Chargeback Analytics

There’s no denying that data is the key to success in modern business, but there’s a valuable source of data many companies still overlook: chargebacks. Analyzing this data can not only lead to a reduction in lost revenue, but it can also uncover valuable insights that can help a business grow and thrive.

What is Chargeback Analytics?

Chargeback analytics involves collecting, analyzing, and interpreting data from transactions and chargebacks to gain insights into chargeback trends, customer behavior, and operational inefficiencies. Its primary goal is to help merchants understand why chargebacks occur and how they can be prevented or mitigated.

Through chargeback analytics, merchants can proactively identify and address potential issues that may lead to chargebacks. This enables them to take preventive measures to reduce their occurrence.

A well-executed chargeback analytics strategy can also enhance the overall customer experience. Identifying and addressing common customer complaints can lead to increased customer loyalty.

Merchants can also fight chargebacks more effectively by analyzing trends in which disputes are reversed and which are upheld. Even data on legitimate chargebacks can be valuable, as it can often help identify gaps in a merchant’s fraud detection measures.

Types of Chargeback Analytics

There are a variety of analytical methods that can be used to glean insights from chargeback data, but most fit into one of three categories: descriptive analytics, predictive analytics, or prescriptive analytics.

Descriptive Analytics: Understanding the Past

Descriptive analytics serves as the foundation of chargeback analysis. It focuses on the examination and interpretation of historical data to determine exactly what happened and why. By delving into historical chargeback data, you can identify the root causes of disputes, whether they are due to operational issues, customer dissatisfaction, or fraudulent activity.

Predictive Analytics: Anticipating Future Outcomes

Predictive analytics takes chargeback analysis a step further by using data science to anticipate future changes in a merchant’s chargeback rate. This can help merchants be more precise in their financial planning. Predictive analytics can also be used to estimate the impact of any changes a merchant might make that would impact their chargeback ratio.

Prescriptive Analytics: Optimizing Decision-Making

Prescriptive analytics provides actionable insights by recommending specific steps to mitigate chargebacks. This may include fine-tuning fraud prevention measures, enhancing customer service, or streamlining operational processes. By offering data-driven explanations for recommended actions, prescriptive analytics helps businesses understand the underlying rationale and incorporate it into their overall business strategy.

Collecting Data for Chargeback Analytics

The foundation of effective chargeback analytics lies in the collection of accurate and comprehensive data. To gain actionable insights and prevent chargebacks, merchants must employ robust data collection strategies.

Start with the fundamental transaction data, which includes information about each customer purchase.

This comprises details like transaction date and time, order ID, payment method, and transaction amount. This data provides the basic framework for understanding individual transactions.

Gathering customer information is crucial for identifying patterns related to specific customers. This may include customer names, IP addresses, shipping addresses, and locations. It helps in recognizing recurring customers and their purchasing behaviors.

Payment processing data is essential to understand the flow of funds and identify potential issues. Collect information related to authorization codes, payment gateway logs, and settlement details to identify processing errors and discrepancies. Data from fraud detection tools should also be collected for analysis.

Obviously, chargeback data is critical. This includes things like reason codes, representment outcomes, pre-arbitration, evidence submitted, etc.

Aggregating this data from across a variety of different systems can be challenging, but tying together every data point from throughout the lifespan of a transaction can help uncover truths that would otherwise remain hidden.

Analyzing Chargeback Data

Let’s go over a few examples of how a merchant might analyze their chargeback data.

Traffic Source Analysis

E-commerce businesses can break down chargebacks by the source that led the customer to the merchant’s website. Analyzing traffic sources helps pinpoint if certain channels are associated with higher chargeback rates.

Chargeback Lag Time

Tracking the time between the transaction and the initiation of the chargeback can help uncover hidden trends. For example, if a merchant has a 14-day return window and there’s a spike in chargebacks with a lag time of 15-30 days, it may indicate that customers are often filing chargebacks because they’re unable to get a refund.

Issuer Analysis

Because issuing banks decide whether to uphold or reverse a chargeback in representment, it can be valuable to examine these outcomes by individual issuer. If a merchant is seeing a much lower win rate with a particular issuer, it could suggest that different evidence might be more effective in those cases.

Repeat Offenders

By tracking patterns of customers who repeatedly dispute transactions, you can take targeted action against bad actors. Once they’ve been identified, consider implementing proactive measures such as blocking future purchases or imposing transaction limits on those with a history of excessive chargebacks.

Iteration and Improvement

Analyzing chargeback data isn't a one-time task. It should be an ongoing process. Regularly review your data, adjust your fraud prevention strategies, and refine your customer service and dispute resolution processes. This iterative approach is key to reducing chargeback rates over time.

Successful chargeback management demands a holistic approach that encompasses data collection, analysis, and action.

For merchants seeking comprehensive solutions, professional chargeback management companies may offer in-depth analytics tools. These specialized firms provide not only the tools to dissect and analyze chargeback data but also access to a wealth of experience in mitigating risks and preventing disputes. They serve as valuable partners in the ongoing battle against chargebacks, enabling businesses to optimize their strategies and maintain financial stability.

Learn More

Those attending AFP 2023 will have the chance to get an inside look at how two enterprise companies used chargeback analytics to create more effective chargeback management strategies.

In a speaking session entitled From Myth to Reality: The Road to Chargeback Success, Marty Williams of Chargeback Gurus will be joined by Tony Hight of Avis Budget Group and Julie Mingus of Cinemark to share advice and insights for merchants looking to take their chargeback management to the next level. The session begins at 10:30 on Monday, Oct. 23, so mark your calendar, because you won’t want to miss it.