Unlocking Hidden Data Treasures through Payment Flow Analysis

Everything flows, especially in digital business. The next supplier, the next contact is just a fingertip away. Trust is a precious but also volatile asset. Winning customers and building a lasting relationship with them is a real challenge for many companies, particularly in digital times. How do you get to know customers better without ever meeting them? How do you correctly assess their needs, wishes, and current life situation? Without personal interaction, the points of contact needed to make the right offer at the right time seem to be missing.

The new perspective: Where personal contact is missing, data helps Digitalization is changing not only the interaction with the customer, it also opens up a new way of gaining knowledge about them. It lies in customer data, especially payment data. After all, hardly anything reveals more about a person than their economic footprint. Every single transaction tells a story. This story can be traced and developed further. Someone who makes a down payment on a car often needs car insurance. Someone who books a trip may need travel cancellation insurance. And someone who buys a baby starter kit is likely facing a whole mountain of previously unimagined needs. Analyzing and specifically evaluating payment transactions makes it possible to develop new approaches for value-added services and interactions. New business models can also be built on this. Banks and financial service providers in particular can benefit, but so can insurers, energy suppliers, and other service providers. Since PSD2, third parties have also been allowed to access the account data of bank customers – provided that they consent. And contrary to some expectations, this is more the rule than the exception: Even in data-sensitive Germany, around two thirds of customers already give their consent to data use if it brings them tangible benefits. Payment service providers at an important crossroads of data streams Payment service providers sit at a central point in these data streams. By processing electronic payment transactions at the point of sale (POS), they often even have two points of access. If used intelligently, this payment transaction data, as generated at the physical or virtual place of purchase, delivers valuable insights on both the consumer side and the merchant side. The potential of payment stream analysis is therefore enormous. Billions of transaction records are generated every day and can be used to get to know consumers' needs better, anticipate their requirements, or develop value-added services for merchants. A prerequisite for this, however, is that this data can actually be made usable. Crucial: The right algorithm for data analysis The key factor for successful payment stream analysis is artificial intelligence (AI). Given the volume of data to be processed, only AI-based and therefore self-learning algorithms can detect the relevant patterns quickly enough and actually turn data into knowledge. To predict customers' purchase preferences, for example, the payment streams are evaluated along defined parameters and categorized by the counter-account and the payment purpose. Spending at a discount store or an organic supermarket is identified, as is spending on sports, travel, or education, and intelligently linked accordingly in order to derive statements about the future.

Including all transaction data sources is crucial for a 360-degree view of the customer. While usually only the payment transactions from the girocard system are considered, Acceleraid enables its customers to link the data from the GiroCard network operation with the data from credit card usage and evaluate them together. This also applies to payments made via smartphone, since the actual payment is made via a virtual card stored in the wallet and is therefore a normal card payment. Integrating the credit card is also important because it is used above all in the growing online retail sector for purchases of 100 euros or more, while the GiroCard is preferably used for cashless payment at the POS. Only by bringing both transaction streams together can accurate conclusions be drawn about the customer's current life situation – and thus ultimately better customer engagement, more tailored offers, and additional impetus for interaction.