Dr. Nina Meinel, Teamlead Digital Methods, Data & Technology | Marketing & Data Sciences - GfK SE Hendrik Wagenseil, Teamleader Geostatistics, GfK Geomarketing Within different Mobile and Location projects, cooperation with mobile carriers allows GfK Marketing and Data Science to analyze data of mobile devices. In Location Insights mobile data focuses on location or internet traffic information left in a carrier´s cellular network. Thus, taking a large sample of mobile phone users, e.g. all subscribers of a carrier, and analyzing their respective network data is a powerful approach to measure crowd movements across space and time. This information is regarded very valuable to a variety of clients, e.g. retailers, who are interested in objectively quantifying visitor traffic at selected areas or in understanding where their customers come from. Therefore, the objective of Location Insights is to turn the massive amount of mobile network data into aggregated figures about population and population movements for selected locations. To properly scale from number of subscribers to total population at a certain location, a relationship had to be established between sample and total population as a universe. A process chain will be presented demonstrating how a place of residence can be estimated from a series of network events, how extrapolation and visitor calculations can be performed at target locations. In Mobile Insights mobile data focuses on internet traffic information left in a carrier´s cellular network. The large scale of the data enables to derive app and browsing behavior as well as the long tail and spatial information. The opportunities this presents brands, advertisers and marketers is huge, and to truly maximise them, it’s essential to understand how mobile devices are used, and especially who is using them and when. In particular we are interested in characterizing those populations by socio-demographic profiles, by consumer-related attitudes and behaviors, which is within carriers CRM information partly or not available. We will demonstrate the process of investigating different approaches by enriching a huge data set and summarize findings, experiences on how to enrich mobile data.

1. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 1
From smart phones to smart places to smart profiles
Predictive Analytics World 2014, Berlin
Hendrik Wagenseil & Nina Meinel

2. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 2
From smart phones to smart places to smart profiles
Who we are
Traces of
consumers
& things
Big
Data
Deep &
granular with
known error
Reference
Data
We capture big data from
various sources, e.g.
• Social Media
• Electronic POS data
• Mobile operator data
• Location data
• Internet Metering data
• Cookie data
Processing and analysis
• Once the data entered our production system,
the typical process is then to clean it, apply
suitable taxonomies and compare or merge
it with reference data to create smart data
• Our Marketing and Data Scientists analyze
the data to ensure maximum and valid insights
Privacy
• It's imperative for us to ensure that
we're compliant with data privacy laws
Smart
Data
Combining the
best of both
worlds

3. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 3
From smart phones to smart places to smart profiles
Mobile data for market research
Mobile
network data
In Europe and North America, more than 4 out of 5
people are using mobile phones
(www.emarketer.com)
80% of US mobile phone users will use a
smartphone by the end of 2014
(www.emarketer.com)
On average, a smartphone creates more than 100
active network events per day (voice, text, data)
(LI STAT TEAM, own data analysis)
Mobile
network data
are valuable for
market research

4. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 4
From smart phones to smart places to smart profiles
Mobile data for market research
Locations
Each dot shows the location of a mobile
device when interacting with the network
(weekly traffic of 100 devices in SF).

5. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 5
From smart phones to smart places to smart profiles
Mobile data for market research
Traffic over time
Dwell time
Cross visits
Transients
Lifestyles
Consumer panel integration …
Dwell time
Cross visits
Transients
Lifestyles
Consumer panel integration …
Demographics
Catchment Future applications

6. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 6
From smart phones to smart places to smart profiles
What we are talking about
1. Mobile data for market research
2. Challenges: Privacy, uncertainty and bias
3. From smart phones to smart places
4. From smart phones to smart profiles

7. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 7
From smart phones to smart places to smart profiles
What we are talking about
1. Mobile data for market research
2. Challenges: Privacy, uncertainty and bias
3. From smart phones to smart places
4. From smart phones to smart profiles

8. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 8
From smart phones to smart places to smart profiles
Challenges: Privacy, uncertainty and bias
PRIVACY UNCERTAINTY BIAS
What
can red
dots…
What
can red
dots…
… tell us
about blue
dots?
… tell us
about blue
dots?

9. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 9
Dan Roam, Author of The Back of the Napkin
From smart phones to smart places to smart profiles
Challenges: Privacy, uncertainty and bias
Daily activity Customer structure
TechnologyLocation and usage
Market share
…
???

10. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 10
From smart phones to smart places to smart profiles
What we are talking about
1. Mobile data for market research
2. Challenges: Privacy, uncertainty and bias
3. From smart phones to smart places
4. From smart phones to smart profiles

11. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 11
GfK Reporting
From smart phones to smart places to smart profiles
Framework
Carrier
GfK Storage &
Analytics
• Tier one carrier
• >90% pop. coverage
• Sample size is approx.
10% of adult pop. or
better
• Tasks:
• Data collection
• Location fix
• Data anonymization
• Data packacking
• VPC hosted by GfK
• HDFS (Hadoop)
• Tasks:
• Quality control
• Data storage
• Data processing
• Data aggregation
• Web-Frontend
• GfK DRIVE based
• Tasks:
• Interactive data
vizalisation
• Static report creation
• Data export
ID encryption
key lifetime
restricted
SFTP
connection
ID encryption
key lifetime
restricted
SFTP
connection
data
aggregation
prior to
reporting
data
aggregation
prior to
reporting

12. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 12
Sample at target location
From smart phones to smart places to smart profiles
Sample vs. universe
0%
25%
50%
75%
100%
No Carrier
Other Carriers
Partner Carrier
Universe at place of residence
Projection
Census data:
Population
Gender
Age
Income
…
…
Place of residence of subscribers is required!

13. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 13
From smart phones to smart places to smart profiles
Predicting place of residence
Mapping of night sightings Redistribute sightings on grid
Consideration of the uncertainty:
Uncertainty:
A sighting is with a high probability within
this radius.
Grid:
100 x 100 meter grid – as the location of the
sightings are afflicted with an uncertainty, the
probability of the sighting will be redistributed
to the grid cells covered by the radius of the
uncertainty.
Uncertainty
Sighting
Traffic Days
Day 1 Day 2 Day 1&2
Weighted Sightings
S1 S2 S1 & S2
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1 1
1 1 1 1 1 1
1 1 1 1 1
1 1 1
1 1 1 1
1 1 1 1
1 1 1 1
1 1 1 1
1 1 1 1 1
1 1 1 1 1
2 2 2 2 1 1
2 2 2 2 1 1
1 2 2 2 1 1
1 2 2 2
4 % 4 %
4 % 4 % 4 % 4 %
4 % 4 % 4 % 4 % 4 % 4 %
4 % 4 % 4 % 4 % 4 % 4 %
4 % 4 % 4 % 4 %
4 % 4 %
8 % 8 %
8 % 8 % 8 % 8 %
8 % 8 % 8 % 8 %
8 % 8 %
4 % 4 %
4 % 4 % 4 % 4 %
4 % 12% 12% 4 % 4 % 4 %
12% 12% 12% 12% 4 % 4 %
8 % 12% 12% 12% 4 %
8 % 12% 4 %

14. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 14
From smart phones to smart places to smart profiles
Predicting place of residence
Determine cluster center by max(trafficdays) Overlap cluster with geographies
Center of home
location cluster
pt 1=0.3
pt 2=0.1
pt 3=0.6
Place of residence = probability distribution of
census/postalcode geographies

15. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 15
From smart phones to smart places to smart profiles
Sample vs. universe by geography
Sample by geography Universe by geography
Relationship of sample to universe by geography accounts for
regional bias

16. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 16
From smart phones to smart places to smart profiles
Sample by time
Device activity is time dependent
24.07. 25.07. 26.07. 27.07.
ActiveDevices
24.07. 25.07. 26.07. 27.07.
ActiveDevices
Projection is dynamic

17. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 17
Projected population of a census tract
From smart phones to smart places to smart profiles
Projection approach
0
1
2
3
4
24.07. 25.07. 26.07. 27.07.
Log10(UniqueDevices/hour)
Population Raw Count
Raw Count x Time Weight Raw Count x Time Weight x Tract Weight

18. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 18
Traffic and catchmentPresence Factor
From smart phones to smart places to smart profiles
Aggregation per target location
Presence Factor = 1
Presence Factor = 0
0 < Presence Factor < 1
Presence Factor =
(Site Area) ∩ (Uncertainty Area)
Uncertainty Area

19. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 19
From smart phones to smart places to smart profiles
What we are talking about
1. Mobile data for market research
2. Challenges: Privacy, uncertainty and bias
3. From smart phones to smart places
4. From smart phones to smart profiles

20. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 20
From smart phones to smart places to smart profiles
Mobile data for market research
Smart Profiles
In particular clients are interested in
characterizing those populations by socio-
demographic profiles, by consumer-related
attitudes and behaviors, which is within
carriers CRM information partly or not
available.
In particular clients are interested in
characterizing those populations by socio-
demographic profiles, by consumer-related
attitudes and behaviors, which is within
carriers CRM information partly or not
available.
Example of Profiles
Ways of Enrichment Individual Enrichment

21. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 21
METHOD
From smart phones to smart places to smart profiles
Framework for Modeling
• Translate people‘s device traces into meaningful
attributes
• Reduce dimensions & select important attributes for
modeling
Select approaches for individual enrichment
• Machine learning as RF / SVM
• Distance measures as Linear Imputation
• Traditional as PMM / Lasso
Select different prediction approaches
• Single nearest neighbor approach
• Block wise nearest neighbor approach
PREDICTION
EXPLANATORY

22. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 22
• Translate people‘s device traces based on operator data
From smart phones to smart places to smart profiles
How to translate people‘s device traces?
Operator Data
CRM • Contract
• Segments
• Device
Attributes are used for predicting smart profiles

23. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 23
From smart phones to smart places to smart profiles
Where to get smart profiles from?
Data Source
Data sources for Smart Profiles should contain
• Users for whom we observe operator data
& smart profile attributes
• All segments of a population in a valuable number
• Repeated measurement over time
Building a panel would be the best source and
can be rich of profiling attributes.
How does individual enrichment perform using operator data?
The answer is given by an internal validation using
• the panel for modeling and validation
• based on standard statistical techniques (e.g. holdout sample or cross validation)
From smart phones to smart Profiles using panel

24. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 24
Scenario XX
From smart phones to smart places to smart profiles
Performance criterias
XX
0.97
Bias
1
Corr
0.77
Corr yy
0.88
Corr yx
Bias & Corr Subgroups
0
10
20
30
40
50
0,0
0,2
0,4
0,6
0,8
1,0
PayG Corp Young London 2G
Performance criterias
Interpretation
2. Preserved structure in subgroups:
• Bias: Average Euclidean Distance between real / predicted structure
• Corr: Average Correlation between real / predicted structure
3. Preserve Correlation
Correlation between y’s
3. Preserve Correlation
Correlation between y’s and X’s
1. Preserve structure overall
Bias: Average Euclidean Distance
between real / predicted structure
1. Preserve structure overall
Corr: Average Correlation between
real / predicted structure

25. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 25
From smart phones to smart places to smart profiles
Performance using smart phones and smart places
Machine Learning
8.8
Bias
0.95
Corr
0.90
Corr yy
0.89
Corr yx
Bias & Corr Subgroups
0
10
20
30
40
50
0,0
0,2
0,4
0,6
0,8
1,0
Traditional
1.3
Bias
1
Corr
0.88
Corr yy
0.89
Corr yx
Bias & Corr Subgroups
0
10
20
30
40
50
0,0
0,2
0,4
0,6
0,8
1,0
Distance based
1.9
Bias
1
Corr
0.67
Corr yy
0.91
Corr yx
Bias & Corr Subgroups
0
10
20
30
40
50
0,0
0,2
0,4
0,6
0,8
1,0
Random*
0
Bias
1
Corr
0.02
Corr yy
0.16
Corr yx
Bias & Corr Subgroups
0
10
20
30
40
50
0,0
0,2
0,4
0,6
0,8
1,0

26. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 26
Traffic and catchmentPresence Factor
From smart phones to smart places to smart profiles
Aggregation per target location
Presence Factor = 1
Presence Factor = 0
0 < Presence Factor < 1
Presence Factor =
(Site Area) ∩ (Uncertainty Area)
Uncertainty Area

27. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 27
+49 911 395 3338
Dr. Hendrik Wagenseil
hendrik.wagenseil@gfk.com
Germany
+49 911 395 3961
Dr. Nina Meinel
nina.meinel@gfk.com
Germany
Contact

28. © GfK 2014 | Predictive Analytics – Berlin 2014 | November 28
THANK YOU