Big Data has emerged as a buzzword that means analyzing extremely large data sets to reveal patterns and trends. How are people applying Big Data to payments? And how might it affect you?
Data analysis is nothing new. Marketers have been segmenting audiences to craft optimal messaging for decades. The same is going on in payment processing. Visa has used it to identify billions of dollars in fraud.
Like Visa, leading retailers are using Big Data to identify threats and opportunities by marrying payments data with marketing data. Here are three areas that are ripe for continued mining:
1. Reduce fraud and chargebacks.
Cybersource estimates online fraud at around 0.9 percent of revenues. Analyzing chargebacks and fraud is a critical step in chargeback mitigation. To maximize the results, the analysis must be run across dozens of metrics including lead source and campaign data. Some marketing campaigns result in higher chargebacks due to an increase in customer confusion. While in other cases, the marketing campaigns had no relation to the chargeback. Adding in payment information, such as what we know about cardholders—which is typically their name, address, phone, card type, issuing bank, device fingerprint, IP address, and Web browser info—allows for a more robust analysis.
2. Payment attributes as predictor of customer lifetime value.
By combining the campaign and payment data, marketers are able to gain clearer insight into how factors such as the type of card a consumer uses (e.g., debit vs. credit) influences the lifetime value of the customer. Some of our savvy retailers are using this data to more accurately forecast customer lifetime values and more effectively distribute their ad spend.
3. Using payment attributes for real-time decisions and routing.
Many retailers are using payments attributes, or third-party tools that use them, for making real-time go/no-go purchase acceptance decisions. However, there is another world:
Reduce declines. Many larger enterprises are using payment attributes to route the payment to maximize approval throughput. A basic example of this principle is routing to the closest acquirer (if a multinational has an acquiring relationship in Spain and the United States, routing the U.S. customer to the U.S. bank will typically be approved more often). This principle is applied heavily for recurring payments, where declines can account for a significant percentage of churn. Analyzing when best to attempt that recurring charge can yield big returns on investment.
Reduce costs. Pricing can also vary based on card type, transaction amount, etc. Your best negotiated price may vary across multiple vendors. Retailers are routing to reduce costs based on the rules of their contracts (e.g., a deal for debit card processing vs. credit).
Target marketing offers. Some retailers will provide specific offers influenced by the payment attributes. These may be special or limited offers, intended to drive up customer lifetime value for that specific segment.
Analyzing payments is very similar to analyzing marketing data for trends. It can be more complex, because much of payments data is sensitive, needs to remain secure, and can be difficult to access. In some instances, it’s just impossible to do with certain payments vendors.
Where does analyzing marketing and payments data fit into your strategic priorities for the upcoming year?