Case Study: Mall Customers Segmentation

Girl in a jacket

Table of Contents

Introduction

Companies employing customer segmentation operate under the fact that every customer is different and that their marketing efforts would be better served if they target specific, smaller groups with messages that those consumers would find relevant and lead them to buy something. Companies also hope to gain a deeper understanding of their customers' preferences and needs with the idea of discovering what each segment finds most valuable to more accurately tailor marketing materials toward that segment.

Malls or shopping complexes are often indulged in the race to increase their customers and hence making huge profits. To achieve this task machine learning is being applied by many stores already. It is amazing to realize the fact that how machine learning can aid in such ambitions. The shopping complexes make use of their customers’ data and develop ML models to target the right ones. This not only increases sales but also makes the complexes efficient.

Problem:

Discuss a specific problem based on clustering techniques (Unsupervised Learning). However, the main aim in this project is to discuss the opulent use of machine learning in business and profit enhancement.

Dataset:

Here we have the following features :

  1. CustomerID: It is the unique ID given to a customer
  2. Gender: Gender of the customer
  3. Age: The age of the customer
  4. Annual Income(k$): It is the annual income of the customer
  5. Spending Score: It is the score(out of 100) given to a customer by the mall authorities, based on the money spent and the behavior of the customer.

Souce: https://www.kaggle.com/

Libraries and Data Importation

In [1]:
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
In [6]:
# Importing the dataset
dataset = pd.read_csv('project_data/Mall_Customers.csv')
dataset
Out[6]:
CustomerID Genre Age Annual Income (k$) Spending Score (1-100)
0 1 Male 19 15 39
1 2 Male 21 15 81
2 3 Female 20 16 6
3 4 Female 23 16 77
4 5 Female 31 17 40
... ... ... ... ... ...
195 196 Female 35 120 79
196 197 Female 45 126 28
197 198 Male 32 126 74
198 199 Male 32 137 18
199 200 Male 30 137 83

200 rows × 5 columns

Elbow Method Application

In [7]:
# Annual incomce and spending score columns
X = dataset.iloc[:, [3, 4]].values
In [8]:
# Using the elbow method to find the optimal number of clusters
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
    kmeans.fit(X)
    wcss.append(kmeans.inertia_)
plt.plot(range(1, 11), wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()

Model Creation (K-Means)

In [20]:
# Fitting K-Means with K-means++ to the dataset
kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)
y_kmeans = kmeans.fit_predict(X)

Data Visualization

In [21]:
# Visualising the clusters
# The data(clusters) are plotted on a spending score Vs annual income curve.
plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 50, c = 'red', label = 'Cluster 1')
plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 50, c = 'blue', label = 'Cluster 2')
plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 50, c = 'green', label = 'Cluster 3')
plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 50, c = 'cyan', label = 'Cluster 4')
plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 50, c = 'magenta', label = 'Cluster 5')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 25, c = 'yellow', label = 'Centroids')
plt.title('Clusters of customers')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()

Conclusion

In [24]:
# Visualising the clusters
# The data(clusters) are plotted on a spending score Vs annual income curve.
plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 50, c = 'red', label = 'Careful')
plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 50, c = 'blue', label = 'Standard')
plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 50, c = 'green', label = 'Target')
plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 50, c = 'cyan', label = 'Careless')
plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 50, c = 'magenta', label = 'Sensible')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 25, c = 'yellow', label = 'Centroids')
plt.title('Clusters of customers')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()

The mall customers can be broadly grouped into five groups based on their purchases made in the mall.

The Careful Customer Cluster (Cyan colored) we can see people have low annual income and low spending scores, this is quite reasonable as people having low salaries prefer to buy less, in fact, these are the wise people who know how to spend and save money. The shops/mall will be least interested in people belonging to this cluster.

The Careless Customer Cluster (Red colored) we can see that people have low income but higher spending scores, these are those people who for some reason love to buy products more often even though they have a low income. Maybe it’s because these people are more than satisfied with the mall services. The shops/malls might not target these people that effectively but still will not lose them.

The Standard customer cluster (Blue colored) we see that people have average income and an average spending score, these people again will not be the prime targets of the shops or mall, but again they will be considered and other data analysis techniques may be used to increase their spending score.

The Target Customer Cluster (Green Colored) we see that people have high income and high spending scores, this is the ideal case for the mall or shops as these people are the prime sources of profit. These people might be the regular customers of the mall and are convinced by the mall’s facilities.

The Sensible Customer Cluster (Pink Colored) we see that people have high income but low spending scores, this is interesting. Maybe these are the people who are unsatisfied or unhappy by the mall’s services. These can be the prime targets of the mall, as they have the potential to spend money. So, the mall authorities will try to add new facilities so that they can attract these people and can meet their needs.

Finally, based on the machine learning technique, it can deduce that to increase the profits of the mall, the mall authorities should target people belonging to Target Customers and Sensible Customers and should also maintain its standards to keep the people belonging to Careful Customers and Sensible Customers happy and satisfied.