Text Analytics
Business objective
The organization aimed to implement a text analytics solution to improve event marketing campaign targeting and extend product offers in response to a customer’s real-life events.
Solution | Phases & benefits
Ataccama’s solution brought a tenfold increase in campaign success. Working with three types of data (bank clerk notes, IB transaction descriptions, and card transaction identifiers), Ataccama used text analytics to identify two specific client group recipients for a new product offer from the bank.
- The first group consisted of parents struggling with financial solvency at the start of the school year. Previously, the bank had no way to determine if a client had a child unless they already owned a child-related bank product. Ataccama used keyword analysis to identify which clients had children and their children’s ages. The bank then offered these parents a short term loan that coincided with the start of the school year.
- The second group was a pool of people planning an upcoming vacation, identified by parsing references to hotel reservations, plane tickets, travel insurance, and related topics. The bank extended these clients a new credit card offer for any unplanned expenses that might arise while traveling.
Performance stats
Ataccama reached a total conversion of 6–7%, or a 2x increase over the traditional combination of direct mail and call-center calls. The estimated selling potential concerning half of this group (obtained using the traditional model) was about five for every thousand people. The bank was thus able to increase the success rate of its sales more than tenfold.
Business objective
The organization aimed to implement a text analytics solution to improve event marketing campaign targeting and extend product offers in response to a customer’s real-life events.
Solution | Phases & benefits
Ataccama’s solution brought a tenfold increase in campaign success. Working with three types of data (bank clerk notes, IB transaction descriptions, and card transaction identifiers), Ataccama used text analytics to identify two specific client group recipients for a new product offer from the bank.
- The first group consisted of parents struggling with financial solvency at the start of the school year. Previously, the bank had no way to determine if a client had a child unless they already owned a child-related bank product. Ataccama used keyword analysis to identify which clients had children and their children’s ages. The bank then offered these parents a short term loan that coincided with the start of the school year.
- The second group was a pool of people planning an upcoming vacation, identified by parsing references to hotel reservations, plane tickets, travel insurance, and related topics. The bank extended these clients a new credit card offer for any unplanned expenses that might arise while traveling.
Performance stats
Ataccama reached a total conversion of 6–7%, or a 2x increase over the traditional combination of direct mail and call-center calls. The estimated selling potential concerning half of this group (obtained using the traditional model) was about five for every thousand people. The bank was thus able to increase the success rate of its sales more than tenfold.
