Transaction data holds a wealth of insights which can be leveraged to personalise customer offers, experiences and interactions at scale.
Transactions are a valuable source of customer intelligence. Evaluating what, where, when and how your customers are spending makes it possible to predict demand.
By aggregating transactions from millions of customers, banks can see spending patterns, share of wallet, loan affordability, payment method preferences and more. And they can use this information to increase the lifetime value of each customer with precision targeting and efficient marketing.
Each piece of raw transaction data is made up of a complex web of information from different sources that often ends up as a garbled string of superfluous letters and numbers, rendering the data unintelligible.
Trying to wrangle insights from a vast quantity of raw transactions is an enormous challenge for banks. So much so, that marketing teams must resort to using basic demographic data for segmentation, resulting in low conversion rates, missed revenue opportunities and unmet sales targets.
Until now.
Powered by machine learning, gini cleans and structures the unintelligible raw data and enriches it with the full merchant name, category, location and descriptive tags.
This not only shortens the data preparation process from months to minutes, it also gives banks more to work with, making it easier to build an accurate 360-degree view of their customers.
