Team GSPANN analyzed the client’s customer analytics dashboard and concluded that the reason behind the high number of ‘new customers’ reflecting on customer analytics dashboards is duplicate customer IDs.
The client’s internal customer database system receives data from multiple sources, which includes online and offline transactions made from multiple cards, internal cash-card programs, loyalty programs, etc. These sources generated different profiles for the same customer in the database.
Earlier, these different profiles were not mapped to a single user ID. Hence, whenever the customers made a transaction from a different credit card or source, the system treated them as a ‘new’ customer and assigned a new customer ID.
To resolve this, we came up with a customer IDs matching approach, which involves choosing the most frequently used source to store the customer details, fetch information from it, and map it with all available customer IDs. Later, when multiple matches were found, we fetched the best-preferred customer ID for each customer/profile based on a few rules/priorities set by the client and refreshed the existing data with the newly created data. Apart from the fetched ID, all other IDs were marked as duplicate IDs and were force-matched to the same customer.
The source of data and their respective databases, in terms of completeness of customer information, can be arranged in the descending order – client’s internal cash card, registered account, guest account, loyalty account, and third-party credit cards. We compared the customer IDs within the third-party credit card database (containing the least amount of information) and with the client’s internal cash card database (containing the best available information). Results of the customer IDs matching process are updated on the dashboards once a day and the same data is used by other campaign tools.
