in the retail
Present in all over the world with more than 450 stores and online stores on the Web, our client is a leading brand in the sale of clothing for the whole family and all tastes with accessible prices.
To survive in the highly competitive e-commerce landscape, our client knew that a great user experience leads to a higher lifetime value. So, with a great customer database, our client requested from us a PoC intended to analyze customer behavior that will play a crucial role in evaluating the lifetime value of customers.
To help segment their customers more effectively, our client provided us with a sample of their customers to analyze and make optimized recommendations based on the results.
Our data scientists analyzed this sample to classify the different groups of customers by age, gender, purchasing behavior, etc. Interested in the buying behavior of their customers, and to identify the most valuable ones, STEPS applied a data-backed method to evaluate each group of customers. The chosen model is the RFM, which stands for Recency, Frequency, and Monetary value. And it’s an intelligent marketing technique used to classify customers based on the purchasing history.
After collecting, preparing the data and analyzing it, the results obtained allowed us to identify the right segments and estimate the customer lifetime value. Also, we were able to identify the most and least profitable customers and make the appropriate recommendations based on that.
As a result, our client can create personalized smart-triggered campaigns to drive conversion and increase their KPIs.
The advantages obtained
from RFM model results are
- An improve in the interactions with customers
- An increase in profits and conversion rate
- An increase in customer retention
- More loyal customers thanks to the personalized marketing efforts
Thank you For reading
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