© Absolutdata 2014 Proprietary and Confidential
Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco
w...
© Absolutdata 2014 Proprietary and Confidential 2
Agenda
Deep Dive
Approaches & Benefits
Business Need
Impact
Way Forward
...
© Absolutdata 2014 Proprietary and Confidential 3
Increasingly complex business environment demands MNCs to…
Homogeneous m...
© Absolutdata 2014 Proprietary and Confidential 4
Complexity
Data
Variability
Process
Standardizati
on
Technology
Interven...
© Absolutdata 2014 Proprietary and Confidential 5
To develop SCC solution we assessed two approaches:
2. Bottom Up: Genera...
© Absolutdata 2014 Proprietary and Confidential 6
Top Down Clustering Approach
Economy
Demographics
Usage and Attitude
K-M...
© Absolutdata 2014 Proprietary and Confidential 7
Challenge
Lack of Business
Alignment
Too Data Driven
However, Top Down c...
© Absolutdata 2014 Proprietary and Confidential 8
Bottom Up clustering approach addresses afore mentioned
challenges
Botto...
© Absolutdata 2014 Proprietary and Confidential 9
Bottom up approaches are uniquely positioned to reduce the
specific comp...
© Absolutdata 2014 Proprietary and Confidential 10
However, success of Bottom Up clustering is dependent on Ensemble
appro...
© Absolutdata 2014 Proprietary and Confidential 11
But, all these approaches are inadequate on their own.
Challenges Faced...
© Absolutdata 2014 Proprietary and Confidential 12
Affinity Matrix Generation
 Create a similarity matrix for each
axis
...
© Absolutdata 2014 Proprietary and Confidential 13
1. Argentina
Axis Weights
Demographics 15%
Economy 20%
Category 50%
U&A...
© Absolutdata 2014 Proprietary and Confidential 14
Below illustrated CAPA – 2/4
Affinity Score =
Demographics – 15%
+
Econ...
© Absolutdata 2014 Proprietary and Confidential 15
Below illustrated CAPA – 3/4
Argentina
Brazil
85%
Greece
Portugal
70%
F...
© Absolutdata 2014 Proprietary and Confidential 16
Below illustrated CAPA – 4/4
Argentina
Brazil
85%
Greece
Portugal
70%
F...
© Absolutdata 2014 Proprietary and Confidential 17
 MDI is a measure which captures the relative maturity of markets
 Sp...
© Absolutdata 2014 Proprietary and Confidential 18
Tactical Strategic
Illustrative Business Impact in CPG Industry
Market ...
© Absolutdata 2014 Proprietary and Confidential 19
Further, we are exploring ways to expand the horizon of country
cluster...
Name
Designation
Phone:
Email:
Follow us on:
Upcoming SlideShare
Loading in...5
×

(MRSI - 3/3) Strategic country clusters using ensemble clustering methodolgies presentation

4,622

Published on

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

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
4,622
On Slideshare
0
From Embeds
0
Number of Embeds
7
Actions
Shares
0
Downloads
31
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

(MRSI - 3/3) Strategic country clusters using ensemble clustering methodolgies presentation

  1. 1. © Absolutdata 2014 Proprietary and Confidential Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco www.absolutdata.com April 29, 2014 Strategic Country Clusters using Ensemble Clustering Methodologies
  2. 2. © Absolutdata 2014 Proprietary and Confidential 2 Agenda Deep Dive Approaches & Benefits Business Need Impact Way Forward   
  3. 3. © Absolutdata 2014 Proprietary and Confidential 3 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
  4. 4. © Absolutdata 2014 Proprietary and Confidential 4 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
  5. 5. © Absolutdata 2014 Proprietary and Confidential 5 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
  6. 6. © Absolutdata 2014 Proprietary and Confidential 6 Top Down Clustering Approach Economy Demographics Usage and Attitude K-Means Clustering Final Clustering Solution Top Down Approach
  7. 7. © Absolutdata 2014 Proprietary and Confidential 7 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
  8. 8. © Absolutdata 2014 Proprietary and Confidential 8 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
  9. 9. © Absolutdata 2014 Proprietary and Confidential 9 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
  10. 10. © Absolutdata 2014 Proprietary and Confidential 10 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)
  11. 11. © Absolutdata 2014 Proprietary and Confidential 11 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
  12. 12. © Absolutdata 2014 Proprietary and Confidential 12 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
  13. 13. © Absolutdata 2014 Proprietary and Confidential 13 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
  14. 14. © Absolutdata 2014 Proprietary and Confidential 14 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
  15. 15. © Absolutdata 2014 Proprietary and Confidential 15 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
  16. 16. © Absolutdata 2014 Proprietary and Confidential 16 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
  17. 17. © Absolutdata 2014 Proprietary and Confidential 17  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
  18. 18. © Absolutdata 2014 Proprietary and Confidential 18 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
  19. 19. © Absolutdata 2014 Proprietary and Confidential 19 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
  20. 20. Name Designation Phone: Email: Follow us on:
  1. Gostou de algum slide específico?

    Recortar slides é uma maneira fácil de colecionar informações para acessar mais tarde.

×