shawn-y-sun/Customer_Analytics_Retail
Customer Analytics for a FMCG company (K-means clustering, PCA, logistic regression, linear regression)
This project helps retail or fast-moving consumer goods (FMCG) companies fine-tune their marketing and pricing strategies for products like candy bars. By analyzing customer purchase history and demographic data, it segments customers into distinct groups and predicts their likelihood to purchase, choose a specific brand, and buy a certain quantity at various price points. Marketing managers and strategists can then use these insights to set optimal prices and tailor campaigns for maximum revenue.
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Use this if you need to understand different customer segments and forecast how changes in product pricing will impact purchase decisions and overall revenue for your retail or FMCG business.
Not ideal if your business doesn't involve consumer goods or if you lack detailed customer demographic and purchase transaction data.
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Last pushed
Mar 11, 2021
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