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
- 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 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. Title Subtitle subtitle subtitle subtitleContextualizing The Customer Through Location Intelligence April, 2014
- 9. 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
- 10. 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
- 12. Top Brands Using PlaceIQ AUTO RETAIL TECH/TELECOM ENTERTAINMENT CPG FINANCIAL AND MORE… @PlaceIQ
- 14. 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
- 15. 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
- 16. 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
- 17. 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
- 18. Location Ingest
- 19. 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
- 20. Data / Base Map @PlaceIQ
- 21. 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
- 22. 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
- 26. 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
- 28. 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
- 30. 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
- 33. 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
- 34. Performance Became a Massive Issue 500 node hadoop cluster = 20 hours of processing time @PlaceIQ
- 36. 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
- 37. Why PlaceIQ? Consumer InsightsData-Driven Q/A Process Patented Platform Unparalleled Audiences Innovation @PlaceIQ
- 38. Thank you.
- 40. Booth # 421 @PlaceIQ @mphnyc @Kognitio#GartnerBI #DataSci