Smarter customer data for a leading us tire retail chain
American company of tire retail chains based on the west coast. Its main business is dedicated to the automotive services industry and has more than 400 branches in the United States.
The company needed to standardize his 13 million customers database in order to take better business decisions. The use of different interfaces in each of their branches generated many duplicated contacts and inconsistent information.
Using Data Science techniques, we unified the database creating a single profile for each customer.
We clustered the information by regions using the K-means algorithm, in order to identify the visits to nearby branches of a same client. Data fields were evaluated using fuzzy matching techniques.
This solution allowed a quicker access to client information thanks to processes automation.
In addition, the solution gives insights into customers profile, interests and other useful information to perform segmented marketing actions.