As summer winds down and fall begins, I am reminded of two things: back to school for my kids and the kick-off of the American football season. Both bring me joy. While these two activities are not connected, they’ve connected in my professional world. Recently, I had the opportunity to speak with some folks from a large online retailer and a sports betting/wagering app. The online retailer just finished with back-to-school sales and was being hammered by a particularly high amount of refunds (even higher than expected). The sports betting app was preparing for the new NFL football season and dealing with a huge spike in new sign-up bonus abuse.
Although both of these companies were from very different verticals with completely different business models, it struck me how similar the abuse types that they had to deal with were.
As online companies continue to grow and branch out into new business models, many fraud and trust and safety teams have been asked to take on additional types of abuse hitting the system that go beyond dealing with fraudulent orders and chargebacks. This abuse can take many forms:
- Promo abuse: A person who continues to get $10 off their “first” purchase over and over again.
- New sign-up bonus abuse: A person who signs up to get $100 in free bets over and over again.
- Refund abuse: A consumer who refunds greater than x% of merchandise.
- Referral/affiliate abuse: A partner who repeatedly refers themselves to the platform over and over again to acquire a higher standing or level.
To keep it simple, I classify all these use cases as different forms of policy abuse. Each business has a specific tolerance when it comes to these things so there’s not necessarily an “industry standard” when it comes to how firm or lenient a company should choose to be. Below I’ll cover the consequences of this type of abuse. In addition, I’ll talk through some effective strategies to combat promo abuse, refund abuse, and other types of policy violations.
Consequences of policy abuse
- Lower customer lifetime value: Most of these “new” users/customers are one and done because there’s only one person behind all those accounts.
- Increased customer acquisition cost: Acquiring new customers is difficult (and expensive) for any business. Reducing promo abusers enabled my previous company to reinvest millions of dollars back into the marketing budget. My marketing team was appreciative of this.
- Artificially inflates the company’s user base numbers: At first, seeing the rush of new sign ups can be exhilarating. But the aftermath can feel like coming down from a sugar high. For publicly traded companies in particular, this can also create some difficult conversations down the line with investors.
Best practices on how to fight policy abuse
- Establish clear policies: Any promotion or bonus should have clear rules on who qualifies for the incentive and who does not. These policies should be documented for both internal and external consumption. Ideally, there should be help center articles and prepared responses ready to go before any sort of incentive program is launched. Clear policies should educate customers to avoid crossing any thresholds. If, however, a user crosses a particular threshold and is upset, the help center article can be a useful tool in helping quell concerns.
- Be kind: Oftentimes the people abusing these policies can be loyal and recurring customers, but they just need a little reminder that they should be engaging with the company through one account. When it comes to tone and messaging, try to avoid any accusatory language, but be clear on how users are expected to use the platform.
- Strong cross-functional collaboration and communication: Most promotions probably originate from the marketing team, but it’s up to the customer support team and fraud team to support it and enforce it. Having regular check-ins and setting up joint team goals at the beginning of each quarter can help produce stronger results (and fewer headaches!) for all teams involved.
- Machine learning vs. rules: Most of these abusers tend to be opportunistic (“I’ll take advantage of it if it’s available”) and not professional fraudsters (“I do this for a living”). As such, the bar to dissuade this abusive behavior tends to be pretty low. Machine learning models should not be the primary option here. Rules should be sufficient.
- Link analysis and querying tools: These days, there are various off-the-shelf business intelligence tools that can be used by teams to query data (e.g., Tableau, Snowflake, Looker, etc). Using tools like this, SQL or the Sift Console can help identify policy abusers by shared attributes like IP, device, billing, email, address, number of lifetime refunds, etc.
If you’d like to learn more specific strategies about how your business can mitigate these types of policy abuse, please request a Trust and Safety Assessment from me or one of my teammates.