Transactions generate insights around purchasing patterns
Data is created whenever a customer uses a digital payment method, either shopping online or instore. In fact, these transactions generate multiple data points – how much was paid in total, as well as for each item, what time the purchase was made and, for transactions made in person, exactly where it took place. Payment providers can also link this information to their detailed information about each merchant, including size of the company, and vertical and distribution channel (i.e., offline or online). Transactions can also be linked to specific customers through credit card numbers or user accounts.
Together this collective information from thousands of transactions, conducted by thousands of customers every day, forms a pool of rich connected data points. In theory, this entire data set can be used in real time, depending on a provider’s technical infrastructure.
Analytics that deepen customer understanding can guide smarter decision-making
Payment providers can create value from this data for both customers and merchants in several ways, depending on the sophistication of analytics used.
1. Descriptive analytics
Find insights in historical data. For example, they may examine data around customers abandoning online checkout, determining whether this is more likely to happen at certain times of day or by specific customer types. These insights can help stores develop strategies to counteract this – perhaps through offering discount codes or directing customers toward payment methods with higher conversion rates.
Another type of descriptive analytics is analyzing customer overlap across merchant verticals, to suggest possible collaborations or just better understand customer behavior.
2. Aggregated data analytics
Examine the behavior and performance of a certain merchant population. This can be used to help individual merchants benchmark against competitors or be sold to third parties. For example, sector-specific insights could be appealing to hedge funds that use real-time economic data to make investment decisions or to advertising firms that are interested in developing more targeted campaigns. Another use case for aggregated data is producing market studies available for purchase.
3. Predictive analytics
Use past data to forecast future events. These insights can help merchants choose the best locations for outlets, optimize staffing or make other business decisions that depend upon knowledge of customer flow or buyer demographics.
Payment providers could also consider how collaborations with others could enrich their data and enhance its value. For example, working with providers of cash registers could combine pure transaction data with information on shopping carts. Another potential use case is in the context of “frictionless finance” offerings, where payment providers partner with banks to enable the seamless provision of financing offerings. Of course, these collaborations would need to be mutually beneficial and potentially include commercial or sales partnerships.
A go-to-market strategy must consider privacy and data protection laws
Once payment service providers have determined how to create value from data, they need to develop these use cases into marketable products. Building a strong go-to-market strategy will include identifying target customers and customer segments, and developing messaging, sales and pricing.
Eventually, the data collection process will need to also offer added value to those that contribute their data, such as merchants and customers. If additional data is collected during the payment process, providers may need to incentivize customers and/or merchants. And, payment service providers must also keep privacy and data protection laws, such as General Data Protection Regulation (GDPR), front of mind. While this is less of an issue when working with aggregated data, analyzing data at a transaction level without consent could have serious legal implications.
Examples of successful data monetization can guide strategies
Payment service providers can learn lessons from other sectors that are using data successfully to either improve existing products and services or offer new ones.
For example, one large online media company used their transaction and customer data to create highly differentiated advertisement offerings in real-time bidding situations. Data about customer demographics and activity allowed the company to offer differentiated prices.
In luxury fashion, an online retailer used transaction and order data to build a financial scoring model of their customers. This was offered as a service to a deferred payment provider and payment partner of the store, which led to an overall decrease of payment defaults.
In another example, an online classified portal uses algorithms to spot when customers move to a new city. This information can be used by business-to-business partners to offer targeted services – for example, special offers from local businesses.
These examples highlight how markets are taking a new approach to how they create value for customers and find new revenue streams in a world shaped by data and technology. For payment service providers, the shift toward a more holistic definition of payment acceptance, including software and financial products, signals that pure payment processing is fast becoming a commodity. Remaining relevant requires redefining business models to add new value. Leveraging data is a first step in that direction and can provide a true differentiator that competitors, such as software providers, are simply unable to match.
Jan Sauerbrey, Associate Director, EY-Parthenon Strategy, and Dr. Christian Wesp, Manager, EY-Parthenon Strategy, are contributing authors for this article.