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In this project, I aim to extract meaningful segments from the customer data of a grocery firm. These segments will assist the company in gaining a better understanding of customer behavior and similarities, enabling them to optimize their products and marketing strategies.

Clearilyfing Problem :

1. Business Objectives:

   • What are the specific goals and key performance indicators tied to customer segmentation?

2. Application Focus:

   • How will the segmentation findings be practically applied—product refinement, marketing enhancement, or both?

3. Product and Marketing Emphasis:

   • Are there specific products, promotions, or marketing areas to focus on in the segmentation analysis?

4. User Behavior:

   • Should the analysis prioritize current or historical user behavior, and are there specific behaviors of interest?

5. Demographics and Behavior:

   • Should the segmentation prioritize demographics, behavior, or a balanced mix of both?

6. Collaboration:

   • Is collaboration needed with other departments, such as marketing or product development?

What data do we have to solve the problem ?

• customer profile data

  • ID: Customer's unique identifier

  • Year_Birth: Customer's birth year

  • Education: Customer's education level

  • Marital_Status: Customer's marital status

  • Income: Customer's yearly household income

  • Kidhome: Number of children in customer's household

  • Teenhome: Number of teenagers in customer's household

  • Dt_Customer: Date of customer's enrollment with the company

• user behavoir data

  • Recency: Number of days since customer's last purchase

  • Complain: 1 if the customer complained in the last 2 years, 0 otherwise

• marketing engagement data

  • Products

    • MntWines: Amount spent on wine in last 2 years

    • MntFruits: Amount spent on fruits in last 2 years

    • MntMeatProducts: Amount spent on meat in last 2 years

    • MntFishProducts: Amount spent on fish in last 2 years

    • MntSweetProducts: Amount spent on sweets in last 2 years

    • MntGoldProds: Amount spent on gold in last 2 years

  • Promotion

    • NumDealsPurchases: Number of purchases made with a discount

    • AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise

    • AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise

    • AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise

    • AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise

    • AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise

    • Response: 1 if customer accepted the offer in the last campaign, 0 otherwise

  • Place

    • NumWebPurchases: Number of purchases made through the company’s website

    • NumCatalogPurchases: Number of purchases made using a catalogue

    • NumStorePurchases: Number of purchases made directly in stores

    • NumWebVisitsMonth: Number of visits to company’s website in the last month

2 - Feature Engineering and Data Cleaning

• Drop null values

• Convert Dt_Customer dtype to date

• Create Dt_Collab column

• Delete outlier (vampires in this case :D) datapoints based on Age column

• Create Age Column (age of each customer in unti 2021)

• Simplifying Education categories

• Simplifying Marital_Status.

• Labeling categorial columns

• Create Children Column (Number of children that customers have).

• Create FamilySize column (Number of family members of customers)

• Create TotalSpent column (The total spents on products (wine,fruits,meat,fish,sweets,gold) that the customer has made during last 2 years)

• Delete outlie datapoints based on Income column

• Create TotalPromotions column (The total promotions user acceped in last 2 years)

• Crate TotalPurchases column (The total number of purchases made by the customer in last two years)

• Create CollabTime column (How long has it been since user registration)

• Drop unneeded columns

• Check Correlation between variables

• Standard Scaling Features

3 - Apply Dmensionality Reduction

• Apply PCA

• Put PCA Output Into a Dataframe

• Visualize reduced dimension Data

4 - Apply Clustering

• Select the right value of k for clustring

• Fit reduced dimension data into Kmeans

• Visualize clusterd data (kemans output)

5 - EDA clusters

• Whats the total members of each cluster ?

• What is the spent of the members of each cluster compared to their income ?

• Members of which cluster accepted more offer in the campaigns ?

• Whats the number of family members of customers in each cluster ?

• Number of children of each cluster ?

• Which way do the members of each cluster buy more ?

• Whats the age situation in each cluster ?

• Number of purchases in each cluster ?

• What are each cluster interested in buying ?

• How is marital status in each cluster ?

• What is the status of each cluster in terms of education ?

• Which cluster showed more interest in discount ?

6 - Profiling Customers & Summarizing Report

Examples :