Question

Prepare a Retail_Shop_Customer Table and explain your understanding by giving sample data on different Customer (CustomID) and strategize for the following using K-Means Clustering.

1. Pricing Segmentation

2. Spend Behavior Segmentation

3. Customer Need Segmentation

4. Category Segmentation.

Answer 1

ANSWER:

Customer Segmentation is the subdivision of a market into discrete customer groups that share similar characteristics. Customer Segmentation can be a powerful means to identify unsatisfied customer needs. Using the above data companies can then outperform the competition by developing uniquely appealing products and services.The most common ways in which businesses segment their customer base are:

Demographic information, such as gender, age, familial and marital status, income, education, and occupation.
Geographical information, which differs depending on the scope of the company. For localized businesses, this info might pertain to specific towns or counties. For larger companies, it might mean a customer’s city, state, or even country of residence.
Psychographics, such as social class, lifestyle, and personality traits.
Behavioral data, such as spending and consumption habits, product/service usage, and desired benefits.

Advantages of Customer Segmentation

Determine appropriate product pricing.
Develop customized marketing campaigns.
Design an optimal distribution strategy.
Choose specific product features for deployment.
Prioritize new product development efforts.

K Means Clustering Algorithm

Specify number of clusters K.
Initialize centroids by first shuffling the dataset and then randomly selecting K data points for the centroids without replacement.
Keep iterating until there is no change to the centroids. i.e assignment of data points to clusters isn’t changing.

60 70 80 90 100 Data This project is a part of the Mall Customer Segmentation Deta competition held on Kaggle. The dataset can be downloaded from the caggle website which can be found here. Environment and tools 1. Bileam 2 seaborn 3. numy 4. pandas 5. metro

import numpy as no import pandas as pd import matplotib.pyplotas plt import seaborn as ons of pd. rend_covl./input/customer-segmentation tutorial-in-python/Mell_Customers.csv dhe do Customerio Gender Age Annual Income (ks) Spending Score (1-100) Male 19 15 Male 21 15 Female 2016 Female 23 16 Female 31 17 df. drop [Customer D axis = 1, inplace=True) pit.figureifigsize(10.60 lt.title Ages Frequency Sns.axes_style/dark sns violinplottyadi Age pit show Artist Alinda

show age18_25= df Agelld Age = 25) & (af Age >= 1811 ge26_35df Agelide Age = 35) & (dt Age >= 260 ge36_45= df Agelia Age C= 45) & at Age >= 361 ge46_55= df Age of Age = 59) & (af Age >= 461 age55above = df. Ageleif.Age >= 56) x = [118-25 26-3536-4546-55-55-] y = [lenlage18_25.values).lenlag Number of Customer and Ages e26_35.values.enlage36_45.valuesleniage46_55.values.enlage55above.values pitfigurefigsize(15.631 sns berpotxex. yy. palettes rocket plttileNumber of Customer and Ages Actate Wir Gated Settings to

pixlabell'Age pitylabell Number of Customer plt show genders = df.Gender:value_counts sns.set_style("darkgrid pit.figure figsize=(10,4%) snsbarplotix-genders.indexy=genders.values) plt.show Female Activate Windo Go to Settings to ad