumers fear the prospect of their accounts ending up in collections. Whether this is due to the potential damage to their credit scores or their perception of traditional call-and-collect debt collection agencies, simply having a collections partner can help to reinforce your valid claim to payments before your recovery specialists ever see the account. Chargebacks can be damaging to a business and catastrophic to small businesses. By effectively tracking them, understanding how to talk to your customers about their chargebacks, and recognizing when to escalate the issue to a partner company, you’ll be prepared to protect your eCommerce business.  Ready to find the right partner for your team? Connect with us and learn what we can do to help!

Building scalable technology requires constant evaluation and improvement. Experimenting is defined by trying new things and creating effective changes that help teams to make informed decisions around product development. Trying new things creates momentum, and organizations that are driven by experimentation turn that momentum into growth. Machine learning and artificial intelligence support large-scale, concurrent experimentation that helps these technologies to improve upon themselves. With the right tools in place, you can test a variety of scenarios simultaneously. For example, we use our systems to track changes in the collection process and better understand how our digital collections efforts can be improved. Since digital-first channels offer thorough tracking and analysis, including real-time tracking on our website, we can learn in short cycles and continuously improve our product.  This kind of frequent experimentation helps to avoid making product development decisions based on untested hunches. Instead, you can test your instincts, measure them carefully, and invest energy where it matters. Machine learning drives the experimentation engine Aggregating historical data and processing it using machine learning algorithms and artificial intelligence helps you to understand their effectiveness. Regardless of how intelligent your learning algorithms may be, waiting to test and expand your knowledge base before marching blindly ahead can make or break the success of your product. To launch an experiment, we follow these steps:  Start with a hypothesis that you want to testAssign a dedicated team to manage the experimentMonitor the performance of the test as it is guided by machine learningIterate B2B companies can benefit from partnering directly with clients to customize experiments for their unique product lines in order to make experimentation-based optimization an ongoing process for both new and existing business. Keep in mind that the goal of product optimization is not always jumping to the finish line.  Understanding how your product works ultimately offers you and your customers more value, but it’s easy to become distracted by positive outcomes. Effective, scalable products require intentional design; if you’ve accomplished a goal, but the path there was accidental, taking a few steps back to review that progress and test it can help you to get a clearer picture and grow the way you want.  Below are two sample experiments we conducted to optimize our machine learning algorithms.  Experiment #1: Aligning Payments to Income Issue The number one reason payment plans fail is consumers don’t have enough money on their card or in their bank account.  Hypothesis If you align debt payments with paydays, consumers are more likely to have funds available, and payment plan breakage is reduced.  Experiment We tested three scenarios: a control, one where we defaulted to payments on Fridays, and one where consumers used a date-picker to align with their payments with their payday. After testing and analysis, we determined that the date-picker approach was the most effective as measured by decreased payment plan breakage without negatively impacting conversion rates. By understanding which payment plan system was the most effective, we were able to provide our AI content that offered these plans as options to more consumers and integrate the knowledge back into our systems and track those improvements at a larger scale! Experiment #2: Longer payment plans can re-engage consumers Issue Customers dropped off their payment plans and stopped replying to our communications. Hypothesis Customers can be enticed to sign up for a new plan if offered longer payment plan terms.  Experiment We identified a select group of non-responsive consumers that had broken from their payment plans and sent them additional text messages and emails. These additional messages offered longer payment plan terms than the plans they broke off from. Ultimately, we found that offering longer payment plans, even with reference to the consumer’s specific life situations didn’t lead to an increase in sign-ups. The offers that we sent had high open and click rates but did not convert. This indicated that we were on the right track but needed to iterate and come up with another hypothesis to test. This experiment was especially important because it illustrates that not every hypothesis is proven to be correct, and that’s okay! Experimentation processes take time, and the more information you can gather, the better your results will be in the future. We’re able to simultaneously update our product and continue experimenting, thanks to algorithms called contextual or multi-armed bandits. Here’s what you need to know about these algorithms and how they help! Building the newest, most innovative products feels exciting, but building without carefully determined direction can be reckless and dangerous. By regularly evaluating the effectiveness of machine learning algorithms, you can make conscious updates that lead to scalable change, and experimentation paves the way for consistent product improvement.

Machine learning algorithms are playing a key role in the collections industry’s technological growth. Companies are working to integrate artificial intelligence and machine learning into their strategies in response to changing regulations and evolving consumer preferences. These processes can look dramatically different from business to business! Some technologies are being applied to optimize traditional call and collect strategies while others are building digital-first outreach platforms. Understanding how these algorithms are working for the industry can provide insight into the future of collections.  Business intelligence and analytics Business intelligence platforms are the foundation for the future of collections. They not only help companies understand how to best reach their existing accounts using traditional collections strategies but also integrate into other digital tools to create powerful automated systems.  These algorithms process large sets of data such as call times, call effectiveness, the value of certain accounts, collections rates, and many other variables. By analyzing this information, teams can optimize their outreach strategies by focusing on accounts that are more likely to be collected on, understand what times of day or channels work the best, and even determine what language to use in conversation with specific subsets of accounts.  Portfolio evaluation and exchange By adding a clear scoring system to business analytics tools, teams can share their portfolios in an online marketplace with other creditors and debt buyers in order to buy, sell, and even outsource debts as needed. While debt marketplaces are not new, real-time scoring updates and activity insights provide a dynamic, cloud-based view into a fluctuating market.  Human-like contact center agents As companies evaluate their data and optimize their outreach, they can also integrate digital agents to interact with consumers over the phone. Artificial intelligence software can be used to create human-like voices and personalized experiences for consumers. These platforms can operate at scale more easily than sprawling call centers but still rely on a traditional call and collect model that consumers are shying away from. As consumer preferences shift toward digital channels, more machine learning tools can help to optimize for an omnichannel experience.   Digital collections platforms Digital collections software is able to optimize performance data and leverage it using a diverse, multi-channel communication approach. Phone calls may be included as part of a larger strategy, but these platforms are primarily built around modern consumer channels including email, SMS, push notifications, and direct drop voicemails. Contextual bandit algorithms take channel selection to a level beyond traditional A/B testing. Even if 10% of your consumers prefer one message type to another, it’s important to understand all of your audience’s preferences. Digital channels integrate seamlessly with decision making algorithms and can optimize communications in ways that call systems cannot. For example, digital channels like email can reach consumers outside of hours typically limited by the TCPA.  25% of TrueAccord’s consumers access their accounts outside of the 9am to 9pm when traditional agencies cannot legally reach them. Digital debt collection agencies Each of these implementations of machine learning help to build a more personalized, more focused, and more forward thinking debt collecting experience for both consumers and creditors. One consistent factor that does limit their effectiveness is the need to build them into existing systems or alter processes at scale.  A collection agency that bears the consumer in mind and has a machine learning-driven, digital-first strategy removes this hurdle and enables a full-service, easy to use experience for both companies and consumers. With these technologies built into a team rather than a product or service, digital debt collection agencies can provide the services outlined above alongside a dedicated infrastructure and a team of technology experts.  Choosing the right tools and support for your company’s collection efforts is more important now than ever before, and understanding the options that are available can help you to future-proof your strategy before it’s too late. Still have questions? Our team is happy to help make sense of what a digital-first collections agency can do. Set up some time to chat!

The term “multi-armed bandit” in machine learning comes from a problem in the world of probability theory. In a multi-armed bandit problem, you have a limited amount of resources to spend and must maximize your gains. You can divide those resources across multiple pathways or channels, you do not know the outcome of each path, but you may learn more about which is performing better over time. The name is drawn from the one-armed bandit—slot machines—and comes from the idea that a gambler will attempt to maximize their gains by either trying different slot machines or staying where they are. How do multi-armed bandits fit into machine learning? Applying this hypothetical problem to a machine-learning model involves using an algorithm to process performance data over time and optimize for better gains as it learns what is successful and what is not.  A commonly used model that follows this type of structure is an A/B/n test or split test where a single variable is isolated and directly compared. While A/B testing can be used for any number of experiments and tests, in a consumer-facing world, it is frequently used to determine the impact and effectiveness of a message. You can test elements like the content of a message, the timing of its delivery, and any number of other elements in competition with an alternative, measure them, and compare the results. These tests are designed to determine the optimal version of a message, but once that perfect message is crafted and set, you're stuck with your "perfect" message until you decide to test again. Email deliverability plays a key role in effective digital communications. Check out our tips for building a scalable email infrastructure. Anyone that works directly with customers or clients knows that there is no such thing as a perfect, one-size-fits-all solution. Message A, when pitted against Message B may perform better overall, but there is someone in your audience that may still prefer Message B. Testing different facets of your communication in context with specific subsets of your audience can lead to higher engagement and more dynamic outreach. Figure 1 below outlines how a multi-armed bandit approach can optimize for the right content at the right time for the right audience rather than committing to a single option. Rather than entirely discarding Message A, the bandit algorithm recognizes that roughly 10% of people still prefer it to other options. Using this more fluid model is also more efficient because you don’t have to wait for a clear winner to emerge, and as you gather more relevant data, they become more potent. Multi-armed bandits and digital debt collection Collections continues to expand its digital footprint, and combining more in-depth data tracking with an omni-channel communication strategy, teams can clearly understand what’s working and what isn’t. Adapting a bandit algorithm to machine learning-powered digital debt collection provides endless opportunity to craft a better consumer experience.  Following from Figure 1, digital collections strategies can determine which messaging is right for which consumer. Sorting this data in context can mean distinguishing groups based on the size or the age of the debt and determining which message is the most appropriate. In a fully connected omni-channel strategy, the bandit can take a step back and determine which channel is the most effective for each account and then determine messaging. These decisions take time and thousands upon thousands of data points to get “right,” but the wonder of a contextual multi-armed bandit algorithm is that it doesn’t stop learning after making the right choice. It makes the right choice, at the right time, for the right people, and you can reach your consumers the way they want to be reached. TrueAccord is optimizing how our multi-armed bandit algorithms create the ideal consumer experience. Come learn more about how we collect better!

The debt collection industry is in the midst of rapid change. With the decline of the effectiveness of phone calls and upcoming legislation from the CFPB that includes limiting call volume, it’s more important than ever that your company’s collections strategy diversifies and introduces a digital, multi-channel approach to communicating with consumers. Determining what digital channels work the best for your collection strategy isn’t an overnight decision, and using them effectively is another hurdle entirely. When reviewing potential communication channels, you have to consider how you want to use them, how you plan to scale them, and what the investment will be for doing those things properly. Email According to the State of Collection 2019, email is the most commonly used digital channel used to communicate with consumers in debt (beating SMS text messaging by 45%). Its frequency of use, however, does not mean that it is necessarily being used effectively. Sending manual emails haphazardly can lead to mixed results at best. Trying to send emails at the scale required of a dedicated agency, however, is even more difficult, and poor email management can lead to low deliverability rates, poor domain authority (you may end up relegated to spam folders), and can even end up getting your company’s sending domains blacklisted from reaching any of your consumers. Figure 1, below, shows Debt Collector A's email sending volume. Figure 1 Sending hundreds of thousands of emails per month can seem like an effective strategy at face value, but when deliverability is taken into consideration, that appearance changes. Figure 2, below, mirrors the bar graph in Figure 1 and represents the percentage of the emails sent from Debt Collector A that are delivered to an inbox vs. those that are filtered into a spam folder. Figure 2 A 2019 email client market share study by Litmus shows just how valuable it can be to understand how to work with individual email service providers that all come with their own unique challenges and filters to protect their users. Gmail, for example, maintains 28% of email users, but only 1% of Debt Collector A's emails are reaching Gmail users. Cost Emails can be an effective strategy, but doing so effectively at scale requires extensive infrastructure. That infrastructure includes five major things, including bringing on email experts to work with email service providers, detailed performance tracking, and creating valuable content for your consumers to engage with. Simple email may not cost much, but building a powerful email-driven strategy from the ground up won't be cheap or easy. Emails can serve as the foundation of an omni-channel digital strategy, but creating an ecosystem for consumers to engage at their convenience requires more than one tool. SMS text messaging Smartphones abound, and when Americans are sending roughly 26 billion text messages every day, it’s easy to see the potential in the texting as a collections communication channel. Millennials spend 3X more time texting than calling or emailing, and they hold an average of $4,712 in consumer debt (not to mention mounting student debt) which makes them prime targets for daunting debt collectors hounding them about a balance. This can be intimidating and turn consumers further away from wanting to work with you. SMS allows for fast, direct contact with consumers that are on the move, don’t have time for a phone call, and may have breezed past an email or two. By creating a flexible system with multiple touch-points across different channels, you can create an organic system of contacting consumers rather that gives them the power to contact your team when and where they want. Key uses for SMS: Payment notificationsFollowing up with customers to confirm a payment can help to reassure them that their next step toward financial freedom is done and increases transparency between your business and consumers. Payment remindersEven consumers on a payment plan might forget once in a while. A ping with a text message can be just enough of a nudge to remind them to log in and make their scheduled payment.Providing instant access to their accountBy providing a one-click option for a consumer to make their payment, you can make taking the next step easy! Pairing this option with a simple online payment portal gives consumers the opportunity for a full self-service experience.Tracking your performanceAs is the case with other digital channels, tracking your data and performance is easier than ever with texting. You can A/B test messaging and get consistent results for improving engagement. When you’re considering what to include directly as part of the content of a text message, keep in mind that people expect texts to be short! Length aside, make sure to avoid: Sensitive information (e.g., account balances, credit card information, etc).Misleading informationThreatening consumersHarassing consumers Text messages have a 209% higher response rate than phone, email, or Facebook, and part of the reason for that is that they are digestible and often feel informal and friendly. On the flip side, misleading, threatening, and harassing texts not only deter engagement and damage your brand, they are also illegal. Plus, the CFPB’s proposed rules will give consumers the ability to opt out of text messaging, and your texting numbers can still be blocked manually. Be selective with the messages you send and consider the consumer experience. Getting started with texting using certain software companies can be as cheap as pennies per message. Full-scale agencies like TrueAccord also make use of SMS tools as part of a broader collections strategy alongside other digital tools. Direct drop voicemail Direct drop voicemails (also known as ringless voicemail drops) are a unique channel that can help supplement a digital communication strategy but can’t do much on their own. Rather than an agent calling a consumer directly, a voicemail is delivered to the recipient’s inbox without their phone ringing (hence the name). The consumer still receives a message from a pre-recorded voice that can relay much of the same information that they would have gotten from an agent, but they do not feel the urgent response pressure associated with a phone call. Much like text messages, direct drop voicemails can be used sparingly as a touch point to remind consumers of upcoming payments or ask them to check an email or call an agent back. From a cost perspective, direct voicemail offerings can range from a few cents to a few tenths of a cent depending on the provider, and many companies will charge based on successful drops rather than a flat charge for the volume sent which can avoid costs incurred for out of date or incorrect phone numbers. Both direct drop voicemails and text messages are legally classified as phone calls by the TCPA as the law applies to “placing a call or text to a consumer using the consumer’s mobile number.” Be careful with when and how you decide to use either channel in your collections strategy! As consumer preferences and collections law continue to evolve, we should expect to see rapid growth in both existing digital channels as well as the emergence of others! Effectively integrating these tools into your strategy together can create a much larger impact than any one channel in isolation, and teams that build these systems today will be the future leaders of the industry very soon.

Using email as a channel for consumer communication seems like a simple way to dive into the digital revolution, but internet service providers (ISPs) actively develop tools to combat spam and abuse. You may have the best intentions, but these service providers want to help consumers feel like they are protected which means blacklisting and filtering out junk mail. Unfortunately, emails sent by the untrained email sender can veer dangerously close to junk.  This can make breaking into emailing consumers difficult, but it makes sending emails by the thousands (and millions) impossible without building email infrastructure that is sustainable and scalable. Establishing that infrastructure begins with recognizing the challenges you might face and then considering how to best confront them. Why scaling email infrastructure is difficult Email communication is heavily regulated by automated filters and systems in a way that more manual forms of communication aren’t. Cell service providers, for example, do not have nearly as much control over the volume or quality of calls that their customers receive.  ISPs have dedicated engineers that design algorithms to keep their users happy, engaged, and protected from malicious senders, and an inbox packed with spam mail makes for a poor user experience. These algorithms are not perfect, and when they are designed, they lean on the side of being more restrictive than less which can lead to some misunderstanding. They may accidentally filter out an email from a legitimate sender that, according to their understanding of what is deemed safe, seems suspicious. To make matters more complicated, each ISP has unique criteria that serve as the basis of their filtering rules. An email that is flagged as spam by Google could land safely in a Yahoo Mail inbox and vice versa. These rules are also constantly changing and updating to fight back against more advanced scammers making it impossible to create a one-and-done solution to properly sending emails at a massive scale. Here are just a few things that spam filters analyze that you’ll need to consider: Content: What do your emails say? Do you have any suspicious attachments or links?Design: How do your emails look?Sending time: Did your email arrive at 4pm or 4am?Sending volume: How many of these emails did you send out at once?Sending frequency: How often are you trying to email people?Consumer engagement: Is anyone actually opening/clicking your emails? Working to get all of these answers (and more) right is essential or you might find your email domain permanently blacklisted from one or all of the ISPs that you’re sending to. So what can you do to build a scalable infrastructure and work within these restraints? How to successfully send email at scale As we mentioned above, there isn’t necessarily a single, perfect solution for overcoming the innumerable hurdles to large-scale emailing. It takes dedicated and focused strategy to improve your long term inbox placement rates. Here are a few tips that our team keeps in mind as we continue to grow. Create valuable content The first step to making sure your emails are well-received by both users and ISP filters alike is creating the right content. Well-designed UX and carefully curated text are important, but it’s equally important that you steer clear of some phrases and keywords and trigger red flags. Here’s a list of some spam trigger words that you might want to avoid! Having a dedicated content team gives you the flexibility to create more personalized and more human messages that have a better chance at reaching your intended audience! Talk to experts We know we’ve been thorough, but fully understanding the challenges of sending email at scale isn’t something we can teach you in a few hundred words. TrueAccord has a full team of email deliverability experts on staff that can provide industry specific knowledge and know the ins-and-outs of different ISPs’ requirements.  They also regularly audit our deliverability rates so that we can iterate on our processes and improve and help segment our domains and IP addresses as we grow. Segment domains and IP addresses Thankfully, our email experts can help explain what that last bit means. Segmenting your domains simply means building different domains that you can email consumers from. For example, some of your emails may come from emails@companyA.com and others may come from emails@help.companyA.com. The same goes for segmenting IP addresses; you may send some of your emails from your main office and others from your satellite office. This process can help to limit the risk to your brand’s reputation with ISPs as you are less likely to take a big hit if only one of your many email addresses makes a mistake (e.g. bouncing frequently, receiving a lot of spam complaints, having many of its emails remain unopened).  This process is intricate and methodical. Creating ten new domains can’t solve deliverability problems because brand new domains also lack authority. If an ISP’s filters see that a brand new email address is sending out 100,000 emails, it’s likely that it’ll be swept to the side. Which brings us to our next point! Take it slow Scaling your program too quickly is heavily penalized even among senders with high engagements. Many well-established companies that want to build a large scale email strategy with their existing customer base make this mistake, and sometimes there isn’t a way to fix it. Placing strict limits on email volume growth can help ensure that ISPs don’t flag your domain. Track your data Set your benchmarks, track your performance, and make changes as you go. Data is the life blood of a scalable email program. As you’ve seen, there’s a lot to keep track of, and if any segments of your strategy spring a leak, the ship might sink.  By frequently and carefully monitoring performance—from open and click rates to inboxing rates to bounce rates—you can maintain a full view of your email strategy and make improvements as you build.  No one has the power to flip a switch and send millions of emails per month without risk, but if you build slowly, you can lay the foundation for a successful email strategy. If you have any questions, let us know in the comments below!  TrueAccord sends 40x more emails and has up to 70% higher inboxing rates than other collection agencies. Chat with our team today to learn more about what that means for you!

Machine learning is a powerful tool that many companies can use to their advantage. The ability to have algorithms make decisions based on large scale sets of data enables teams to build efficient, scalable tools. Some of these algorithms require frequent monitoring and management from data scientists in order to get up to speed and continue learning. Others are able to operate and learn on their own in order to generate new information to act on!  Supervised and unsupervised machine learning algorithms both have their time and place. Let’s discuss a few examples, the difference between the two, and how they can be used together to create a powerful, AI-driven strategy for your company! Supervised Machine Learning Supervised learning algorithms are trained over time based on foundational data. This data will provide certain features as data points that will teach the algorithm how to generate the correct predictions. Figure 1, below, provides an example of a binary classifier and a set of data about cats and dogs that will teach the algorithm how to identify one or the other! These models function best in situations in which there is an expected, intentionally designed output. In the example above, the expected output is that the algorithm can properly separate cats from dogs. In digital debt collection, it may be separating accounts that will be easy to collect on from ones that are more difficult.  Classification vs. Regression The models above are both examples of a supervised learning model that is seeking classification, but supervised learning can also be used to build regression models. The key difference between the two is that in a regression model the output is a numerical value rather than categorical. A regression-based model may use input features such as income and whether or not they have children to accurately predict a person’s age. When using a regression based model in combination with consumer data, you can even segment demographics for communication and marketing.  For full transparency we want to state that TrueAccord does not use its customer demographic data for these purposes. This is strictly an example. With proper supervision, these models will become more accurate over time, and the data scientists building them can adjust them as business needs change. Whether you are gathering data using a regressor or a classifier, it is dependent upon the data scientists to build the most effective inputs in order to get the “correct” output. Unsupervised Machine Learning While supervised models require careful curation in building proper features that will lead to the “correct” output, unsupervised models can take large sets of unlabeled data and identify patterns without aid. The output variables (e.g. dog or cat) are never specified because it is now the algorithm’s job to process and sort the data based on similarities that it can identify. Using this method, you can learn things about your data that you didn’t even know! Clustering vs. Association Just as supervised models have primary methods for training their output data as either classification or regression models, unsupervised models can be trained using clusters or associations. Clustering algorithms gather data into groups based on like-features that exist in the data set.  If you have thousands upon thousands of customer accounts in your system, a clustering algorithm can learn using the customer data and form them into distinct (but unlabeled) groups. Once it has assigned these clusters, data scientists can review the output data and make inferences such as: This cluster is all of the accounts that have not yet established a payment planThis cluster is all of the users that started signing up for a payment plan but didn’t finish the process This new data set then provides the foundation for a new outreach strategy! Building the infrastructure to process this data is the hardest part. Learn more about how TrueAccord is laying the foundation for scalable machine learning systems! Association algorithms are the other end of unsupervised learning algorithms. Associations take the idea of grouping random data points one step further and can make inferences based on the data available. Continuing on from our account creation example, an association-based model can identify two data points and draw conclusions based on the patterns it finds. One such pattern may be: A person that signed up for an account the first time they opened an email is more likely to pay off their balance. The algorithm recognizes that multiple steps in a customer's journey creates another data point. Because association algorithms are still unsupervised, a team of scientists will be responsible for labeling the output data, but the algorithm can outline previously unnoticed patterns. The power of teamwork By leveraging both supervised and unsupervised machine-learning algorithms, you can make decisions based on previously unfathomable scales of data. While they cannot necessarily be used to substitute one another, they can be used to create a perpetually improving cycle. Using unsupervised models to extract meaningful information from large data sets and building new supervised models to further hone your data creates more opportunities than ever before.

Everyone knows that customers are the backbone of a business; if people don’t use your service or buy your product then you won’t have a business for very long. In order to solve this problem, companies often work to bring in as many new customers as possible, but you can’t forget to nurture relationships with consumers that you've worked with in the past.  According to Adobe, 40% of eCommerce revenue comes from returning customers which make up only 8% of total visitors! That number alone should inspire you to get out and talk to your old customers and figure out what they think, but there are quite a few more reasons you should cherish customer feedback and use it to strengthen your company! Building brand promoters The omnipresence of social media means that consumers that are excited about your company will shout from the digital rooftops to endorse you. Unfortunately, the power of social sharing also means that the opposite is true: if a person has a particularly negative experience with your brand, they will spread the word around fairly quickly.  Properly managing customer feedback can dramatically improve your brand's reliability. A Net Promoter Score measures customer's satisfaction with a business by asking: “how likely are you to recommend this (product/service) to a friend?” Customers that rate your business at a 9 or a 10 are considered promoters and are your best friend when it comes to spreading the word about your brand.  Maintaining a high NPS score is challenging, but by focusing some efforts on gathering and listening to customer feedback, you can gradually build effective, organic branding that sets you apart from your competition! If maintaining customer relationships is so important, you may be hesitant to try and collect on debts for fear of negative feedback. But digital debt collection solutions can support your brand and your bottom line! Incorporating feedback and iterating Not every review will revolutionize your business. If you take every negative review to heart, you might start to feel a bit down on yourself, but by analyzing customer feedback in aggregate, you’ll start to see patterns emerge! These patterns won’t appear overnight, and even some patterns may not give you the direction you're looking for (it is still your business after all). That said, if you have dozens of customers asking for a new feature or piece of content, imagine how many more customers want the same thing that aren’t asking! By listening to customer feedback and building new tools that your customers are looking for, you can demonstrate that you listen to them and further improve retention. Plus, incorporating these changes into your customer lifecycle can pay big dividends!  Promoters will continue to support your brand, bring in new customers, and in the long run, they will continue to spend more as your brand relationship improves. A survey by Bain & Company shows that customers actually spend more in months 31-36 of their relationship with a brand than they do in the first six months. Creating a self-sustaining system Feedback helps your business to grow and meet the ever-expanding needs of your market. If you don't listen to your customers and build in a vacuum, you may soon realize that you were not solving the root of a problem. This isn’t to say that every customer suggestion or idea is the right one for your business, but if you take the time to listen to your customers you’ll build their trust and might just find the next right step. 

When communicating with debt collectors it's important to ensure they are legitimate before making a payment. Scammers posing as debt collectors will pressure you aggressively, use threatening language, and will not provide any documentation to verify the debt.. When a scammer is attempting to collect a fake debt using an email it’s called a phishing scam. The vague nature of scammer scare tactics combined with the sense of urgency in their communications make for a worrisome case, but if you keep a level head and follow these quick tips, you can protect yourself from phishing scams. 1. Verify the sender’s email address Scammers will often make themselves appear legitimate by operating under a company or other authority figure’s name, but they cannot replicate a sender’s address. For example, if you receive a collections communication from TrueAccord, it will be from one of our company domains meaning that the email address (after the @ symbol) will either read “trueaccord.com" or a related address. Even if you are anticipating communications from a collector (or anyone else for that matter), take a second to review the “From” address confirm that they are who they say they are. And in the case of collections, if they seem suspicious or don't have a company domain, don’t respond to the email or click on any links. 2. Validate but do not click on links Debt collection phishing scams are designed to collect private information—like your credit card number or bank account and routing numbers—by tricking you into providing that data. Some of them are even more malicious and will try to get you to download malware directly onto your computer. Any links provided in the body of the email could redirect you to fake sign-in pages that will share your login credentials with the scammer, payment portals designed to capture account numbers, or even prompt you to download malware that could jeopardize the security of your entire device. In order to check that the links in the email are legitimate, you can hover your mouse cursor over the link to see a link preview, likely at the bottom of your screen with the full URL. Make sure that you do not click when previewing the link, especially if you spotted a suspicious email address. By hovering your mouse cursor over the link without clicking, you can make sure that the link address information matches the information in the email explaining where the link will direct you. 3. Investigate the company If a collector’s information seems accurate, but you don’t recognize the debt the most surefire way to dissuade a phishing scam is to probe more deeply. Look up the debt collection company online see if the company is registered with the Better Business Bureau, conduct a Certified Business Search through RMAI or and email the company’s support team to confirm they sent the message. Like we mentioned above: a scammer’s best friend is an unaware consumer. If the content of the email is legitimate, they will also have a way for you to validate your debt before you pay them a penny. Call, write, or email  the debt collection company directly and request additional documentation Scammers won’t offer additional details because they don’t have it—a company that collects real debt will.  4. Take your time to process the content Scammers know that they don’t have much time to get the information they want. Once a recipient of a phishing email can process the details and recognizes that they don’t add up, the scam is a bust. This is why scammers posing as debt collectors rely on aggressive, manipulative, and urgent language. They may threaten legal action or other types of harm and will stop at nothing to make you pay as soon as possible. Real debt collectors will not resort to these tactics, and many of the actions that these scammers threaten are actually against the law. Don’t let explicit language and threats pressure you into paying; while being in debt has obvious downsides, fake debt does not. By remaining patient and seeing through their smoke and mirrors, you can report the email as a phishing attempt and safely move on with your day. 5. Check for spelling and grammar errors Phony debt collectors are hoping to catch you off guard. Their phishing emails are designed to look professional on the surface, but with a careful eye, they can easily be picked apart. Scammers target distracted, uninformed, and unaware consumers which is why their messages are often hastily thrown together.  This means that phishing emails are much more likely to have typos, spelling errors, and issues with proper grammar. Read the message carefully and remain suspect if a message doesn't make sense or look like they were thrown through a quick Google translate. Stay informed and stay safe It’s easy to feel overwhelmed by debt, and mounting debts from multiple sources can make it feel like you’re in a spiral. Scammers that send phishing emails prey on vulnerable consumers and take advantage of those financial fears, but keep these tips in mind and protect your financial well being.