Description
Overview
A leader in the apparel, uniforms, and safety products industry, faced challenges with data-driven decision-making due to legacy systems with limited analytics capabilities. Promotion effectiveness and product assortment planning relied on inconsistent historical data, leading to inefficiencies. To address these issues, the company partnered with Applexus to implement advanced predictive analytics for customer churn and lifetime value prediction.
Major benefits realized by our client
- Optimized Product Assortment Planning: Reduced revenue leakage from cannibalization and forward buying.
- Enhanced Decision-Making: Improved store profitability through labor utilization insights, price optimization, and supplier performance analysis.
- Improved Customer Engagement: Offered personalized promotions using predictive models and market basket analysis.
How We Did It
- Advanced Data Analytics: Utilized statistical and data classification methods to calculate RFM (Recency – Frequency – Monetary) scores.
- Machine Learning Implementation: Applied decision tree, random forest, logistic regression, and other ML models to predict customer churn and CLV.
- Data Integration: Consolidated sales, invoice, inventory, customer feedback, and NPS data for comprehensive analysis.
- Interactive Dashboards: Designed visualizations for customer churn probability, CLV calculation, high-value customer identification, and customer group analysis.
