Wherecamp Navigation Conference 2015 - The state of the OSRM machine
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Transcript of Wherecamp Navigation Conference 2015 - Geo-behavioral personas for next generation marketing and...
1 Alexei Poliakov [email protected] @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
15
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
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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
32
APPENDIX
33
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
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34
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 [email protected]
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