Customer Analysis
with Machine Learning
Case Description:
A commercial sector company struggled to understand customer behavior, predict churn risk, and identify potential transaction fraud. Leveraging machine learning and interactive dashboards, Dataside developed a solution that transformed these challenges into actionable insights, increasing customer retention and satisfaction.
Challenge
The company needed to better understand the purchasing behavior of its customers, identify churn risk patterns and potential fraud, as well as segment the customer base to define more targeted actions. There was a lack of visibility into who the most valuable customers were and which ones were at risk of disengagement.
Solution
Dataside implemented interactive dashboards using machine learning to analyze purchase frequency, revenue, and transactional behavior of customers.
This solution enabled the segmentation of customers into groups such as "Inactive," "Important," and "Ultra," and identified potential fraud in transactions throughout the year. The sales team gained clear insights into which customers to prioritize, preventing the loss of valuable customers and ensuring a personalized approach for each segment.
Benefits
Accurate Customer Segmentation:
Segmentation based on specific characteristics allowed the company to personalize its offers and marketing approaches, increasing customer retention and satisfaction.
Churn Risk Identification
By visualizing risk behaviors, such as decreased purchase frequency or disengagement with communications, the team was able to act before churn occurred, reducing losses.
Fraud Prevention
The identification of suspicious transactions helped avoid fraud and financial losses, ensuring greater security for both customers and the company.
