Customers are the lifeline of any business and a steady in-flow of customers is one of the biggest indicators of a business doing well. As rewarding as attracting new customers is, it is also hard work and expense. A more effective solution is customer retention – it is not only a cost-effective formula but also makes for loyal customers who are more likely to turn into brand ambassadors than the new ones. To retain customers, it is important to track them and understand their churn/attrition propensity. The ones with a higher risk of churn must be targeted to implement retention strategies upon. Customer churn prediction for retail businesses is one of the most important and challenging problems for retail companies worldwide. Customer churn models and metrices can help businesses improve their customer retention rates and increase profitability.
About the Client
The client is an American online grocery delivery service provider and operates in 30+ cities in the United States.
To help companies in the US retail sector to excel and reduce customer churn, our team of customer analytics experts have curated a detailed guide to analytics in retail. Request a FREE proposal to know more.
The Business Challenge
The client was facing a sharp increase in customer churn over the past six months and wanted to devise a model to predict and avoid it. The client also wanted to accurately segment their customers so as to create personalized recommendations to engage with and grow the different segments effectively. The approached Quantzig to leverage customer churn prediction for retail businesses to predict and reduce customer churn.
On collaborating with Quantzig, the client signed up for a transformational journey of customer churn prediction for retail businesses. Our experts bifurcated this engagement in three stages. The first stage focused on forming accurate customer segments and the allocation of a marketing budget. The second stage allowed the client to gain actionable insights by deploying a data visualization framework. The final stage included deriving customer churn models that helped in understanding the segment with the highest risk of churn and allowed the client to focus their customer retention strategy on this segment.
Analyzing the troves of consumer data to predict customer churn rates is cumbersome, but it can be analyzed smoothly with the right in-store customer analytics solutions. Request a FREE solution demo to get a glimpse of our customer churn prediction solutions portfolio.
In order to build a sustainable online retail business, online retailers must focus on reducing churn rate, thereby increasing customer retention.
The key outcomes of this collaboration were –
- The client was able to reduce customer churn rates by 8% in a span of one year
- The client subsequently observed a 25% increase in profitability
- The client successfully segmented its customers basis their churn risk to create personalized engagement models.
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