Churn Prevention
Customer Company Size
Large Corporate
Region
- Europe
Country
- France
Product
- DSS
Tech Stack
- Predictive Analytics
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- E-Commerce
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Replenishment
Services
- Data Science Services
About The Customer
Showroomprive.com is a leading e-commerce player in Europe. Founded in 2006, the company has over 20 million members and hosts about 15 flash sales per day, attracting over 2 million visitors. In 2014, Showroomprive generated €480M in business volume, representing a 40% growth compared to 2013. The company was facing a challenge with customer churn and wanted to refine its client qualification process to anticipate, prevent, and reduce churn rates.
The Challenge
Showroomprive, a leading e-commerce player in Europe, was facing a challenge with customer churn. The company was using static rules to trigger marketing actions, which were common to all customers and did not take into account the individual value of each client. This approach was not effective in preventing churn and improving customer loyalty. Showroomprive wanted to refine its client qualification process to anticipate, prevent, and reduce churn rates. The company aimed to detect clients with a high potential of no longer buying from the website based on individual purchase rates and refine the targeting of marketing campaigns for each potential churner to improve customer loyalty.
The Solution
Showroomprive used DSS to develop a solution that predicts whether or not a buyer will return to the website to make a purchase. The solution automated the integration and enrichment of a variety of data sources, including customer data, order and delivery data, and web logs. It created more than 690 features derived from this data depending on variables such as clicks on sales, orders, litigation, customers, and more. The solution also tested multiple machine learning algorithms to achieve the best predictive model. This approach allowed Showroomprive to internalize all of the work revolving around this solution, from R&D to production.
Operational Impact
Quantitative Benefit
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