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How to use Geosocial Data to
Identify CPG Demand Hotspots
Follow @CARTO on Twitter
CARTO — Unlock the power of spatial analysis
Introductions
Argyrios Kyrgiazos
Data Scientist at CARTO
Lyden Foust
CEO of Spatial.ai
Welcome to the new
Webinar Series
CARTO — Unlock the power of spatial analysis
Replace this image
How Spatial Data
can be used to
reveal features &
areas for
successful
distribution
rollout?
CARTO — Unlock the power of spatial analysis
“Organic is the fastest growing sector of
the U.S. food industry”
Source: https://ota.com/hotspots
CARTO — Unlock the power of spatial analysis
➢ How can we identify
the hotspots?
➢ What drives the growth
in demand and the
location preference?
➢ How can we use
location
information-data to
extrapolate?
Growth in demand is affected by:
● Culture
● Socio-economic factors
● Health factors
● ?
Hotspots are related with:
● Where people spend their
money
● What are their interests
depending on the location
● What do they search online
● ?
CARTO — Unlock the power of spatial analysis
● How can we identify the hotspots?
● What drives the growth in demand and the location
preference?
● How can we use location information-data to extrapolate?
● Culture
● Socio-economic factors
● Health factors
● ?
● Where people spend their
money
● What are their interests
depending on the location
● What do they search online
● ?
Growth in demand is affected by: Hotspots are related with:
CARTO — Unlock the power of spatial analysis
POLL 1
Do you have at least one social media account?
(LinkedIn, Twitter, Instagram, TikTok, Foursquare, Snapchat, etc)
Yes
No
CARTO — Unlock the power of spatial analysis
79%
US Population uses social media
Source: Statistica: https://www.statista.com/statistics/273476/percentage-of-us-population-with-a-social-network-profile/
CARTO — Unlock the power of spatial analysis
72
Geosocial
Segments
CARTO — Unlock the power of spatial analysis
Spatial.ai
Geosocial Segments: behavioral segments based on the
analysing social media feeds with location information
Mastercard
Geographic Insights: providing sales-based dynamics of a
location with indices measuring the evolution of credit card
spend, number of transactions, average tickets, etc.
happening in a retail area over time
Dstillery
Behavioral Audiences: audiences derived from online
behaviors
Pitney Bowes
Points of Interest: database with the location of businesses
and other points of interest categorized by classes and
industry groups
AGS
Sociodemographics: basic socio-demographic and
socio-economic attributes estimated at current year and
projected 5 years into the future
What Data have
we used?
In the new millenia people tend to
express their interest and
preferences in social media.
People use the internet search
engines to find whatever they want.
Can we use information from social
media and internet to identify the
hotspots apart from socio economic
factors?
CARTO — Unlock the power of spatial analysis
Data Sources
Behavioral
Geosocial Segments: behavioral segments based
on the analysing social media feeds with location
information
Behavioral Audiences: audiences derived from
online behaviors
POI’s
POIs: database with the location of
businesses and other points of interest
categorized by classes and industry groups.
Demographics
Sociodemographics: basic
socio-demographic and socio-economic
attributes estimated at current year and
projected 5 years into the future
Geographic Insights: providing sales-based dynamics
of a location with indices measuring the evolution of
credit card spend, number of transactions, etc.
happening in a retail area over time
Financial
COMMERCE
PEOPLE
Physical Digital
CARTO — Unlock the power of spatial analysis
🐶 🐕 🐾
#Puppylove
#Dogsofinstagram #fur
Woof #mansbestfriend
#Dogmom
#Furbabies
Walks #dogtoy
Clustered
Text Data
#dogbreeds Grooming
#Puppylove +100s more
Kong
Geographic
Segment
CONFIDENTIAL 14
CARTO — Unlock the power of spatial analysis
Two Shopping Areas
CARTO — Unlock the power of spatial analysis
Two Shopping Areas
90
86
82
80
96
92
88
88
CARTO — Unlock the power of spatial analysis
How can we replicate this at scale?
CARTO — Unlock the power of spatial analysis
Example
CARTO — Unlock the power of spatial analysis
1. Average ticket size in Grocery Stores based
on Mastercard data
2. Organic food has potentially a higher
demand via the exploration of social media
posts (using Spatial.ai geosocial
segmentation) and internet search
behaviours (with Dstillery's audience data)
How can we identify the
hotspots?
Built a classifier which considers the socio
economic and geosocial segments (Spatial.ai
data) to identify which features are
“responsible” for the selection of the
“targeted” areas
{Selected areas} = {Mastercard ∩ {Spatial.ai ∪ Dstillery}}
What drives the growth in
demand and the location
preference?
CARTO — Unlock the power of spatial analysis
Resulting Areas New York
Link
CARTO — Unlock the power of spatial analysis
Resulting Areas Philadelphia
Link
CARTO — Unlock the power of spatial analysis
Exploring features and Characterizing the selected areas
● Perform t-test to identify which features are “different” between selected and the rest of the areas
● Further reduce the dimension of Geosocial segments, see the differences between the selected and
non-selected areas
CARTO — Unlock the power of spatial analysis
Building a
classifier
For the remaining features:
● Upsampling the imbalance
dataset.
● Random forest Classifier.
● Output the significance of
each feature to whether or
not a block should be
labelled as “targeted”.
Identification of the driving factors
CARTO — Unlock the power of spatial analysis
Main driving factors for New York
CARTO — Unlock the power of spatial analysis
Main driving factors for
Philadelphia
CARTO — Unlock the power of spatial analysis
It’s time for a real world example!
CARTO — Unlock the power of spatial analysis
Identifying twin
areas in different
cities
Example
Available data:
● Per capita income (projected, five years)
● Average household Income (projected, five years)
● EB03_lgbtq_culture
● ED09_hops_and_brews
● ED08_wine_lovers
● ED04_whiskey_business
● Median household income (projected, five years)
● ED02_coffee_connoisseur
● LEGAL SERVICES
● ED01_sweet_treats
Selected block in New York
Thanks for listening!
Any questions?
Request a demo at CARTO.COM
Lyden Foust
CEO of Spatial.ai // lyden@spatial.ai
Argyrios Kyrgiazos
Data Scientist at CARTO // argyrios@carto.com
CARTO — Unlock the power of spatial analysis
{Selected areas} = {Mastercard ∩ {Spatial.ai ∪ Dstillery}}
Methodology
Our analysis follows two main steps:
● Identification of target areas with high potential for a successful rollout of organic products.
■ Identification of areas with higher average ticket size in Grocery Stores based on Mastercard data
■ Identification of areas where organic food has a potentially higher demand via the exploration of
social media posts (using Spatial.ai geosocial segmentation) and internet search behaviours (with
Dstillery's audience data)
■ Intersection of the areas identified in the above two steps; these will be the resulting selected target
areas for the reminder of the case study.
○ Analysis of the different factors that characterize and have driven the selection of the target areas, build a
classifier
● Identification of twin areas in San Francisco based on those selected in New York and Philadelphia
CARTO — Unlock the power of spatial analysis
Study of people based on
where they live.*
Study of people based on what
they do.
The Traditional Data Landscape
Government mandated
survey.
Census Data Psychographic Data True Human Behavioral Data
*Harris, Sleight, Webber. Geodemographics, GIS and neighborhood targeting. Wiley, 2005

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How to Use Geospatial Data to Identify CPG Demnd Hotspots

  • 1. How to use Geosocial Data to Identify CPG Demand Hotspots Follow @CARTO on Twitter
  • 2. CARTO — Unlock the power of spatial analysis Introductions Argyrios Kyrgiazos Data Scientist at CARTO Lyden Foust CEO of Spatial.ai
  • 3. Welcome to the new Webinar Series
  • 4. CARTO — Unlock the power of spatial analysis Replace this image How Spatial Data can be used to reveal features & areas for successful distribution rollout?
  • 5. CARTO — Unlock the power of spatial analysis “Organic is the fastest growing sector of the U.S. food industry” Source: https://ota.com/hotspots
  • 6. CARTO — Unlock the power of spatial analysis ➢ How can we identify the hotspots? ➢ What drives the growth in demand and the location preference? ➢ How can we use location information-data to extrapolate? Growth in demand is affected by: ● Culture ● Socio-economic factors ● Health factors ● ? Hotspots are related with: ● Where people spend their money ● What are their interests depending on the location ● What do they search online ● ?
  • 7. CARTO — Unlock the power of spatial analysis ● How can we identify the hotspots? ● What drives the growth in demand and the location preference? ● How can we use location information-data to extrapolate? ● Culture ● Socio-economic factors ● Health factors ● ? ● Where people spend their money ● What are their interests depending on the location ● What do they search online ● ? Growth in demand is affected by: Hotspots are related with:
  • 8. CARTO — Unlock the power of spatial analysis POLL 1 Do you have at least one social media account? (LinkedIn, Twitter, Instagram, TikTok, Foursquare, Snapchat, etc) Yes No
  • 9. CARTO — Unlock the power of spatial analysis 79% US Population uses social media Source: Statistica: https://www.statista.com/statistics/273476/percentage-of-us-population-with-a-social-network-profile/
  • 10. CARTO — Unlock the power of spatial analysis 72 Geosocial Segments
  • 11. CARTO — Unlock the power of spatial analysis Spatial.ai Geosocial Segments: behavioral segments based on the analysing social media feeds with location information Mastercard Geographic Insights: providing sales-based dynamics of a location with indices measuring the evolution of credit card spend, number of transactions, average tickets, etc. happening in a retail area over time Dstillery Behavioral Audiences: audiences derived from online behaviors Pitney Bowes Points of Interest: database with the location of businesses and other points of interest categorized by classes and industry groups AGS Sociodemographics: basic socio-demographic and socio-economic attributes estimated at current year and projected 5 years into the future What Data have we used? In the new millenia people tend to express their interest and preferences in social media. People use the internet search engines to find whatever they want. Can we use information from social media and internet to identify the hotspots apart from socio economic factors?
  • 12. CARTO — Unlock the power of spatial analysis Data Sources Behavioral Geosocial Segments: behavioral segments based on the analysing social media feeds with location information Behavioral Audiences: audiences derived from online behaviors POI’s POIs: database with the location of businesses and other points of interest categorized by classes and industry groups. Demographics Sociodemographics: basic socio-demographic and socio-economic attributes estimated at current year and projected 5 years into the future Geographic Insights: providing sales-based dynamics of a location with indices measuring the evolution of credit card spend, number of transactions, etc. happening in a retail area over time Financial COMMERCE PEOPLE Physical Digital
  • 13. CARTO — Unlock the power of spatial analysis 🐶 🐕 🐾 #Puppylove #Dogsofinstagram #fur Woof #mansbestfriend #Dogmom #Furbabies Walks #dogtoy Clustered Text Data #dogbreeds Grooming #Puppylove +100s more Kong Geographic Segment
  • 15. CARTO — Unlock the power of spatial analysis Two Shopping Areas
  • 16. CARTO — Unlock the power of spatial analysis Two Shopping Areas 90 86 82 80 96 92 88 88
  • 17. CARTO — Unlock the power of spatial analysis How can we replicate this at scale?
  • 18. CARTO — Unlock the power of spatial analysis Example
  • 19. CARTO — Unlock the power of spatial analysis 1. Average ticket size in Grocery Stores based on Mastercard data 2. Organic food has potentially a higher demand via the exploration of social media posts (using Spatial.ai geosocial segmentation) and internet search behaviours (with Dstillery's audience data) How can we identify the hotspots? Built a classifier which considers the socio economic and geosocial segments (Spatial.ai data) to identify which features are “responsible” for the selection of the “targeted” areas {Selected areas} = {Mastercard ∩ {Spatial.ai ∪ Dstillery}} What drives the growth in demand and the location preference?
  • 20. CARTO — Unlock the power of spatial analysis Resulting Areas New York Link
  • 21. CARTO — Unlock the power of spatial analysis Resulting Areas Philadelphia Link
  • 22. CARTO — Unlock the power of spatial analysis Exploring features and Characterizing the selected areas ● Perform t-test to identify which features are “different” between selected and the rest of the areas ● Further reduce the dimension of Geosocial segments, see the differences between the selected and non-selected areas
  • 23. CARTO — Unlock the power of spatial analysis Building a classifier For the remaining features: ● Upsampling the imbalance dataset. ● Random forest Classifier. ● Output the significance of each feature to whether or not a block should be labelled as “targeted”. Identification of the driving factors
  • 24. CARTO — Unlock the power of spatial analysis Main driving factors for New York
  • 25. CARTO — Unlock the power of spatial analysis Main driving factors for Philadelphia
  • 26. CARTO — Unlock the power of spatial analysis It’s time for a real world example!
  • 27. CARTO — Unlock the power of spatial analysis Identifying twin areas in different cities Example Available data: ● Per capita income (projected, five years) ● Average household Income (projected, five years) ● EB03_lgbtq_culture ● ED09_hops_and_brews ● ED08_wine_lovers ● ED04_whiskey_business ● Median household income (projected, five years) ● ED02_coffee_connoisseur ● LEGAL SERVICES ● ED01_sweet_treats Selected block in New York
  • 28. Thanks for listening! Any questions? Request a demo at CARTO.COM Lyden Foust CEO of Spatial.ai // lyden@spatial.ai Argyrios Kyrgiazos Data Scientist at CARTO // argyrios@carto.com
  • 29. CARTO — Unlock the power of spatial analysis {Selected areas} = {Mastercard ∩ {Spatial.ai ∪ Dstillery}} Methodology Our analysis follows two main steps: ● Identification of target areas with high potential for a successful rollout of organic products. ■ Identification of areas with higher average ticket size in Grocery Stores based on Mastercard data ■ Identification of areas where organic food has a potentially higher demand via the exploration of social media posts (using Spatial.ai geosocial segmentation) and internet search behaviours (with Dstillery's audience data) ■ Intersection of the areas identified in the above two steps; these will be the resulting selected target areas for the reminder of the case study. ○ Analysis of the different factors that characterize and have driven the selection of the target areas, build a classifier ● Identification of twin areas in San Francisco based on those selected in New York and Philadelphia
  • 30. CARTO — Unlock the power of spatial analysis Study of people based on where they live.* Study of people based on what they do. The Traditional Data Landscape Government mandated survey. Census Data Psychographic Data True Human Behavioral Data *Harris, Sleight, Webber. Geodemographics, GIS and neighborhood targeting. Wiley, 2005