If you happen to follow business news and industry journals, there’s a good chance you’ve heard about companies aiming to be data-driven through the practice of transaction data analysis. Your company may already be engaging with data gathering and analysis. Data can be useful for pursuing many different goals and can help clarify and improve the business decision-making process. When it comes to fighting chargebacks, data analysis can be an especially potent tool.
Depending on how your merchant website, Point-of-Sales systems, and other platforms are set up, you can source data from many different places. Google Analytics, chargeback management platforms, your sales management tools, gated content, payment processors and gateways, and various other sources can generate data that you can use to empower your business.
Let’s take a look at how you can use data, and especially transaction data, to fight chargebacks. We’ll start by going over some of the basics when it comes to gathering data. From there, we’ll look at how you can approach chargeback and transaction data analysis, and also, some specific things to pay attention to when fighting chargebacks.
Preparing for Effective Transaction Data Analysis
There are many different tools for gathering data. People jumping into data analysis are often overwhelmed by the sheer amount of data flooding in. It can be hard to focus, and you may not even have a clue where you should start. Thankfully, when it comes to chargebacks, the fundamentals are relatively straightforward.
Transaction data offers a strong foundation for chargeback data analysis. When using data to understand chargebacks, and crucially, how you can reduce them, the more you can tie the data to a specific transaction, and thus dispute, the better. Transaction data provided by your sales platform, checkout cart, and chargeback management tools can be especially insightful.
The more you can figure out about individual transactions, and the more transaction data you gather, the better. So, for each transaction, you might seek to gather not just shipping addresses and credit card numbers, but also things like IP addresses, the category of products, the cardholder’s transaction history and shopping habits, and more.
Likewise, the more transactions you gather data from, the better. Data from a hundred transactions will prove more reliable and insightful than data from 10 transactions. Data from thousands of transactions? Even better.
Likewise, long time frames can also yield insights. Ideally, you’ll have data not say just for December 2024, but data from each December since 2010. The best time to start collecting data, by the way, is as soon as possible. So, if you’re not currently systematically gathering transaction data, it’s best to get to work ASAP.
Your payment processing portal may already be collecting some of the above data. You may be able to integrate other tools or otherwise increase data collection through your payment processor. You can also integrate ChargebackHelp’s chargeback platform. Our tools can help you gather and analyze data, which could prove crucial for reducing chargebacks.
Besides transaction and website data, cyber security and fraud detection tools can also provide relevant data as chargebacks are often linked to fraud and potentially cybercrime. Of course, gathering data is just the first step. Once you gather the data, you have to make sense of it.
An Approach to Data Analysis
Data can be powerful, but it can also be difficult to make sense of. Fortunately, practice and a thoughtful approach can go a long way. There are a variety of ways to approach data analytics. One common way is to break down analytics into three categories as outlined below.
- Descriptive Analytics– This type of analytics focuses on the past and describing what happened. So, perhaps it’s March and you’re studying an uptick in chargebacks in January. You might quickly find that these chargebacks were linked to holiday shopping.
- Predictive Analytics– With this approach, you dig deeper, using data to try to predict what could happen in the future. The December uptick in chargebacks discussed above could not only hint at a rise in chargebacks come next December, but around other shopping holidays as well, such as the back-to-school sales.
- Prescriptive Analytics– Here’s where chargeback mitigation really takes root. With prescriptive analytics, you uncover steps and ideas that you can use to reduce chargebacks in the future. This might mean setting up a longer return period around the holidays, so customers can return stuff to your company rather than filing a chargeback if a gift ends up unwanted or unneeded.
You could think of the above as a sort of hierarchy. With chargeback mitigation, the goal is to reduce chargebacks, which means using prescriptive analytics. However, to reach a point where you can start to change the future (prescriptive), you’ll need to understand the past (descriptive), and also, anticipate how things might unfold in the future (predictive).
Some Things to Analyze When it Comes to Chargebacks
Data analysis is immensely complex and there are many opportunities. We could fill up a lengthy book covering the many different data points and insights merchants should watch out for. Ultimately, it’s wise to customize your analysis approach to your company and the unique challenges and opportunities presented.
When it comes to chargebacks, there are some things everyone should pay attention to, including:
Individual Cardholder Profiles
One of the easier ways to use data to clamp down on chargebacks is to look at the profiles of individual shoppers. You might find that a certain cardholder at a specific shipping address is charging an abnormally high number of chargebacks. With repeat chargeback offenders, you may simply want to stop doing business with them.
Issuing Banks
It’s also wise to watch specific card-issuing banks. Issuing banks will give the green light for the cardholder to file a chargeback. The issuing bank will also determine whether or not to approve the chargeback. Some banks may be more generous to their cardholders than others. Other banks may prove more lenient to merchants, or you might find that a particular issuer is more likely to be swayed by a certain piece of evidence, such as shipment tracking data.
Transaction Traffic Data
It’s wise to break down where transactions come from. Knowing the channels and sources that result in the highest amount of chargebacks can help you identify priorities, among other things. You could find that your paid ad data on Google search is leading to higher chargeback rates than organic traffic. If so, tweaking your ad campaigns might help you prevent chargebacks.
You could also analyze traffic based on its geographic source. A merchant based in Canada might find that orders from the United States produce more chargebacks, or vice versa. Perhaps the merchant finds that a certain state, province, or metropolitan area is especially prone to higher chargeback rates. Could variations in banking practices, laws, and regulations from jurisdiction to jurisdiction be involved?
Chargeback Dispute Success Rates
Merchants can fight chargebacks through the representment process. By studying what leads to successful resolutions, you may be able to improve the rate at which you win disputes. You might find that signed shipping receipts are the most effective form of evidence to sway issuers, for example. If so, you may want to require signatures for more deliveries.
Gather, Reiterate, Learn, and Grow
Data analysis only begins, it never ends. The insights you gather this year could be out-of-date by next year (if not next month). Data collection and analysis should be constant and always ongoing. It’s important to regularly study trends new and old to see if you can spot challenges, changes, and everything else. By constantly learning and adjusting, you can stay on top of threats and more quickly recognize opportunities.
Ultimately, the right chargeback tools and an effective chargeback management platform can make it easier to gather and analyze chargeback, fraud, and transaction data. If you need assistance with data analysis and chargebacks, please get in touch.