Helped a B2B Retailer analyze potential customers at street level to increase sales conversion ratio
Product
- Web Crawling/Web Scraping technique
- SVM Classification technique
- Interactive web app
Tech Stack
- R
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Data Mining
Applicable Industries
- Retail
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Replenishment
- Demand Planning & Forecasting
Services
- Data Science Services
About The Customer
The client is a B2B Retailer company operating in the retail industry. The company is involved in trading and distribution and is looking to increase its market share. The company's goal is to add new customers to its portfolio, but it lacks a comprehensive list of customers it is not currently servicing. The company aims to increase its customer base by identifying potential customers at the street level. The company also wants to obtain a mix of existing and potential customers to identify and target the most profitable segments.
The Challenge
The trading and distribution company wanted to increase its market share by adding new customers to its portfolio. The client did not have a comprehensive list of the customers which they were not servicing. The company aimed to increase their customer base by identifying potential customers on a street level basis and obtain a mix of existing and potential customers to identify and target the segments which would be most profitable.
The Solution
The solution involved using Web Crawling/Web Scraping technique to get the exhaustive list of customers available in that region. Variables such as menu price, longitude & latitude, customer group, chain/non-chain and derived variables (distance from the nearest existing customer) were used for further analysis. A highly interactive web app was built using R for easy viewing of existing and non-customers along with magnifier features like zoom in & zoom out. The concentration of both existing and non-customers was analyzed to identify low or high coverage districts and/or streets. A model was built using SVM Classification technique to find the probability score of non customers and categorize them into ‘high’, ‘medium’ and ‘low’ potential customers.
Operational Impact
Quantitative Benefit
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