Knowing insights about the personality of the people you are less familiar with in the work place, social media, or real social circle is always an interesting idea. This can help businesses to understand psychology of their customers, employees and partners which can in turn help creating a successful partnership and loyal customers. A Recommendation Engine which can provide insight about the personality of a customer can be very effective to maintain a loyal customer base by aligning with their need and behavioral pattern while suggesting a new product/service. However, creating such an engine and keeping it up to date with changing behavioral aspect of human nature can be a daunting task. In this session, we’ll discuss how Watson Personality Insight API in conjunction with Spark can be used to create and maintaining such a Recommendation Engine for Personality Insight for the customer. We shall demonstrate the steps for the same through a use case where Spark Streaming would be used to continuously get written content snippets from various streaming data sources; Spark DataFrameReader would be used to get static data from static data sources; Watson Personality Insight API would be used to obtain Personality rating around 3 popular Personality models (Big Five, Needs and Values) from the snippets of written communication by a target person and finally Spark’s distributed processing engine would be used to call Watson Personality Insight API in parallel for thousands of time for thousands of the text snippet and also for collating the result. In this session attendees will learn how insights about target person’s personality can be created using the snippets from their written communication using Watson Personality Insight API and Spark. They will also learn how a Recommendation Engine for Personality Insight can be created and maintained in an automated fashion.

1. Sourav Mazumder, IBM Analytics (smazumder@us.ibm.com)
Aradhna Tiwari, University of South Florida (aradhna@mail.usf.edu)
Create a Loyal Customer
Base by Knowing Their
Personality
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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
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Aradhna Tiwari
Business
Analytics
Graduate Student
Muma College of
Business,
University of
South Florida
Sai Seetha Ram
Nomula
Business Analytics
Graduate Student
Muma College of
Business,
University of South
Florida
Sourav Mazumder
Data Science Thought Leader
IBM Analytics
Kaushik Dutta
Associate Professor
Muma College of Business,
University of South Florida
Stacey Ronaghan
Sr. Data Scientist
IBM Analytics

4. Big 5 model for Personality Traits
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Big Five Model : Describes how a person engages with the world.

5. We based our work on two (recent) prior works
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Aspects Prior Work 1 Prior Work 2
Hypothesis Used Big 5 Traits -> Customer Empowerment ->
Customer Satisfaction/Loyalty
Big 5 Traits -> Brand Identity (Excitement,
Competence, Sincerity, Ruggedness) -> Brand
Loyalty
Year 2017 2014
Industry Retail Automobile
Sample Retail customers across the world above 21
yrs age (278)
Customers for a particular brand (150)
Big 5 Traits used for
comparison
All 5 Only Consciousness, Extroversion and Emotional
Range are used
Key Conclusion “Therefore, it can be inferred that companies’
strategies to promote loyalty and satisfaction
among consumers should consider focusing
more on consumers related to
Conscientiousness and Agreeableness”
“The results indicate that there is a positive and
direct relationship between extroversion and
excitement, conscientiousness and excitement,
conscientiousness and competence, excitement
and loyalty, competence and loyalty, sincerity and
loyalty and ruggedness and loyalty and other
hypotheses were rejected”

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 Customer ID
v Product ID
v Review Text
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v Ratings:
v Customer ID
v Product ID
v Rating

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
GitHub Link
Used the library to parallelize calling Watson API through
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)
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-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Electronics Pet_Supplies Book Grocery Baby
Person Coefficient relating Big 5 Traits to Rating (Loyalty)
Conscientiousness Agreeableness Extraversion Emotional range Openness

12. Findings – The Personality Traits that
can indicate Loyal Customers (2/2)
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0
0.1
0.2
0.3
0.4
0.5
0.6
Electronics Pet Supplies Book Grocery Baby
Random Forest's Feature Importance across 5 different Product Types for Big 5 Traits
Conscientiousness Agreeableness Extraversion Emotional Range Openness

13. Spark Environment
Data
Loading
Merging Wrangling
--------
--------
--------
--------
Text
Preparation
Big 5
Traits
Loyalty
Indicator
based on the
important
Personality
Traits
Calling
Watson
API
Creating a Customer Loyalty Database
Important
Personality
Traits
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Applications
Data Sources -
Emails, Speech to
Text from IVR,
Review comments,
etc.
Loyal
Customer
base

14. Spark Streaming
Merging Cleaning
--------
--------
--------
--------
Text
Preparation
Big 5
Traits
Loyalty Indicator
based on the
Important
Personality traits
Calling
Watson
API
What are u looking for Qs’ in
online site, Chat texts, etc.
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Applications
Using Customer Loyalty Indicators for new or
potential Customers

15. Quick Demonstration
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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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