Segmented respondents based on the part worth data (the output of conjoint analysis) using Ward’s Hierarchical Clustering and K-means. Data used in cluster analysis is the level of importance of each attribute of every individual. Ward’s method can interpret the optimal number of clusters of input data from the dendogram image formed. The second stage is to implement the k-means method to determine the members of each cluster. Logit rule was used to calculate the market share of the new beer product in each of the clusters, so that the cluster with potentially highest market share can be targeted. The logit rule is more apt in consumer products where randomness in consumer choice is prevalent. To target the cluster, consumer data was used to describe the segments, using Decision Trees.

1. MARKETTING Analytics AssignmentMARKETTING Analytics Assignment
Submitted BY- Prateek SingalSubmitted BY- Prateek Singal
Step-1 :Two Stage Cluster Analysis
(Python Jupyter Notebook Encl.)
The cluster analysis performed in two phases: 1) Ward Method, and 2) K-Means Method.
Data used in cluster analysis is the level of importance of each attribute of every individual. Ward method
can interpret the optimal number of clusters of input data from the dendogram image formed .
The second stage is to implement the K-means Method to determine the members of each cluster.
Hierarchical methods, like Ward’s minimum variance method, can determine the candidate number of
clusters and starting point that non-hierarchical methods, like the K-means method, need, while non-
hierarchical methods can provide better performance with the specified information.
Conjoint Analysis -Basic Idea
Humans evaluate the overall desirability of a complex product or service based on a function of the value
of its separate parts (Orme, 2005).

2. Inferring Number of Clusters using Dendogram
● LARGE distance jumps / gaps in the dendrogram are pretty
interesting for us.
● They indicate that something is merged here, that maybe just
shouldn't be merged.
● In other words: maybe the things that were merged here really don't
belong to the same cluster,
●
THe change from 154 to 183 is a jump big enough to be
considered, hence we truncate the dendogram at 155.
●
BY truncating at 155, we obtain 5 Customer Segments.

3. Market Share
of New Product
Rank
Cluster1 0.04% 5
Cluster2 10.34% 1
Cluster3 6.78% 3
Cluster4 2.95% 4
Cluster5 7.08% 2
Total Market
Share of New
Product
4.94%
Step-2:Market Share of New Product in each Cluster
(Calculated using Excel :Sheet Encl.)
● First, we calculated market share of each product for each
customer.
● In applying the logit choice rule we assume that the computed
utility values are mean realizations of a random process, so
that the brand with the maximum utility varies randomly, say
from one purchase situation to the next.
● The Logit Rule is more appt in consumer products where
randomness in customer choice is prevelant.
● According to our analysis, Cluster-2 has max market share for
new product, followed by Cluster-5.

4. Attribute Importance is the Maximum value of each attribute (because the part-worth data is
scaled that way)
Lets look at the Important Atributes for each Cluster , which will also affect the actual Market Share of Existing
and new Product.

5. Step-3:Describing Customer Segments using Decision
Tree Classifier and choosing appropriate segment for
targetting new product.
●
From the Attribute graph in previous slide, it can be shown that
attributes such as Origin, Price, Calories ,Packaging and Glass
have larger utilities for Cluster-2 and Cluster-5.
●
Hence, Respondents belonging to Cluster-2 and Cluster-5 can
be targetted using strategy and Decision Rules obtained from
the Decision Tree Classifier (Image and Code Encl. )
Attributes / New product profiles
Kirin New
Origin
Japanese
Price
5.49
Body
Rich full bodied
Aftertaste
Very mild
Calories
Regular
Packaging
Six 12Oz Small
Glass
Brown Label

6. ●
One rule for Targetting that can be obtained from the decision tree is
●
IF {
●
Knowledge about beer >3.5 , and if {
● Like to travel abroad >3.5, and if {
●
Weekly consumption >4.5 ,and if {
● Income <4.5 (around $45k), and if {
● Dont like to be tied to timetable >3.5 =>More
probability of belonging to cluster-2 customers
}
●
Else if {
● Education >= 5.5 (Post Graduate) => More
probability of belonging to cluster-2 customers}
Inferring Decision Rules For Targetting
● Multiple decision rules can
describe a particular segment.
● All the green coloured boxes in th
decision tree have majority votes
for cluster-2 type respondents.
● All the Purple coloured boxes
describe the rules for cluster-5,
whcih has the second highest
Market Share for new Product.
● With more training data available
and detailed analysis, better
segmentation and targetting rules
can be obtained.

7. ●
One rule for Targetting that can be obtained from the decision tree is
●
IF {
●
Knowledge about beer >3.5 , and if {
● Like to travel abroad >3.5, and if {
●
Weekly consumption >4.5 ,and if {
● Income <4.5 (around $45k), and if {
● Dont like to be tied to timetable >3.5 =>More
probability of belonging to cluster-2 customers
}
●
Else if {
● Education >= 5.5 (Post Graduate) => More
probability of belonging to cluster-2 customers}
Inferring Decision Rules For Targetting
● Multiple decision rules can
describe a particular segment.
● All the green coloured boxes in th
decision tree have majority votes
for cluster-2 type respondents.
● All the Purple coloured boxes
describe the rules for cluster-5,
whcih has the second highest
Market Share for new Product.
● With more training data available
and detailed analysis, better
segmentation and targetting rules
can be obtained.