In today’s world, connecting with consumers requires more than just making a phone call or sending a standard email, especially in the realm of debt recovery and collection. Navigating through the various strategies often feels like wading through a sea of acronyms and buzzwords. Terms like AI, machine learning, and data science can quickly become overwhelming or even feel interchangeable, leaving you unsure of what they actually mean and how they affect your business and bottom line.
To help clear up the confusion, we’ve put together a glossary of key terms, definitions, and examples to help you make sense of it all:
- Artificial Intelligence (AI): AI is a broad field, which refers to the use of technologies to build machines and computers that have the ability to mimic cognitive functions associated with human intelligence.
- Big Data: This term means larger, more complex data sets. Big data can save collectors a lot of time by using many variables for analytics-based customer segmentation, insert, insert.
- Champion/Challenger: This model of A/B testing is a method for comparing the performance of a current strategy, agency, or software (the champion) to an alternative solution in the same category (the challenger), often used in debt collection to evaluate two agencies or service providers.
- Dark Patterns: A dark pattern (also known as a “deceptive design pattern”) is a user interface or flow that has been crafted to trick users into doing things. Although dark patterns are often considered to be intentionally deceptive, poor design can inadvertently result in an unintended dark pattern.
- Data Science: A cross-discipline combination of computer science, statistics, modeling, and AI that focuses on utilizing as much as it can from data-rich environments. Data science (which includes machine learning and AI) requires massive amounts of data from various sources (customer features such as debt information or engagement activity) in order to build the models to make intelligent business decisions.
- Data Mining: Data mining describes the process whereby you dig through data to discover hidden connections and patterns, and then use this data to predict future trends. Most often it uses a combination of machine learning and artificial intelligence and is very much related to Big Data.
- Digital Engagement Metrics: A range of key performance indicators (KPIs) that capture how effectively digital channels are reaching and engaging consumers.
- Digital Opt-In: The percentage of users who have indicated their preference to receive digital communications in a particular channel.
- Digital Opt-Out: Percentage of users who have requested to be removed from a specific communication channel or all lists owned by the sender. Opt-out requests can be relayed through a variety of words or phrases beyond the standard “STOP.”
- Domain Reputation: Domain reputation is the opinion receivers—including mailbox providers, ESPs, and other service providers—have of your domain, which helps them decide if your emails should make it to a recipient’s inbox instead of being rejected or ending up in a spam folder. Domain reputation is a key factor for email deliverability rates.
- Email Deliverability Rate: Deliverability, or inboxing rate, divides how many emails reach the recipient’s inbox, as opposed to their spam folder, by the total number of emails sent. Your deliverability is influenced by a variety of fluctuating factors, including Internet Service Providers (ISPs).
- Email Delivery Rate: Email Delivery Rate refers to the successful transmission of an email from the sender to the recipient’s mail server. It is the measurement of emails delivered divided by the number of emails sent. Bounces (when an email gets rejected by the mail server for any reason) and failures will impact this number.
- ESPs: Email service providers (ESPs) are a service that enables businesses to send emails and email campaigns to a list of subscribers.
- Generative AI: AI that creates new content. (Images, Text, Sound, etc.)
- ISPs: Internet Service Providers (ISPs) provide internet. Although ISPs can provide email services, separate ESPs are often used for business email operations—but ISPs play a major role in email delivery and landing in the recipient’s inbox.
- Machine Learning (ML): Machine learning is a subset of AI that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, machine learning uses algorithms to analyze large amounts of data, learn from the insights, and then make informed decisions.
- Mailbox Provider: A mailbox provider provides email hosting and implements email servers to send, receive, accept, and store email for the recipient.
- Mail Server: A mail server (also known as a mail transfer agent or MTA) is an application that receives incoming email from the sender and forwards outgoing messages for delivery to the recipient.
- MMS: An acronym that stands for Multimedia Messaging Service, similar to SMS, that allows for multimedia content like images, videos, and audio, along with longer text messages.
- Multi-channel: Multi-channel communication refers to the use of multiple, separate communication channels to reach a consumer—such as email, text messages, phone calls, letters, or even self-service portals—but each channel operates independently from the others, and there is little to no integration between them.
- Omnichannel: Omnichannel communication goes a step further than multi-channel communications by integrating all available channels to create a unified engagement strategy. Whether the debtor is engaging with a text message, phone call, email, or self-service portal, the consumer’s journey flows smoothly across these channels.
- Open Rate, Click-Through Rate: The percentage of users who are actually opening and clicking digital communications.
- Predictive Analytics: Predicting outcomes is one specific application of machine learning. It allows companies to predict which accounts are more likely to pay sooner and allows them to better plan operations accordingly.
- SaaS: Software as a Service (SaaS) is a cloud based technology that uses the internet to deliver an application which is owned, managed and developed by an external party. Normally run on a subscription basis, the software is usually not installed on the user’s device.
- Scalability: In debt collection, scalability is the ability to handle more debt and customers while maintaining efficiency and cost-effectiveness of the operation, often involving or determined by the utilization of employees or third-party agencies, the application of different software, optimizing processes, and adopting new technologies.
- Self-Serve: A self-service or self-serve portal is a secure online platform or application designed to empower consumers to make payments and, ideally, allow them to manage their accounts and payment terms independently.
- SMS: An acronym that stands for “Short Message Service” referring to text messages on cellular devices, a key channel for today’s debt collection communication and consumer engagement strategies.
- Tech Debt: Often tech debt refers to the unnecessary reworking or refactoring of code, design, or implementation due to prioritizing speed over quality of work.