This Presentation was presented in the 23rd Edition of MRSI, the Annual Market Research Seminar by Aviral Mathur. In today’s competitive landscape, with organizations chasing similar global growth opportunities under challenging market conditions, it becomes imperative to rationalize marketing strategies and optimize spends. Strategic Country Clustering (SCC) is a powerful tool that enables the decision makers to identify homogenous markets for optimal strategy execution. This paper introduces the concept of SCC, its applications, impact on business, and details on the available approaches. However, the focus is on the process followed by us which involves bottom up clustering approach with customization of available ensemble method. AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools

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April 29, 2014
Strategic Country Clusters using Ensemble
Clustering Methodologies

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Agenda
Deep Dive
Approaches & Benefits
Business Need
Impact
Way Forward




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Increasingly complex business environment demands MNCs to…
Homogeneous markets for optimal
strategy execution
Holistic view for robust decision
making
Lessons from similar markets to
shape future growth
Learn
Strategic Country Clustering (SCC)
addresses these challenges
Identify
Develop

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Complexity
Data
Variability
Process
Standardizati
on
Technology
Intervention
People
Expertise
Country
Clustering
Customer
Lifetime
Value
Segmentatio
n
Marketing
Mix
Modeling
Response
Modeling
Brand,
Portfolio
Optimization
Retention
and Churn
Complexity
Tactical Impact Strategic
Marketing Mix Modeling
Response Modeling
Retention and Churn
Brand, Portfolio Optimization
Customer Lifetime Value
Segmentation
Assessment of analytics
applications
HighLow
Country
Clustering
Impact
Tactical Strategic
More Complexity Less
However, SCC is a complex analytical undertaking but delivers high
strategic impact

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To develop SCC solution we assessed two approaches:
2. Bottom Up: Generates country clusters by
aggregating multiple axes solutions
1. Top Down: Generates country clusters by
taking into account all the clustering variables at once
Top Down Approach
Bottom Up Approach

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Top Down Clustering Approach
Economy
Demographics
Usage and Attitude
K-Means Clustering Final Clustering Solution
Top Down Approach

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Challenge
Lack of Business
Alignment
Too Data Driven
However, Top Down clustering approach throws up significant
challenges and its corresponding complexity
Description Complexity
Misalignment with
Business
Data Integration
across multiple
parameters
Information loss Data Structuring
Lack of
Transparency
Black Box Solution
Hard to understand for
clients
1
2
3

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Bottom Up clustering approach addresses afore mentioned
challenges
Bottom Up Approach
Economy
Demographics
Usage and
Attitude
K-Means
Clustering
K-Means
Clustering
K-Means
Clustering
Economy Country
Clusters
Demographics
Country Clusters
Usage and
Attitude
Ensemble
Clustering Method
Final Clustering
Solution
Assigned
Dimension
Weights

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Bottom up approaches are uniquely positioned to reduce the
specific complexities of SCC
Challenge
Lack of Business
Alignment
Easier “buy-in”
from the client
Bottom Up approach benefits
Data Integration
across multiple
parameters
Logically segregated
axis solutions
Lack of
Transparency
Increased Client
Confidence with
“white box”
solution
1
2
3
A robust, logical, and
transparent country
clustering solution aligned
with client objectives

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However, success of Bottom Up clustering is dependent on Ensemble
approaches; Many approaches exist and choosing the right one is critical
 Take axis solutions as categorical features and run a clustering algorithm on them
Feature Based
Clustering
 Create a membership matrix
 Run a secondary cluster analysis on the membership values using k means
Sawtooth Software
Algorithm
 Create a similarity matrix for each partition
 Take an average over all similarity matrices to create similarity index matrix
 Run a suitable clustering algorithm to arrive at final clustering solution
Cluster-based
Similarity Partitioning
Algorithm (CSPA)

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But, all these approaches are inadequate on their own.
Challenges Faced
No. of
observations
Not suitable for lower no. of
observations
Axis Weights
No scope for weighing the axis
solutions
Business Alignment
Irreverence to Business
objectives
1
2
3
Solution
Modified version of CSPA
where we can use weights
to align the final solution
with client’s strategic
business objectives

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Affinity Matrix Generation
 Create a similarity matrix for each
axis
 Assign weights to each axis
 Take sum-product of the weights
and similarity matrices to create
an affinity matrix
Cluster based Affinity Partitioning
Algorithm (CAPA)
We improved CSPA ensemble approach further
Robust and Transparent output
CAPA - Steps:
Affinity based
Grouping
 Clusters formed on the basis of
affinities between countries.
 An iterative process with each
stage being identified within a
threshold defined by:
Threshold = 100% - (Minimum of
dimensions weights(Wi)
Final Solution
Strategic Country
Clusters for Effective
Decision
Making

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1. Argentina
Axis Weights
Demographics 15%
Economy 20%
Category 50%
U&A 15%
Affinity Matrix
Generation
Below illustrated CAPA – 1/4
Green indicates presence of both countries in the same cluster in a given axis while
Red indicates otherwise
Note:- The affinity scores are calculated considering only the green ones
2. Brazil

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Below illustrated CAPA – 2/4
Affinity Score =
Demographics – 15%
+
Economy – 20%
+
Category – 50%
= 85%
 Similarly, affinity scores are calculated for all country combinations;  generating a matrix of
affinity scores
 On the basis of this matrix, countries with similar scores are grouped together
1. Argentina
Affinity Matrix
Generation
2. Brazil

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Below illustrated CAPA – 3/4
Argentina
Brazil
85%
Greece
Portugal
70%
France35% 65%
50%20%
Cluster 1 Cluster 2
France – Cluster 2 Affinity = 115%< 
New country – France is
introduced and cluster
affinities calculated
France – Cluster 1 Affinity = 55%
Affinity based
Grouping

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Below illustrated CAPA – 4/4
Argentina
Brazil
85%
Greece
Portugal
70%
France 65%
50%
Cluster 2
Affinity based
Grouping
Based on cluster affinities
France becomes part of
Cluster 2
Cluster 1
 Following the same process, we arrive at the final SCC solution

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 MDI is a measure which captures the relative maturity of markets
 Specific strategies can be customised based on the development stage of the market
Market
Development
Index
We have even leveraged SCC results in variety of applications
 It is a model used to enhance the cluster solution by adding other countries into the
clustering solution
 This helps in understanding the characteristics of countries that were not even present
in the original solution
Cluster
Predictive Model
 Used to estimate growth patterns for a country based on several controllable and
uncontrollable factors
 Enables the decision makers to devise effective long term strategies
Cluster
Progression
Simulator

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Tactical Strategic
Illustrative Business Impact in CPG Industry
Market Research Efficiency
Reduced market research
expenditure by fielding research in
India and projecting results to all
countries in same cluster
New Product Development
Focused development on sugarized kid-
friendly flavors and the small sizes
preferred by Cluster 1 consumers
Advertisement Planning
 Aired new “Bring on the
Fun” TV advertising
campaign, targeting
kids/young adults
 Shifted funds from
print/radio to digital
channels
Spends Reallocation
Shifted $27 Million in
Marketing expenditure across
clustersTrade Spending
Eliminated multi-pack trade
promotions
Trial Generation
Started sampling and in-
store display programs
SCC is significantly impacting global planning process

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Further, we are exploring ways to expand the horizon of country
clustering
 Identify trends in countries ahead in progression curve and adapt strategies to develop a
category in a given country
 Discover evolutionary trends of product categories and attributes affecting their usage
Market Evolution
Study
 Classify regions within a country based on lessons from global clustering
 Predict the grouping of a new region based on a given clustering
Regional
Clustering
 SCC being a scalable concept, can be leveraged by MNC’s across industries (e.g.
Banking, Auto, Manufacturing etc.) in their planning process
Extension across
Industries

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