Sense Networks

1

Mining Customer Location Data to Provide Business Intelligence

Tony Jebara

Sense Networks

2

www

A network of online places

Sense Networks

3

facebook

A network of online people

Sense Networks

4

From online to real networks? What’s next?

a network of real places

a network of real people

Online data is easy to get, what about the real world?

GPS LBS

Location Data

Sense Networks

5

GPS, LBS, and location data

GPS LBS

Location Data

VEHICLES APPS DEVICES CARRIERS

SENSE NETWORKS ANALYSIS, NETWORKS OF PLACES & PEOPLE, SEGMENTATION

Marketing Advertising Collaborative Filtering

Social Recommendation

Search

Sense Networks

6

The Old View of LBS Data

Single Ping @ Starbucks: No personalization, no targeting Can’t use a single ping, too much error in space & time… It’s not just about when and where but also about who

=

Sense Networks

7

The New View of LBS Data

Location history for understanding & personalization Store data over space and time to overcome accuracy issues Lesson: save your LBS data to segment your customers!

+ = Health & Fitness Young Adult Outdoorsy

Sense Networks

8

Who Has Historical Data?

Carriers

Infrastructure

Device Mfgrs

Ericsson Alcatel-Lucent

Nokia Siemens Networks

RIM

Google

Bank of America Polaris

TruePosition

Nokia

Vodafone

Samsung

Other

WPP

AT&T

Newfield

Airsage

DATA

Google

Qualcomm Publicis

Omnicom

Sprint

GloPos

Verizon T-Mobile

User

Ad/Content

Nielson

Loc-aid Pinch

WaveMarket

Sense Networks

9

Infrastructure Providers 2009 Q1

missing: small & enterprise players: Newfield, Airsage, ZTE, etc.

• Infrastructure looking for new competitive advantages • Along with Carriers: looking sources of top line growth via

• targeting, advertising, content delivery...

Sense Networks

10

Carriers and Prepay

• Prepay carriers know less and less about their customers • Now: Churn management & segmentation w/o billing info • Soon: targeting, advertising, content delivery...

Sense Networks

11

Ericsson Ad Orchestrator

• Infrastructure solution to ad targeting/segmentation… • How to get the segments?

Sense Networks

12

MacroSense Segmentation • Sense Networks’ solution: Convert massive per-user call/location data segments

Location & Call Data

Sense Networks MacroSense

Segmentation

SOURCE MONTHS PINGS USERS

18 10b 1.4m

4 8b 4m

12 2b 4m

MDN WEALTH AGE CHURN TRAVELER

6462123442 200,000 46 6% 30%

9174341434 35,000 43 4% 20%

6468762413 150,000 31 11% 85%

Sense Networks

13

MacroSense Architecture

Demo Tree

Server

Learning Engine

User Segment Output

Raw Customer

Call & Location

Data

Features1….

Features2.…

Features3….

Features4….

Features5…

……

……

……

……

……

……. SIC Tree

Server

Proprietary Non-PII Profiles

Delta

Normalizer

Roller

Sense Networks

14

GPS and location data

10+ million devices giving (lat,long,time,acc)… lingua franca

Sense Networks

15

Sense Networks

16

CitySense: where is everyone • Citysense: real-time density of users at every street corner • Poisson models find most active bars/restaurants

Sense Networks

17

Next: where’s everyone like me Need to have a network of people

Each dot is a user

Dot’s color is user’s social cluster

Sense Networks

18

Network of People who is like whom? who co-locates with whom?

Sense Networks

19

Network of People Hard to say if User A is like User B…

… don’t just look if they co-locate physically … check if they overlap semantically (network of places)

User A User B

Sense Networks

20

Network of Places is place A like place B? Look at each place’s Flow, Commerce & Demographics

Sense Networks

21

Network of Places: Flow Look at flow A to B

Markov transition

Apply MVE on graph

Color code clusters in graph

Sense Networks

22

Encoding a Person’s Lifestyle 9 example users’ lifestyle matrices

no PII information

compute pair-wise similarity from matrices = how much two people co-locate semantically

… can then use machine learning to segment, predict, etc.

Sense Networks

23

Churn Advertising Marketing Collaborative Filtering Demographics++

People Network Segmentation

Sense Networks

24

Network of People: Segments

“Young & Edgy” • Out every night in young, racially diverse, low income neighborhoods

“Weekend Mole” • Out occasionally on weeknights, typically middle-aged, Latino, middle-income neighborhoods

“Mature Homebody” • Rarely goes out, typically spends nights in mature, white, higher income neighborhoods

Segmentation: Standard Output

25

Proprietary & Confidential

Convert call, location & lbs data into Claritas type segments e.g. Pepsi wants to send ad to only “Young&Edgy” Move lbs into established advertising industry…

Raw Customer

Call & Location

Data

01 - Millionaires: Millionaires, as the name implies, collects America’s most successful achievers and old money. It ranks first for both median and per-capita income, salaries, self and investment income, home values and net worth, and ranks second behind the Urban Brahmins in higher education and professional/managerial occupations.

02 - Country Clubbers: Country Clubbers are one rung down from the Millionaires and ranks second on all measures of income and affluence and third in college and postgraduate education. These expensive suburbs are packed into our three great metropolitan strips, dubbed “Bos-Wash”, “Chi-Pitts”, and “San-San”, with the bulk (40%) in Bos-Wash. Much of this wealth is new money, earned and freely spent by captains of business and technology.

03 - Turbo Boomers: Turbo Boomers rank first in the large “baby boomers” age group of 35-44 and are concentrated in the rapid growth cities of Atlanta, Washington DC, Dallas, Denver, Los Angeles and San Francisco. They are heavy hitters, highly educated and employed in executive and professional occupations ranking second in marriage and fourth for household income.

…

MACROSENSE

SEGMENTATION

Segmentation: Car Buyer

26

Proprietary & Confidential

High end large cars for wealthy, middle age families with kids

Low end small cars for lower income, middle/older age consumers

Recommendation & Marketing based on the Network Identifying Active New Car Shoppers

Sense Networks

27

Case Study: Churn

Sense Networks

28

Case Study: Apps

Sense Networks

Discussion: New LBS Opportunity

• LBS and Location more about segmenting customer • Store your data to understand your customer • Use MacroSense to convert LBS history into segmentation

• Infrastructure (Ericsson, Alcatel, NSN,…): best data sources • Carriers (AT&T, Sprint,…): want growth, ads, targeting • Advertisers (WPP, Nielsen,…): want segmentation

• Sense Networks: segments from location & call data • Enables online business, segmentation, personalization …from Offline LBS data