The customer analysis module that we have designed is a valuable tool for retailers seeking to gain insight into the buying patterns and trends of their customers. Our solution gathers and studies critical data such as spending habits, buying patterns, and other relevant information, enabling users to track top-end customers based on their sales performance over different time frames. The module is structured into five sub-modules: Dashboard, Details, General, Irregular, and Problematic Customer.

Dashboard 

The Dashboard provides a quick and concise view of the total customers, new customers, loyal customers, and lost customers, along with their respective contribution percentages to the overall sales. It also presents data on top return stores over two periods, department-specific new customers, and significant customer, site, and salesman-related sales figures. Furthermore, it highlights top customers, sites, sales amounts, and quantities, along with customer details based on site and city.

Open

• Customer Dashboard --> Go to -- > Customer Analysis--> DashBoard

Details Report

The Details report allows retailers to identify their most loyal and valuable customers and develop targeted marketing campaigns to engage and retain them.

Open

• Customer Details--> Go to -- > Customer Analysis--> Details

• Best Customer--> Go to -- > Customer Analysis--> Details--> Best

• Lost Customer--> Go to -- > Customer Analysis--> Details--> Lost

• New Customer--> Go to -- > Customer Analysis--> Details--> New

• Loyal Customer--> Go to-- > Customer Analysis--> Details--> Loyal

• Return Customer--> Go to -- > Customer Analysis--> Details--> Return

General, Irregular and Problematic Customer

 Our solution further enables users to identify the top and problematic customers based on their return history, helping retailers proactively manage customer complaints and improve the overall customer experience.

In summary, our customer analysis module is a robust and powerful tool that provides retailers with valuable insights into customer buying patterns and trends. By utilizing this data, retailers can make data-driven decisions that can improve customer satisfaction, drive sales, and boost revenue.

• General Customer--> Go to -- > Customer Analysis--> General

• General Customer Day Visit--> Go to -- > Customer Analysis--> General--> Day Visit

• General Customer  Bill Value--> Go to -- > Customer Analysis--> General--> Bill Value

• General Customer  Bill Count--> Go to -- > Customer Analysis--> General--> Bill Count

• Irregular Customer--> Go to -- > Customer Analysis--> Irregular

• Irregular Customer Day Visit--> Go to -- > Customer Analysis--> Irregular--> Day Visit

• Irregular Customer Bill Value--> Go to -- > Customer Analysis--> Irregular--> Bill Value

• Irregular Customer Bill Count--> Go to -- > Customer Analysis--> Irregular--> Bill Count

• Problematic Customer--> Go to -- > Customer Analysis--> Problematic

• Most Return--> Go to -- > Customer Analysis--> Problematic--> Most Return

• MTM Return--> Go to -- > Customer Analysis--> Problematic-->  MTM Return