SPS 33
Location Analytics:
The Next Generation
@mphnyc
Michael Hiskey
Big Data Evangelist
2
Big
Messy
Unstructured 
NoisyData 
3
We do the “hard stuff” of Big Data analytics
#DataSci
4
Business Users have existing interfaces
Business Intelligence Tools and 
Dashboards, custom‐designed 
internal applicati...
5
Move from dashboards to advanced analytics
create external script LM_PRODUCT_FORECAST environment rsint
receives ( SALED...
6
A Platform for Advanced Analytics
• Business Applications
• Run advanced 
analytics in‐memory
• MPP CPU Scale‐out
• Pers...
Title
Subtitle subtitle subtitle
subtitleContextualizing The Customer Through Location Intelligence
April, 2014
The Mobile Consumer Challenge
• Loss of online context 
– Limited cookies
– Anonymous usage
– Short sessions / attention s...
For organizations seeking to understand human behavior,
PlaceIQ derives intelligence from activities across time,
space an...
Customer 
Segmentation‐based
Tell me about 
behaviors
Customer 
Segmentation‐based
Tell me about 
behaviors
Tile‐based
Tel...
Top Brands Using PlaceIQ
AUTO RETAIL TECH/TELECOM ENTERTAINMENT
CPG
FINANCIAL
AND MORE…
@PlaceIQ
Location is Hard
Geographic Information 
System
Billions  of Points of 
Interest
People are temporal
Taxonomy definitions ...
GIS Rule
SELECT r.taxonomy
,COALESCE(tppw.time_period_id, 6) AS period_id
,rw.feature_name AS feature
,rw.attribute_name A...
Non‐Scalable Knowledge Base
Movement Data Streets Land Use Parcels
Uniquely structured data, no unifying key across 
datas...
Location Data Quality
How do we confirm the accuracy of incoming lat/long data? 
1M
Centroid Detection
Detecting devices t...
The PlaceIQ Solution
Analytics
Contextualizing the Customer
Customer Segmentation
Creating behavioral clusters from locati...
Location Ingest
Taxonomy
4K+ categories organize
our 40+ data sources
27 Time Periods
Periods mapped to
moments
Nearly 1 Billion Tiles
USG...
Data / Base Map
@PlaceIQ
PlaceIQ Ingests a Diverse Selection of Data Sets
Residential
• Age
• Income
• Household Size
• Children
• Life Stage
• Eth...
Hand-Made Polygons
Hundreds of thousands built by
cartographers
Tile Based Scoring
Tiles are scored from 0 to 10
Leading P...
Enterprise Connector
@PlaceIQ
Customer
Segmentation
@PlaceIQ
Tile‐based
Tell me about this 
location
Tile‐based
Tell me about this 
location
The Location Contextualization Opportunity...
Tile Analysis – the World
• Legal and financial office buildings
• Hyatt hotel
• Tully’s Coffee
• Upscale Dining (Daniel’s...
The Location Contextualization Opportunity
Location‐based
Tell me about my store 
or my competitor’s 
store
Location‐based...
Location Analysis – Your Store
Place Visit Rate
Do devices shown
Mobile ads visit key
retail locations?
PreVisit
Where do ...
The Location Contextualization Opportunity
Location‐based
Tell me about my store 
or my competitor’s 
store
Location‐based...
Customer Segment: Movie Goer & FC Diner
• Census
• Businesses  
• Parks 
• Events 
• Social
• Photos
• Polk 
• Rentrak
• L...
Behavioral Graph
Big Box A Big Box B
@PlaceIQ
Big Box B Detail
@PlaceIQ
Technical Architecture
Enterprise Data
Normalize
HDFS
platform component
data Location Data
Ingest
PIQL
Enterprise Connect...
Performance Became a Massive Issue
500 node hadoop cluster 
= 
20 hours of processing time
@PlaceIQ
Enter Kognitio
Transferred to in memory database 
and obtained advanced performance 
on clustering, querying and 
multidim...
Kognitio Delivers Unrestricted Answers, Quicker
THEN NOW
500 node hadoop cluster 
= 
20 hours of processing time
500 node ...
Why PlaceIQ?
Consumer InsightsData-Driven Q/A Process
Patented Platform Unparalleled Audiences Innovation
@PlaceIQ
Thank you.
39
Recognized by Industry Analysts
Forrester Wave™: Enterprise Data 
Warehouse, Q4 ’13 
Gartner Magic Quadrant for Data 
W...
Booth # 421 
@PlaceIQ @mphnyc @Kognitio#GartnerBI #DataSci
Gartner BI PlaceIQ presentation with Kognitio
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Gartner BI PlaceIQ presentation with Kognitio

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Location is the biggest indicator of intent—for purchasing patterns, buyer behavior, and consumer intent. Anonymously leveraging over half a trillion data points, PlaceIQ has contextualized the relationship between places, time and people, and more specifically, behavior, preferences and intent. All of North America—in 100m x 100m tiles. Kognitio is the innovative in-memory analytical platform that underlies the PlaceIQ technology, persisting data in Hadoop with near-real time analytics.

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Gartner BI PlaceIQ presentation with Kognitio

  1. 1. SPS 33 Location Analytics: The Next Generation @mphnyc Michael Hiskey Big Data Evangelist
  2. 2. 2 Big Messy Unstructured  NoisyData 
  3. 3. 3 We do the “hard stuff” of Big Data analytics #DataSci
  4. 4. 4 Business Users have existing interfaces Business Intelligence Tools and  Dashboards, custom‐designed  internal applications, etc.  Business  Analysts Business  Users @Kognitio
  5. 5. 5 Move from dashboards to advanced analytics create external script LM_PRODUCT_FORECAST environment rsint receives ( SALEDATE DATE, DOW INTEGER, ROW_ID INTEGER, PRODNO INTEGER, DAILYSALES INTEGER ) partition by PRODNO order by PRODNO, ROW_ID sends ( R_OUTPUT varchar ) isolate partitions script S'endofr( # Simple R script to run a linear fit on daily sales prod1<-read.csv(file=file("stdin"), header=FALSE,row.names=1) colnames(prod1)<-c("DOW","ID","PRODNO","DAILYSALES") dim1<-dim(prod1) daily1<-aggregate(prod1$DAILYSALES, list(DOW = prod1$DOW), median) daily1[,2]<-daily1[,2]/sum(daily1[,2]) basesales<-array(0,c(dim1[1],2)) basesales[,1]<-prod1$ID basesales[,2]<-(prod1$DAILYSALES/daily1[prod1$DOW+1,2]) colnames(basesales)<-c("ID","BASESALES") fit1=lm(BASESALES ~ ID,as.data.frame(basesales)) forecast<-array(0,c(dim1[1]+28,4)) colnames(forecast)<-c("ID","ACTUAL","PREDICTED","RESIDUALS") Via the Data Scientist #DataSci
  6. 6. 6 A Platform for Advanced Analytics • Business Applications • Run advanced  analytics in‐memory • MPP CPU Scale‐out • Persist data in  Hadoop (and existing  Data Warehouses)
  7. 7. Title Subtitle subtitle subtitle subtitleContextualizing The Customer Through Location Intelligence April, 2014
  8. 8. The Mobile Consumer Challenge • Loss of online context  – Limited cookies – Anonymous usage – Short sessions / attention span – Usage of many diverse applications • Modality: Phone influences usage & mindset – Device – Location  – Time @PlaceIQ
  9. 9. For organizations seeking to understand human behavior, PlaceIQ derives intelligence from activities across time, space and devices, to uncover opportunities to learn about and connect with consumers with unrivaled clarity, quality and relevance. @PlaceIQ
  10. 10. Customer  Segmentation‐based Tell me about  behaviors Customer  Segmentation‐based Tell me about  behaviors Tile‐based Tell me about this  location Tile‐based Tell me about this  location The Location Contextualization Opportunity Location‐based Tell me about my  store or my  competitor’s store Location‐based Tell me about my  store or my  competitor’s store @PlaceIQ
  11. 11. Top Brands Using PlaceIQ AUTO RETAIL TECH/TELECOM ENTERTAINMENT CPG FINANCIAL AND MORE… @PlaceIQ
  12. 12. Location is Hard Geographic Information  System Billions  of Points of  Interest People are temporal Taxonomy definitions  abound Petabyte Scale Storage  and processing Very few data points keyed  to location #analytics
  13. 13. GIS Rule SELECT r.taxonomy ,COALESCE(tppw.time_period_id, 6) AS period_id ,rw.feature_name AS feature ,rw.attribute_name AS attribute ,r.target_feature_name ,r.target_attribute_name ,COALESCE(tppw.weight, 1.0) AS tp_weight ,rw.weight AS attr_weight ,rw.threshold_above ,rw.threshold_below ,r.offset ,r.logistic ,rw.instant_10 FROM rule_weights rw JOIN rules r ON r.id = rw.rule_id LEFT OUTER JOIN time_period_profile_feat_ats tppfa ON (tppfa.feature_name = rw.feature_name AND  tppfa.attribute_name = rw.attribute_name) LEFT OUTER JOIN time_period_profile_weights tppw ON (tppw.time_period_profile_id =  tppfa.time_period_profile_id) WHERE lower(r.taxonomy) = lower('leo') ORDER BY r.taxonomy ,period_id ,feature ,attribute #GISishard
  14. 14. Non‐Scalable Knowledge Base Movement Data Streets Land Use Parcels Uniquely structured data, no unifying key across  datasets, difficult to implement into existing BI tools @PlaceIQ
  15. 15. Location Data Quality How do we confirm the accuracy of incoming lat/long data?  1M Centroid Detection Detecting devices that could appear to be at the center of a zip code or city (middle of field or body of water) as a result of inaccurate geo-coding from IP address or registration data. Device Detection Detecting spam devices (such as receiving 1M ad calls from one device in a short amount of time. Transporter Detection Detecting devices that: • Appear to move faster than humanly possible (velocity detection) • Remain stagnant for a period of time • Bounce (constant movement) Read about PlaceIQ’s hyperlocality and clusterability methodologies Location #DQ
  16. 16. The PlaceIQ Solution Analytics Contextualizing the Customer Customer Segmentation Creating behavioral clusters from location histories Data / Base Map Organizing billions of data points Location Ingest 100x100 meter tile structure Enterprise Connector Integration with CRM / Enterprise @PlaceIQ
  17. 17. Location Ingest
  18. 18. Taxonomy 4K+ categories organize our 40+ data sources 27 Time Periods Periods mapped to moments Nearly 1 Billion Tiles USGS 100 x 100 meter tile grid system PlaceIQ’s Platform Organizes Hundreds of Billions of Data Points @PlaceIQ
  19. 19. Data / Base Map @PlaceIQ
  20. 20. PlaceIQ Ingests a Diverse Selection of Data Sets Residential • Age • Income • Household Size • Children • Life Stage • Ethnicity • Language • Building type • Auto Owned • Auto in Market Retail & Dining Grocery, Clothing, Big Box, QSR, Buffet, Casual Entertainment Movies, Museums, Parks, Tourism, Bars Consumer Spending Purchase Data from Retail Partners Auto & Travel Dealership Lots, Airports, Hotels, Bus Stations PIQ PrimeTime TV Viewership from Set-Top boxes And More… Photos, Social Media Events, etc. @PlaceIQ
  21. 21. Hand-Made Polygons Hundreds of thousands built by cartographers Tile Based Scoring Tiles are scored from 0 to 10 Leading Precision We map to “rooftop” not “driveway” PlaceIQ Leads the Industry in Location Precision @PlaceIQ
  22. 22. Enterprise Connector @PlaceIQ
  23. 23. Customer Segmentation @PlaceIQ
  24. 24. Tile‐based Tell me about this  location Tile‐based Tell me about this  location The Location Contextualization Opportunity Location‐based Tell me about my store  or my competitor’s  store Location‐based Tell me about my store  or my competitor’s  store Customer  Segmentation‐based Tell me about  behaviors Customer  Segmentation‐based Tell me about  behaviors @PlaceIQ
  25. 25. Tile Analysis – the World • Legal and financial office buildings • Hyatt hotel • Tully’s Coffee • Upscale Dining (Daniel’s Broiler and Suite)  • Casual Lunchtime Dining (Joey's Bellvue and KORAL) • Luxury Retail (Nordstrom, BoConcept furniture and Elements gallery) 1. White Collar Financial Workers  • M‐F 8:30am ‐ 5:30pm 2. Travelers • 6am ‐ 12am 3. Casual Lunch Dining • 12PM to 1PM 4. Upscale Dining  • Sat 5‐8PM, Sun 7‐8PM • M‐F 5:30‐8PM 5. Luxury Shopper • 6am ‐ 12am 6. Mall Shopper  • 6am ‐ 12am 1) RAW DATA  2) THE RULE 3) AUDIENCES
  26. 26. The Location Contextualization Opportunity Location‐based Tell me about my store  or my competitor’s  store Location‐based Tell me about my store  or my competitor’s  store Customer  Segmentation‐based Tell me about  behaviors Customer  Segmentation‐based Tell me about  behaviors Tile‐based Tell me about this  location Tile‐based Tell me about this  location @PlaceIQ
  27. 27. Location Analysis – Your Store Place Visit Rate Do devices shown Mobile ads visit key retail locations? PreVisit Where do visitors go before they visit key retail locations? PIQ Analytics What is unique about the movements, behaviors, and demographics of an audience? #Location
  28. 28. The Location Contextualization Opportunity Location‐based Tell me about my store  or my competitor’s  store Location‐based Tell me about my store  or my competitor’s  store Customer  Segmentation‐based Tell me about  behaviors Customer  Segmentation‐based Tell me about  behaviors Tile‐based Tell me about this  location Tile‐based Tell me about this  location @PlaceIQ
  29. 29. Customer Segment: Movie Goer & FC Diner • Census • Businesses   • Parks  • Events  • Social • Photos • Polk  • Rentrak • Land use  Raw Data The Rule Customer Segments RULEPLUS SegmentsPlus‐>SegmentX [RANGE: 6 m] [FREQUENCY: 1 per month] { Dining‐>Fast_Casual_Restaurants &&  Entertainment‐> Movie_Theaters && HOME  Segments‐> Demographic‐>Income‐>50k_74k  && HOME Segments‐>Demographic‐>Income    ‐>75k_99k; }; @PlaceIQ
  30. 30. Behavioral Graph Big Box A Big Box B @PlaceIQ
  31. 31. Big Box B Detail @PlaceIQ
  32. 32. Technical Architecture Enterprise Data Normalize HDFS platform component data Location Data Ingest PIQL Enterprise Connector Output (Visualization, Reporting) Analytics Base Map Tiles Customer Behaviors Customer Segmentation @Kognitio
  33. 33. Performance Became a Massive Issue 500 node hadoop cluster  =  20 hours of processing time @PlaceIQ
  34. 34. Enter Kognitio Transferred to in memory database  and obtained advanced performance  on clustering, querying and  multidimensional analysis Completely unstructured approach to  queries enabling you to ask questions  as they come to you, get answers  returned quickly and iterate @Kognitio
  35. 35. Kognitio Delivers Unrestricted Answers, Quicker THEN NOW 500 node hadoop cluster  =  20 hours of processing time 500 node hadoop cluster  =  20 hours of processing time ½ terabyte system  =  20 minutes ½ terabyte system  =  20 minutes @Kognitio
  36. 36. Why PlaceIQ? Consumer InsightsData-Driven Q/A Process Patented Platform Unparalleled Audiences Innovation @PlaceIQ
  37. 37. Thank you.
  38. 38. 39 Recognized by Industry Analysts Forrester Wave™: Enterprise Data  Warehouse, Q4 ’13  Gartner Magic Quadrant for Data  Warehouse DBMSs ‐ 2014 #GartnerBI
  39. 39. Booth # 421  @PlaceIQ @mphnyc @Kognitio#GartnerBI #DataSci

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