1. Big Data Analytics: a GIS approach on Market Segmentation
Sudha Yerramilli, Ph.D , Urban Planning and Interim Director for National Center for Biodefense Communications Research Center
Yohn Jairo Parra Ph.D Student, Computational and Data Enabled Science and Engineering
Presented by Dr. Richard Aló
Dean, College of Science Engineering and Technology, JSU

2. Traditional Market Segmentation
Traditional Market Segmentation focused on static Demographic segments
One message sent to whole audience
Few messages sent to few segments

3. BIG Data Analytics changing Market Segmentation approach
Behavioral information : a dynamic setting between the static demographic data and existing consumer spending characteristics of customer files.

4. Why Market Segmentation ?
Smart companies don’t attempt to market everything to everyone.
Basis of market segmentation
Build a Customer Profile

5. Market Segmentation Process
Dynamic spending trends 1
Static facts 2
Demographic Segmentation
Psychographic Segmentation
Family size and Upper Middle High Income
Wide Range of products
Safety
Economical
Eco-friendly
Sporty Attitude
International
Comfort
Differentiation strategies
CUSTOMER PROFILING

6. What is Dynamic Spending Trends ?
ESRI’s Tapestry Segmentation system combines
the “who” of lifestyle demography
with
the “where” of local neighborhood geography to create a model of various lifestyle classifications
Organizations
ESRI’s Tapestry Segmentation Data
Decennial Census
Census Bureau’s
American Community Survey
Private Organizations
12 Life Modes

7. Big Data Analytics: Market Segmentation (for Toyota: a Demo)
Geographical Hierarchy
Country
State
County
Tracts
Block Groups
Blocks
Tapestry Data
65 US neighborhood segments
Urbanization groups : Based on demographic profiles
Lifestyle Modes : Consumer spending behaviors
BIG DATA PROBLEM:
Block Groups (207,721) with more than 1000 variables
Demographic Segmentation
Psychographic Segmentation
Toyota Demographic
Urbanization Centers I and II
Based on Consumer Spending

8. ESRI 12 LifeMode Summary Groups
L1 High Society ( affluent and well educated)
L2 Upscale Avenues ( suburban neighborhoods)
L3 Metropolis (living in larger cities)
L4 Solo Acts ( singles who prefer city life)
L5 Senior Styles (traveling and relocating to warmer climates.)
L6 Scholars and Patriots (college life or military service)
L7 High Hopes (singles who seek the “American Dream)
L8 Global Roots (Their youth reflects recent immigration trends)
L9 Family Portrait(presence of children)
L10 Traditional living (hardworking, settled families) L11 Factories and Farms(Employment in manufacturing and agricultural industries
L12 American Quilt (preference for fishing, hunting, power boats)
TOYOTA Psychographic
L2 Upscale Avenues
L4 Solo Acts
L7 High Hopes
L8 Global Roots
L9 Family Portrait
L10 Traditional Living

9. Market Segmentation and Hot Spots: Results
Understand Customer Base
Demographic
Customer Profile strategies
Psychographic
Tapestry data (ESRI)
Cluster Analysis
Hot Spot Analysis
Maps with location of Targeted Customers
Zip code Clusters of Targeted Customers
Hot spots of Targeted Customers

10. Benefits with Big Data Analytics
Defined market segments help businesses define their target customers.
Segmenting Customer markets based on Demographic and Psychographic profiles strengthen company’s database marketing abilities.
Huge behavioral datasets at a fine geographic levels demand Big Data Analytics to generate customer segmentation based on spending trends.

11. Future Work
Expand the scope to national level at a finer geographical level
Use Big data analytical tools (like Hadoop as the process needs to map and reduce the data ) to process market segmentation hot spots
Visualize and process spatial analytics by integrating with GIS.