1 Alexei Poliakov alexei@locomizer.com @poliakov

Rocket Space, SF

B2B, London

Minority Report 2.0: Geo-

Behavioral Personas for

Next Generation Marketing

and Beyond

2

Billions of location signals are

generated by smartphone users daily

3

New opportunity to understand

why customers are here and now

and predict their next moves

4

THE FUNDAMENTAL FLAW OF LOCATION-DATA

ANALYSIS AND MARKETING

1.Primitive geo-fencing

2.Counting

3.No context

4.Rational

5.Perception

5

LOST IN THE FOREST, STUCK IN THE TREES/

LOCAL VS GLOBAL

6

THE FAMOUS EBBINGHAUS ILLUSION/

CONTEXT IS EVERYTHING

7

MAP-BASED PARADOX

“Rather than reproducing pictures in the bran, research results

indicate that what we perceive is a systematically altered

version of reality. Part of what we “see” are the opportunities for

and costs of acting on the environment.”

THE LESS RELEVANT THE ENVIRONMENT THE SMALLER

THE DEVIATION OF THE PERCEIVED FROM MAP-BASED

REALITY…

WHICH MEANS

… “UNDERSTANDING OF PEOPLE BY ASSUMING THEY

ARE UNMOTIVATED, NON-ENGAGED AND EMOTIONLESS

8

THERE MUST BE ANOTHER WAY MAP…

9

10

THE MAP…

11

Reach your

audience the way

Nature intended:

Biological

Intelligence by

Locomizer

12

MOVING WITHOUT A “MAP”

13

LIVING WITHOUT A “MAP”

r3 r4 r5

14

WE PROFILE PEOPLE BASED ON SCIENTIFIC

DISCOVERY

Research on spatial behavior

in live systems

Biological Intelligence Technology –

Geo-Behavioral Interest Profiling

Cell movements and interactions People movements and interactions

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RESULTING IN GEO-BEHAVIORAL INTEREST GRAPH (GLOBAL DATABASES OF USER AND PLACE INTEREST PROFILES)

Arts Shopping Eating Sports Crafts Office Financial Leisure Auto Travel Transport

36 75 80 59 10 26 17 62 48 12 54

Affinity Score Category

Place Profiles User Profiles

WEEKDAY, 4PM

Locomizer Algorithm

ID+ lat/lon +

timestamp

ID + lat/lon +

timestamp

ID + lat/lon +

timestamp

16

Historical whereabouts wrapped in

Biological Intelligence result in user

interest profile – a real-life based

360° customer view

17

DIGITISED PROFILES ARE READY FOR TARGETING,

MATCHING AND MODELLING

Cinemagoer Eating lover 1 Eating lover 2

0

1

2

3

4

5

6

7

8

9

10

**** *** ** *Po

pu

lati

on

size

wit

h t

he

po

siti

ve in

tere

st (%

)

Overal Confidence Level

Gay

0

10

20

30

40

50

60

70

**** *** ** *Po

pu

lati

on

size

wit

h t

he

po

siti

ve in

tere

st (%

)

Overall confidence level

Shopping

0

10

20

30

40

50

60

**** *** ** *Po

pu

lati

on

size

wit

h t

he

po

siti

ve in

tere

st (%

)

Overall confidence level

Nightlife

0

10

20

30

40

50

60

70

**** *** ** *

Po

pu

lati

on

size

wit

h t

he

po

siti

ve in

tere

st (%

)

Overall confidence level

High Street fashion shopping

SIZE OF LOCOMIZER’S AUDIENCES BY

INTEREST BROADNESS AND RANGE

Broad Interest

Niche Interest

Short Middle Broad

Range

Short Middle Broad

Range

Short Middle Broad

Range

Short Middle Broad

Range

‘Calvin Klein’ audience

Broad

Interest

Niche

Interest

DISTRIBUTION OF THE INTERESTS SCORES FOR

FOR THE MIDDLE-RANGE INTEREST

Shopping High Street fashion shopping

Nightlife

60% of the population 52% of the population

18% of the population 4% of the population

‘Calvin Klein’ audience

20

PLACE PROFILING

USE CASE

Translate individual location

history (from locomizer’s data

pool) into targetable interest

profiles

Pinpoint customers with

Interests or Intents that make

them receptive to after-work

drinks targeting (based on

target persona description)

Build heatmaps based on user

profiles of people with high

affinity to after-work drinks

Discover optimal sites to target after-

work drinks crowd (18-39 yr old

professionals) by day part

How it works:

21

DELIVERABLE

Pinpoint places as granular as

a street level with people

whose Interests make them

receptive to after-work drinks

targeting by hour, day, week or

month

Intelligently decide WHEN and

WHERE to run your targeting

campaign to achieve the

maximum effect

Know daily whereabouts of crowd with

high interest to after-work drinks

all day

FRI

SAT

5-9pm

MON

pm

WED

am

Heatmap will allow to:

22

DEMO

The proposed interactive heatmap will pinpoint sites

(500x500m polygons with a street level granularity) with

different levels of affinity to ‘after-work drinks’ targeting by day

part

This will enable the brand to:

– Pinpoint places with people whose interests make them more receptive

to your OOH or mobile targeting campaigns

– Intelligently decide when and where to run your OHH and/or mobile

campaign

– Influence your creative recommendations, making your product more

relevant to location

23

[Sample] All day (8am-11pm) heatmap shows areas

with different levels of affinity to eating/drinking

24

[Sample] Working hours (10am-5pm) heatmap

shows changes in affinity from all day heatmap

25

[Sample] Out-of-working hours (5pm-10pm)

heatmap shows dynamic changes in affinity

26

[Sample] Night time (11pm-8am) heatmap

27 Photo credit: by Eva Rinaldi Celebrity and Live Music Photographer

Expected Impact

Discover non-obvious sites for

targeting

Increase foot traffic to key venues

driven by campaign relevancy

Create brand uplift by selecting

optimal target sites

MAKE EVERYBODY HAPPY

28

Minority Report 2.0

Minority Report 1.0

29

Minority Report 1.0

Minority Report 2.0

30

Minority Report 2.0

Minority Report 1.0

Photo Credit: http://goo.gl/p9sYa

- matching with behaviour

- changing behaviour

31 Alexei Poliakov alexei@locomizer.com @poliakov

Rocket Space, SF

B2B, London

Thank you!

32

APPENDIX

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GEO-BEHAVIORAL PROFILING IN

LONDON, September-October 2014

https://demo.locomizer.com/map/London

Area: 25 km radius around London

Unique users in the sample database: 206164

Number of historic location signals: 3261665

Source: geo-tagged tweets

Statistics:

>£0.2 mln active users within M25, which represents ~2.5% of the total population in London.

Gender: 33% male, 25% female, 10% unisex, 32% not specified

Device type: 48% iphone, 19% android, 18% sent through a web browser (could be any device), 2% ipad, 6% sent

through instagram

33

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PROFILING OF PLACES BASED ON

HISTORIC AUDIENCE INTERTESTS TO

‘NIGHTLIFE’ AND ‘GAY’ ACTIVITIES

PARIS, October-November 2014

https://demo.locomizer.com/map/Paris

Area: 8 km radius around Paris

Unique users in the sample database: 59 998

Historic location signals: 1 342 370

Identified males in the sample database: 17 922

Historic location signals by identified males: 254 460

Source: geo-tagged tweets

(c) Locomizer.com April 2015 alexei@locomizer.com

35

GEO-BEHAVIORAL PROFILING IN

Tokyo, July-December 2014

https://demo.locomizer.com/map/Tokyo

Area: 25 km radius around Tokyo

Unique users in the sample database: 126 752

Number of historic location signals: 479429

Source: mobile operator

35

36

MCDONALD’S CASE

WHEN & WHERE to target?

WEEKDAY, 4PM-5PM

Locomizer drove both CTR and conversion rates by 50% and 30% correspondingly, resulting in an incremental increase in footfall of 7,000 customers in MacDonald’s

restaurants in one month

Locomizer API Locomizer partner’s

Hyperlocal Ad Platform

place context

NEARBY McD

50% CTR

McDonald’s made data-driven decisions of WHEN & WHERE to send mobile ads based on Locomizer’s extrapolated view of footfall by fastfood interest and time, resulting in 50% lift in CTRs in comparison to non-targeted ads.

TARGET AUDIENCE

GEO-BEHAVIORAL MAPS CHANGE OVER TIME

(EXAMPLE: LUXURY INTEREST)

week1 week2 week3 week4 week5 week6

average

37