What is customer outcomes data?
At its most simple level, a customer outcome is a result of knowing if a query to us was associated with a “good” or “bad” event related to fraud that the customer might face. For example, if an ecommerce customer queries our data on transactional orders within their fraud decisioning workflow, they may later learn that some transactions were fraudulent and resulted in chargebacks while others did not. Linking these transactional level fraud-related learnings (i.e., outcomes data) to their original Ekata queries is central to Ekata’s ability to provide highly predictive machine learning features.
Still, as customer success outcomes are defined in many different ways, it is important that we understand how the customer success outcomes are determined, collected, and, therefore, defined.
As previously mentioned for ecommerce fraud prevention use cases, “bad” events may be defined by fraud-related chargebacks. For account opening use cases, where customers query Ekata on account signup, “bad” events may be defined by promotional or rewards abuse, chargebacks, spam, abusive content behavior, and other fraud-related account activity events.
As you can imagine, it can take time before customer success outcomes are known. A fraudster might sign up for an account but not commit fraud on the platform right away. This period between an event and the final outcome is called the “maturity period,” and its length may vary between different types of outcomes. Chargebacks, for example, have a typical maturation period between 60-90 days. Ekata requests that only outcomes that have aged past their maturation period be shared to ensure the quality of the labels.
How do we use outcomes data?
We use customer success outcome data to increase the predictiveness of our machine-learning products. We do not use outcome data to label any person, business, phone, address, email or IP as potentially fraudulent or create any type of approval list. We solely use the data to improve our ability to identify patterns and retrain and refine our models. Furthermore, all outcome data is hashed and encrypted. Raw outcome data will never be disclosed to any of our other customers and cannot be accessed outside of Ekata.
By providing outcome data on a recurring basis, customers greatly improve the efficacy of Ekata’s machine-learning models over time. It’s a unique opportunity for customers to directly improve the products that they use. Our machine learning models improve by learning from the fraud patterns customers are actually seeing and as a result, become more refined and predictive with each model retraining. Our machine learning-based attributes, such as Identity Risk Score and Network Score, thus provide more lift and value to our customer base. These scores also tend to be the strongest Ekata signals that customers rely on to help them identify both “good” and “bad” actors.
How can current customers share outcome data with us?
We offer a programmatic, secure way for customers to share outcomes data regularly with us via our APIs and solutions. After a simple integration, customers can easily send us the outcomes associated with past Ekata queries and risk assessment platforms so they are available for our data science team to use to retrain our models.
Current customers who are interested in sharing their outcomes data with us should get in touch with their account manager to learn more.
We encourage all our customers to share their customer success outcomes data so that they not only benefit themselves but all of our customers in their risk assessment journeys.