SPS 33Location Analytics:The Next Generation

@mphnyc

Michael HiskeyBig Data Evangelist

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BigMessy

Unstructured NoisyData 

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We do the “hard stuff” of Big Data analytics

#DataSci

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Business Users have existing interfaces

Business Intelligence Tools and Dashboards, custom‐designed internal applications, etc. 

Business Analysts

Business Users

@Kognitio

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Move from dashboards to advanced analytics

create external script LM_PRODUCT_FORECAST environment rsintreceives ( SALEDATE DATE, DOW INTEGER, ROW_ID INTEGER, PRODNO INTEGER, DAILYSALES INTEGER ) partition by PRODNO order by PRODNO, ROW_IDsends ( R_OUTPUT varchar )isolate partitionsscript 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$IDbasesales[,2]<-(prod1$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

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A Platform for Advanced Analytics

• Business Applications

• Run advanced analytics in‐memory

• MPP CPU Scale‐out

• Persist data in Hadoop (and existing Data Warehouses)

Title

Subtitle subtitle subtitle subtitleContextualizing The Customer Through Location IntelligenceApril, 2014

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

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

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

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 abound

Petabyte Scale Storage and processing

Very few data points keyed to location

#analytics

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 rwJOIN rules r ON r.id = rw.rule_idLEFT OUTER JOIN time_period_profile_feat_ats tppfaON (tppfa.feature_name = rw.feature_name AND 

tppfa.attribute_name = rw.attribute_name)LEFT OUTER JOIN time_period_profile_weights tppwON (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

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

Location Data QualityHow do we confirm the accuracy of incoming lat/long data? 

1M

Centroid DetectionDetecting 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 DetectionDetecting spam devices (such as receiving 1M ad calls from one device in a short amount of time.

Transporter DetectionDetecting 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

The PlaceIQ Solution

AnalyticsContextualizing the Customer

Customer SegmentationCreating behavioral clusters from location histories

Data / Base MapOrganizing billions of data points

Location Ingest100x100 meter tile structure

Enterprise ConnectorIntegration with CRM / Enterprise

@PlaceIQ

Location Ingest

Taxonomy4K+ categories organize

our 40+ data sources

27 Time PeriodsPeriods mapped to

moments

Nearly 1 Billion TilesUSGS 100 x 100 meter

tile grid system

PlaceIQ’s Platform Organizes Hundreds of Billions of Data Points

@PlaceIQ

Data / Base Map

@PlaceIQ

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 & DiningGrocery, Clothing, Big Box, QSR, Buffet, Casual

EntertainmentMovies, Museums,Parks, Tourism, Bars

Consumer SpendingPurchase Data from Retail Partners

Auto & TravelDealership Lots, Airports, Hotels, Bus Stations

PIQ PrimeTimeTV Viewership from Set-Top boxes

And More…Photos, Social MediaEvents, etc.

@PlaceIQ

Hand-Made PolygonsHundreds of thousands built by

cartographers

Tile Based ScoringTiles are scored from 0 to 10

Leading PrecisionWe map to “rooftop” not

“driveway”

PlaceIQ Leads the Industry in Location Precision

@PlaceIQ

Enterprise Connector

@PlaceIQ

Customer Segmentation

@PlaceIQ

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

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

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

Location Analysis – Your Store

Place Visit RateDo devices shownMobile ads visit keyretail locations?

PreVisitWhere do visitorsgo before they visit key retail locations?

PIQ AnalyticsWhat is unique about the movements, behaviors, and demographics of an audience?

#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

Tile‐based

Tell me about this location

Tile‐based

Tell me about this location

@PlaceIQ

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

Behavioral Graph

Big Box A Big Box B

@PlaceIQ

Big Box B Detail

@PlaceIQ

Technical Architecture

Enterprise Data

NormalizeHDFS

platform component

data Location Data

Ingest

PIQL

Enterprise Connector

Output (Visualization, Reporting)

Analytics

Base Map

Tiles Customer Behaviors

Customer Segmentation

@Kognitio

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 multidimensional analysis

Completely unstructured approach to queries enabling you to ask questions as they come to you, get answers 

returned quickly and iterate

@Kognitio

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

Why PlaceIQ?

Consumer InsightsData-Driven Q/A Process

Patented Platform Unparalleled Audiences Innovation

@PlaceIQ

Thank you.

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Recognized by Industry Analysts

Forrester Wave™: Enterprise Data Warehouse, Q4 ’13 

Gartner Magic Quadrant for Data Warehouse DBMSs ‐ 2014

#GartnerBI

Booth # 421 

@PlaceIQ @mphnyc @Kognitio#GartnerBI #DataSci