Marketing Predictive Analytics

Executive Summary:

The Dataset is from a Major retail firm that sells children's clothing.

 

Strategy is to increase profits through:

1. Market segmentation
2. Demography and Sales Analysis
3. Elasticity modeling and Market Basket analysis 

Visualization areas:

 1.Intro

• Contains snippets of different segments.
• Segmentation is done through a tool called Latent Gold. K-means clustering could also be used,but since this method has arbitrary Cluster and seed inputs, chose Latent Gold which provides segments based on Probability scores.

2.Demography

A detailed analysis of different segments based on

• Age, Income, Education & gender
• Mode of Communication
• Location
• Added an interactive segment slicer

3.Sales

Analysis of product Sales for different segments using the following Visualizations:

• Top 5 Products by Revenue
• Revenue by month and Percent Change
• Sales vs Units by Year - Added a play axis containing Year
• Sales vs target

4.Analysis:

An interactive slicer containing different segments. Please note that this page makes sense only when a segment option is selected.

Price Elasticity Analysis:

• Select a Segment and a Product to view Elasticity characteristics. Suggested price change and predicted revenue are displayed.
• Analysis is done using Linear Regression with SAS. An alternate way to do this is by using an R script with Power BI.

Market Basket Analysis:

• Find which products to combine to get the most out of the segments.
• Analysis is done using Logistic Regression with SAS and Product affinities are displayed for every segment.

5.Challenges faced:

• To pick which variables to segment on. Zeroed in on important behavioral and product attributes to achieve this.
• Since Original Dataset had 220 features, choosing which ones to visualize required significant data munging.
• Price Elasticity Analysis was done for 21 products each in 5 segments.
• Market Basket Analysis involved performing logistic regression for 20 different products in every segment.
• Involved Python code, power queries and pivot tables in Excel to bring data to a clean format.

 

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