This Presentation was presented in the 23rd Edition of MRSI, the Annual Market Research Seminar by Lavanya Ramanan, Aaroshi Asija and Vishal Khullar. The objective of the research presented in the paper was to improve the process of data collection in terms of the quality of data collected while simultaneously ensuring that the instrument was itself not tedious (for the respondent) and yet is able to accurately (with a very high degree of accuracy) classify respondents into predetermined segments. While these seem like distinct objectives, we were able to achieve both by changing the scale of some of the questions in the instrument and also by modifying the scale of some of the questions after capture. 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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Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco
www.absolutdata.com
April 30, 2014
XXIII ANNUAL MARKET RESEARCH SEMINAR
Re-engineering Classification Methodologies for
an Enriching Customer Experience

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Quiz! Who am I?
 Am I a Bipede? (Yes/No)
 Do I possess opposable thumbs? (Yes/No)
 Am I a Herbivore? (Always/Sometimes/Never)
 Can I climb trees? (Yes/No)
 Can I fly? (Yes/No)
Yes
Sometimes
Yes
Yes
Yes
Are you a …CHIMP HUMAN ??

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 Am I a Bipede? (Yes/No)
 Do I possess opposable thumbs? (Yes/No)
 Am I a Herbivore? (Always/Sometimes/Never)
 Can I climb trees? (Yes/No)
 Can I fly? (Yes/No)
Quiz! Who am I?
Irrelevant /
Ambiguous Questions
Incorrect
Classification
Incorrect
Scale
CHIMP HUMAN
Yes
Sometimes
Yes
Yes
Yes

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A US based social
networking site, which
provides unique web-
based product offerings
to its customers; based
on their behavior and
how they perceive
themselves.
Our Client

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A look into Client’s existing classification approach
Online survey (quiz)
on website sign up
Identify customer
segments
User persona distribution
in identified segments
Online Quiz
Administer 8 questions with statements for each of the 12 pre-defined
segments. Response captured in ‘select all’ that apply format
 Tedious questionnaire
 Captures dichotomous response
Classification
Approach
User
Segments
Classification based on highest tally of responses for each segment
 Unbreakable ties in classification
 Unable to sharply capture every facet of user’s personality
 Not a proprietary classification approach

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Revise the data collection
instrument
Improve classification
methodology
Administer a quiz with a shorter set of questions
Probable rescaling of quiz questions
Classify the respondents accurately into their
primary, secondary and tertiary segments
Identify the best typing tool based on statistical
evidence as well as strategic input
The study objectives were, therefore, two-fold:-

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Administer survey
Analyze Data and Redesign quiz
to be hosted on website
Develop classification algorithm
to accurately identify user
segments
 A sample of 2000 collected, split equally between males
and females
 Data captured on both ranking and select all scale
 Introduced attitudinal and behavioral questions to
capture enriched data
 Algorithm to derive segments from new set of questions
 Identify respondent segments based on existing
approach to be taken as benchmark
 Identify new set of questions for accurate classification
and shorten the length of quiz
Our approach to address these objectives:-

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Shortening the quiz
Revisit the quiz originally used and
redesign it to a shorter version

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 Deriving 4 most predictive questions from the original quiz
 A shorter quiz (with only 72 statements from original 144 statements)
Arriving at a subset of original
eight questions
Using descriptive statistics (cross-tabulations)
Incorporating client inputs to identify priority as
per business sense
Identifying relevant questions to be retained

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Low accuracy of segments
identification from subset of 4
identified questions…
 Build a robust classification approach
based on statistical evidence
 Provide a Proprietary Approach to the
Client
Deterministic to Probabilistic Classification
Approach

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 Improved classification using 6 questions capturing every facet of user’s
personality
 Reduced chances of ties as in Deterministic approach
 A proprietary method with set of coefficients defining user segments
Build algorithm using
predictive approach for
segment identification
Multinomial Logistic Regression (MNL)
 Multiple iterations using different set of variables to
maximize prediction accuracy
Introducing new attitudinal and behavioral
questions
 Factoring the new questions to find the most
representative statements
Probabilistic classification approach

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 Improve prediction accuracy achieved in
probabilistic approach
Need to further improve prediction
accuracy to match with original
segment classification…
Rescaling Quiz Responses and
Questions

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Richer insights from the same data
 Distribution of percentages as per
classification
Improved prediction accuracy
Rescaling
Change “Select any 3” response in original quiz
to “Rank top 3” response
Change data captured on “5 point agreement
scale” to “Select all that apply” response
Questions Responses
Improved prediction accuracy
User friendly experience
 Answering the quiz is much easier
Rescaling

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 Build a robust and accurate user
classification in line with business
objectives
Probabilistic model predicted segments not ALL
aligned with user responses…
Apply Business Rules

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Business Rules
 Probabilistic model solutions triangulated with Deterministic approach
 Remove any sample bias in estimating probability coefficients
 Overall improved prediction accuracy
Build a mixed approach for
segment identification using
MNL coefficients and segment
tally scores
Calculate tally scores for respondent MNL
identified segments
If a predicted segment has low tally score
(below cutoff) it is replaced with next higher
tally score segment

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Shorter version of quiz
Original quiz with 8 questions revised
to 6 questions
Proprietary classification tool
Coefficients for each user code
Classification distribution
amongst top 3 segments
top 3 segments with their weightings
identified
Final Deliverables (1/3)

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Shorter version of quiz
Original quiz with 8 questions revised to
6 questions
Proprietary classification tool
Coefficients for each user code
Classification distribution
amongst top 3 segments
top 3 segments with their weightings
identified
Final Deliverables (2/3)

18. © Absolutdata 2014 Proprietary and Confidential 18
Shorter version of quiz
Original quiz with 8 questions revised to
6 questions
Proprietary classification tool
Coefficients for each user code
Classification distribution
amongst top 3 segments
top 3 segments with their weightings
identified
Final Deliverables (3/3)

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85-95%
78-85%
70-78%
55-70%
Sub-set of original Quiz
Probabilistic Approach
Rescaling
Business Rules
Selecting four of the most predictive out of eight
questions
Added an attitudinal and behavioral question, and
moving from a tally approach to an MNL solution
Changing the scales to a ranking scale and a select
all scale for attitudinal & behavioral questions
Combining business sense with statistical
evidence
Prediction Accuracy

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Implications on Indian MR Industry
Importance of asking relevant questions
(in tandem with business objective to avoid diluting the
business implications)
Developing the right questions
(to accurately convert business objectives into research
hypothesis)
Agency
Client

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Implications for Customers
WIN
WIN
Enhanced experience for the customer
 The response time for the user is
considerably reduced with fewer
number of questions
 An appropriate scale allows the user to
respond effectively

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