Very Simple Example of Using Data Science in Real-Life Situation (Real-Time Scenario)
Scenario:
The purpose of "Data Science" is to turn the data into actionable insights!
Data collected from the customers of a shop. Each observation represents the data from the client sharing their customer satisfaction or brand loyalty. Let's say you collected 30 observations. Each observation represents a client who shared their customer satisfaction and brand loyalty. Let's say that the owners of the shop had hired you as the consultancy firm to analyze customer behavior. Dividing the shop's customer base into groups of individuals with similar traits is a great way to reduce complexity and come up with ideas on how to serve these customer groups better and of course win their business in the long run. Now you are a Data Scientist who was hired to do this task!
Insights & Further:
The data set that we have got is already loaded in the variable data. A good preliminary step of most of the analysis is to visualize the data and examine it. One of the better tools to do that is a "Scatter Plot". How many groups of points can we see here? There are two groups standing out. In data science, we would normally call these groups as clusters.
So, two clusters can be identified instantly with no machine learning whatsoever.
One represents people with low loyalty and low satisfaction and the other one contains all the rest. Our preliminary visual examination shows us that there are some insights we can draw for sure but let's take a more scientific approach.
Most of the time in data science we would want to standardize our data. Next, we will perform some unsupervised machine learning. More specifically, cluster analysis. Using the popular Kmeans algorithm we will identify four clusters.
I can now plot the data using the predicted clusters as colors of the new scatter plot.
We've got the same scatter plot but with four clusters. Our customers have been segmented.
From here, we can distinguish four types of customers and name them. The ones with low satisfaction and low loyalty will be called alienated. Those with high satisfaction and high loyalty are fans. Those with low satisfaction and high loyalty are supporters, and the last ones that are neutral or disloyal but have a high satisfaction, these are roamers.
Using just a few lines of code, we've reached a remarkable result. We have segmented our customers into four different groups. We've applied an algorithm on our data to reach an insight.
Import pandas as pd
Import matplotlib.pyplot as plt
Import seaborn as sns
sns.set()
from sklearn.cluster import KMeans
from sklearn import preprocessing
data = pd.read_csv('Example.csv')
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plt.scatter(data ['Satisfaction'], data ['Loyalty'])
plt.xlabel('Satisfaction')
plt.xlabel('Loyalty')
Text(0, 0.5, 'Loyalty')
Naturally, we must analyze what we see. Data science is about storytelling and making sense of numbers. We have four groups, but only one of them is favorable. The fans. Cluster analysis indicates the problem. Some customers are dissatisfied, others are disloyal. However, we must figure out how to solve the problem ourselves. What are some ideas that data scientists and management will come up with? It makes sense to focus our efforts on turning supporters into fans by improving their shopping experience. Normally, we would have to dig deeper to find the drivers of dissatisfaction for these customers. Maybe it is long queues or unfriendly staff or perhaps high prices. Whatever the reason, we must take actionable steps to fix the issue and make our supporters happier. Simultaneously we can do something else.
We can turn the roamers into fans by increasing their brand loyalty. Loyalty cards, gifts, personalized discounts, vouchers, and raffles are different strategies used to make such clients loyal in the long run. Great. Please bear in mind that in this exercise we missed a few steps along the way. Typing code step-by-step, creating a dendrogram, analyzing a heat map, and finding the optimal number of clusters.
Credit Source Reference: The Data Science Bootcamp by Martin and Ilia.
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