Using Data Analytics to Avoid ChargebacksPrepping for the Future by Studying the Past

How Analyzing Previous Chargebacks Can Help You Avoid Future Disputes

They say those that ignore history are doomed to repeat it. That’s definitely true with chargebacks: if you’re not paying attention to what caused problems in your past, the disputes will just keep coming. 

Fortunately, that door swings both ways. Your transaction history is a treasure trove of information. Analyzing that data can help you uncover chargeback patterns, predict future trends, and take preemptive action.

Here, we’ll show how using your own data to make smarter, data-driven predictions allows you to save time, money, and headaches.

Predicting & Preventing Chargebacks With Data

If you could stop a chargeback before it started… wouldn’t you? Well, analyzing your transaction data on a regular basis enables you to do just that.

Uncovering key trends and metrics that may signal chargeback risk can help you put preventative measures in place. Lots of datapoints can be used to detect these patterns. For instance: 

• The time of day a purchase is made
• The velocity of transactions submitted from a single IP address
• A mismatch between the shipping address and the IP location

Any one of these factors on their own would have little meaning. Collectively, though, they can point to possible fraud.

Tools that use an “if-then” approach can be used to automatically trigger flags on high-risk orders, or automatically block a purchase attempt that seems suspicious. But, how do you know you’re not blocking legitimate customers and reacting to too many false positives? This is another area where actionable intelligence comes into play. 

By automatically analyzing key metrics, your system can generate reports that show where or when fraud attacks are originating. You can tighten filter parameters in those areas. Conversely, you might see that stricter rules in a different arena are causing more issues than you thought. Again, the parameters can be adjusted to the need.

What Type of Analytics Do I Need?

Getting actionable intelligence doesn’t have to be overly complex. You can take a variety of approaches to get usable data based on your business:

You can start with simple spreadsheet-based tracking to identify your most common reasons for chargebacks. Using query functions, pivot tables, and simple data manipulation techniques may be enough to visualize your data, develop a strategy, and put you ahead of the curve.

As a growing business with a slightly larger budget, you can consider rules-based software-as-a-service (SaaS) tools to flag suspicious orders. This enables you to move beyond a manual process while avoiding the expense of a sophisticated machine learning model.

If you have the capacity for it, you can leverage machine learning and AI platforms to build a fully-automated fraud decisioning system. This means your system can learn and improve over time, while automatically defusing present threats.

The point is that chargeback analytics can take on a variety of different forms. What works best for you will depend on your business and your needs.

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How to Budget to Avoid Chargebacks

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