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1 .Create a Loyal Customer
Base by Knowing Their
Personality
Sourav Mazumder, IBM Analytics (smazumder@us.ibm.com)
Aradhna Tiwari, University of South Florida (aradhna@mail.usf.edu)
#AI9SAIS
2 . Agenda
v Team Introduction
v Big 5 Personality Model
v Prior Work and Our approach
v The Datasets we used
v The Technologies
v Modeling techniques used
v Findings
v How to Use the Customer Loyalty Indicators for Business Use cases ?
v Demonstration
v Q and A
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3 .Team Introduction
Kaushik Dutta Sourav Mazumder
Associate Professor Data Science Thought Leader
Muma College of Business, IBM Analytics
University of South Florida
Aradhna Tiwari Sai Seetha Ram Stacey Ronaghan
Nomula
Business Sr. Data Scientist
Analytics Business Analytics
Graduate Student Graduate Student IBM Analytics
Muma College of Muma College of
Business, Business,
University of
University of South
South Florida
Florida
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4 .Big 5 model for Personality Traits
Big Five Model : Describes how a person engages with the world.
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5 .We based our work on two (recent) prior works
Aspects Prior Work 1 Prior Work 2
Hypothesis Used Big 5 Traits -> Customer Empowerment -> Big 5 Traits -> Brand Identity (Excitement,
Customer Satisfaction/Loyalty Competence, Sincerity, Ruggedness) -> Brand
Loyalty
Year 2017 2014
Industry Retail Automobile
Sample Retail customers across the world above 21 Customers for a particular brand (150)
yrs age (278)
Big 5 Traits used for All 5 Only Consciousness, Extroversion and Emotional
comparison Range are used
Key Conclusion “Therefore, it can be inferred that companies’ “The results indicate that there is a positive and
strategies to promote loyalty and satisfaction direct relationship between extroversion and
among consumers should consider focusing excitement, conscientiousness and excitement,
more on consumers related to conscientiousness and competence, excitement
Conscientiousness and Agreeableness” and loyalty, competence and loyalty, sincerity and
loyalty and ruggedness and loyalty and other
hypotheses were rejected”
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6 .Our Hypothesis and Approach
Hypothesis - Every individual has certain innate Personality traits
those contribute to his/her propensity to become a loyal customer
for any product/brand with varied degree
Approach –
ü Identify Personality traits using Data across various Brands and
Products
ü Verify the consistency of those traits across various product
types
ü Use of Data Science techniques – less Time and Money and
more reliable model compared to Survey based approach
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7 .About the Datasets used
v The dataset comprises of ratings and reviews from an Online Retailer.
v The datasets are selected for 5 types of products namely –
v Electronics
v Book
v Grocery
v Pet Supplies
v Baby
v Dataset Citation:
v R. He, J. McAuley. Modeling the visual evolution of fashion trends with
one-class collaborative filtering. WWW, 2016
v J. McAuley, C. Targett, J. Shi, A. van den Hengel. Image-based
recommendations on styles and substitutes. SIGIR, 2015
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8 .Key Attributes from the Datasets
v Reviews: v Ratings:
v Customer ID v Customer ID
v Product ID v Product ID
v Rating
v Review Text
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9 .Technology Stack
Technologies Usage
Watson API for Personality Insights For generating Personality Traits associated with the Big 5
based model
Watson Studio For development of overall Model using Notebooks in a
Collaborative way
REST Data source for Spark Used the library to parallelize calling Watson API through
GitHub Link Spark for multiple sets of input and collating the result in
single Dataframe
Python 3.5 Used as the programming language.
PySpark ML 2.0 Used this library to identify important features using Random
Forest algorithm
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10 .Diving Deep into Model
v First, the output of Watson Personality Insights API is used to get
Big 5 Personality Traits - Conscientiousness, Extraversion,
Agreeableness, Openness, and Emotional Range.
v Correlation analysis between the Big 5 Personality Traits and the
Average Rating (separately for each Big 5 Personality Trait)
v Next Random Forest algorithm is used to create a model where
v Big 5 Personality Traits are used as Independent Variables and
v Average Rating (signifying Loyalty) are used as Dependent
variable
v Feature Importance estimation using Random Forest to identify the
Big 5 traits those are most important
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11 . Findings – The Personality Traits that
can indicate Loyal Customers (1/2)
Person Coefficient relating Big 5 Traits to Rating (Loyalty)
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Electronics Pet_Supplies Book Grocery Baby
-0.05
-0.1
Conscientiousness Agreeableness Extraversion Emotional range Openness
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12 . Findings – The Personality Traits that
can indicate Loyal Customers (2/2)
0.6
Random Forest's Feature Importance across 5 different Product Types for Big 5 Traits
0.5
0.4
0.3
0.2
0.1
0
Electronics Pet Supplies Book Grocery Baby
Conscientiousness Agreeableness Extraversion Emotional Range Openness
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13 . Creating a Customer Loyalty Database
Spark Environment
--------
Calling
-------- Watson
Data Merging Wrangling Text -------- API
Loading Preparation --------
Data Sources -
Emails, Speech to
Text from IVR,
Loyalty
Review comments, Indicator
etc. based on the Important
important Personality Big 5
Personality
Traits
Traits
Traits
Loyal
Applications Customer
base
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14 .Using Customer Loyalty Indicators for new or
potential Customers
Spark Streaming
What are u looking for Qs’ in Calling
-------- Watson
online site, Chat texts, etc. -------- API
--------
Merging --------
Cleaning Text
Preparation
Loyalty Indicator
based on the
Important
Big 5
Applications Personality traits Traits
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15 .Quick Demonstration
Demo
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17 .References
The following papers were referred that infer a strong relationship
between personality and loyalty.
v Jan 2017, Journal of Business and Retail Management
Research (JBRMR), Vol. 11 Issue 2 , Javier Castillo
v Jan 2014,Case Study: product group of Isfahan Iran
Khodro,Dr. Hassan Ghorbani, Seyede Maryam Mousavi
v John, O. P., & Srivastava, S. (1999). The Big Five trait
taxonomy: History, measurement, and theoretical
perspectives. In L. A. Pervin, & O. P. John (Eds.), Handbook
of personality: Theory and research (pp. 102–138). New York:
Guilford Press.
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