kweyuchesa/Market-Segmentation-Using-Geospatial-Analysis-in-Python

PROBLEM STATEMENT: Visualize various market segments, and identification of indicator variables that influence sales patterns through time in Kenya. # Market-Segmentation-Using-Geospatial-Analysis-in-Python PROBLEM STATEMENT: Visualize various market segments, and identification of indicator variables that influence sales patterns through time in Kenya. Based on patterns discovered from the sales data. We have some variables (indicators) for creation of an index based on variables that you think influence sales patterns. Use whichever datasets you find relevant in the folder. Customers in any setting/industry are typically associated with a location. In the banking world, these locations are usually branching. Sometimes the relationships are explicit - the customer has a pattern of regularly visiting the location. Understanding the characteristics of your market area such as consumer demographics and consumption pattern helps you better understand who your customers are and how to effectively reach them. The type of questions we would like to answer around doing a segmentation analysis on a customer data set are: I. Which market segments are important? II. How can micro markets around these customers be defined? III. Which micro markets yield the best opportunities for revenue growth? (Identify target markets) IV. Where can we find more of these potential markets? Based on the above pattern you will discover from the sales data. We have provided some variables (indicators) for creation of an index based on variables that you think influence sales patterns. Use whichever datasets you find relevant in the folder. You are also allowed to enrich your data from other sources.