How to Use PSP, Merchant, and 3rd Party Data to Enrich Your Chargeback Representment and to Recover Revenue

The key to recovering revenue lost to chargebacks is to dispute them, and the dispute document – also known as a “representment” – needs to include proof that can reverse the chargeback.

To do this, you need data, or evidence. A simple example can be someone that ordered an item, and the data would show them receiving the item at home, or using it afterwards.  

Effective chargeback management is impossible without the data needed to win disputes. The data serves as the pivot around which the dispute is built – and while it isn’t the only element that determines your result, it certainly plays a major role. Clearly, it is important for merchants to get data collection right, which is why many chargeback processes  require significant data collection efforts. However, as we will explain below, this is not always a scalable approach.. 

This article looks at the types of data, collection challenges, and solutions surrounding data collection for chargeback disputes, including the typical types of data merchants need to collect (and hold on to) in order to have the relevant evidence for future disputes. 

We then explain how Justt uses automation to collect data from internal and external sources and how it builds its representments around that data. 

The Basics: Better Data = Better Revenue Recovery

When cardholders file disputes, you need documentation and data that proves the transaction was legitimate.The evidence needed to win a dispute can vary by industry, reason code, and many other factors, as can be seen in the (simplified) examples below:

As you can see in the table above, you can classify the types of data that can be used for compelling chargeback dispute evidence into three categories:

1. Merchant data is information from the merchant’s own systems. This includes order details, account history, product descriptions, transaction logs. Beyond the transaction basics, additional data points can be other important details about the  relationship with the cardholder – customer interactions, support tickets, product usage, account activity, etc. It’s important to make this data easily accessible, as disputes have tight timeframes. If you use chargeback automation solutions these can be accessed by the solutions to automatically create the representments. 
2. Payment Service Provider (PSP) data comes from your payment processors (Stripe, Paypal, Adyen…). This includes transaction metadata like AVS/CVV matches, 3D Secure verification results, authorization codes, IP addresses, and device information from checkout. While this data is available through the many different PSPs you work with, they might provide the data in different formats, without standardized APIs, etc. Using a chargeback automation solution can help you skip this issue, since it will pull the data from the PSPs and standardize it.
3. Third-party data comes from external sources, which are not directly involved in the transactions. Examples include delivery confirmations from shipping carriers, geolocation services, device intelligence platforms, and identity verification systems. This type of evidence can make a real difference, especially when it comes from sources seen as ‘objective’. 

While data collection isn’t the entire story (as we’ll explain below), making sure you have all the evidence at hand is an important first step. 

Manual vs. Automated Data Collection and Enrichment

Most merchants still rely on manual processes to collect, organize, and enrich chargeback evidence. This often means logging into multiple PSP portals, dealing with various data formats, and managing ad-hoc requests for specific bits of evidence. For third-party data, chargeback teams will often need to coordinate with different vendors (such as carrier companies) – each with their own access methods, terms, and response times.

This process usually works fine with low dispute volumes but falls apart as transaction volumes grow. Each new payment service provider adds another portal or API to monitor, and another set of rules to follow for some categories of disputes. As case types diversify, you need more specialized evidence, making collection increasingly difficult.

Seasonal spikes make matters worse. During Black Friday or holiday seasons, chargeback volumes can jump 30% or more. Manual teams get overwhelmed and struggle to meet deadlines, which can lead to a decline in the quality or types of data being collected.

Taken together, these challenges turn chargeback evidence collection into a major time and resource sink, and eventually lead to revenue losses that could have been avoided.

How we use automation at Justt

In designing our modern chargebacks platform, we aimed to take the data issue completely off merchants’ plates. We do this by automating and streamlining all types of data collection, including from non-standard third-party sources:

• On the data collection side, Justt makes it easy to share merchant data (if the merchant chooses to do so), via CSV upload or API connection.
• For PSP data, Justt will automatically collect data from over 50 payment service providers it integrates with (see here for all integrations), eliminating the merchant’s need to navigate through different portals, manage multiple API connections, or expend effort on standardizing the data received from different providers.
• For third-party data enrichment, Justt maintains partnerships with hundreds of external databases and service providers, allowing us to instantly retrieve essential evidence such as delivery confirmations, as well as data from proprietary sources such as risky IP databases. We also apply our industry-wide learnings to understand which sources are typically helpful in a certain set of circumstances, and ensure we have access to these sources for future cases.

Presenting Data to Drive Decisions

Let’s say you’ve solved all of the above problems – either using an automation solution such as Justt, an outsourced team, or simply by expanding your in-house chargeback response team. Does that ensure you are now equipped to win more disputes?

Not necessarily!

Even if you’ve collected all the data under the sun, that doesn’t mean you’ll necessarily win the dispute. Chargebacks are about persuasion, and issuers typically spend just three minutes reviewing each representment document. In that brief window, you need to present your evidence in a way that immediately establishes legitimacy and addresses the cardholder’s specific claim – with a clear, coherent narrative that contextualizes your evidence and helps the reviewer decide in your favor.

This is one (of many) reasons why generic, template-based approaches to chargeback disputes rarely deliver optimal results. Relying on a pre-defined template will make it difficult to present unique or non-standard evidence in easy to understand ways; you will often end up burying your most persuasive argument or strongest data point in a nondescript paragraph on page 24, rather than giving it the spotlight it deserves.

Justt solves the presentation problem through dynamic arguments – our AI-based mechanism that transforms raw evidence into tailored narratives. Unlike rigid templates, dynamic arguments:

• Apply the data elements which have historically been most effective for similar cases, and ensure they will be reviewed by featuring them prominently 
• Determine the optimal placement, formatting, and emphasis for each evidence type
• Craft logical, persuasive arguments that flow naturally from one piece of evidence to the next
• Adjust language style, layout, and visual hierarchy based on issuer preferences and case specifics

Dynamic arguments are continuously improved through Justt’s machine learning algorithms. Every dispute outcome teaches the system which presentation approaches work best for specific scenarios, creating a feedback loop where your win rates improve over time.

This enables ongoing improvement that manual or template-based processes simply can’t match. While human teams might eventually notice broad patterns, Justt can identify correlations between evidence types and success rates across different issuers, reason codes, and transaction types. In doing so, Justt provides a complete revenue recovery solution – encompassing data collection, enrichment, and presentation.

The Revenue Recovery Advantage

Learn More

• Read more about compelling evidence for chargebacks
• Watch Justt Co-founder Roenen Ben-Ami explain the differences between dynamic arguments and templates
• Talk to a Justt chargebacks expert to find out how much revenue you can recover with AI-powered chargeback automation