Big Data Analytics: a GIS approach on Market Segmentation

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Presentation transcript:

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

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

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.

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

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

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

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

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

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

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.

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.