PDF of presentation given by John Cain, Sheldon Monteiro, Thomas McLeish for Strata London 2013: Using big data to understand the mobile in-store shopping experience.

1. Understanding experience:
a mobile in-store shopping
case study
Sheldon Monteiro
John Cain
T J McLeish
Image
by
Ma*
Bri*

2. § A brief history of now: technology
and the connected consumer
The story in 3
parts
§ Moving from what to why:
developing intelligence from the
internet of things
§ Engines for insight: analytic
architectures
2

3. 01
A brief history of now

4. Private & Confidential, SapientNitro © 2013

5. http://info.cern.ch/hypertext/WWW/MarkUp/Tags.html
Private & Confidential, SapientNitro © 2013

6. 2013
Private & Confidential, SapientNitro © 2013

7. Private
&
Confiden7al,
SapientNitro
©
2013
Image
by
Google
Chrome
team

8. customer
experience
8

9. enterprise
technology
9

10. Source:
go-­‐globe.com

11. The potential of all this data?
Discovering the new,
beyond the edge
of current understanding

12. THE MORE THINGS CHANGE…
Copernicus, 1473
Étienne-Jules Marey
1901
Art Olson
Scripps Research Institute
2011

13. Data is next
13

14. But wait!
14

15. What is the value of a
single piece of data?
© 2013 SAPIENT CORPORATION | CONFIDENTIAL
Image:
Science
Educa/on
Resource
Center,
Carleton
College

16. What is the value of a
single piece of data?
© 2013 SAPIENT CORPORATION | CONFIDENTIAL
Image:
Science
Educa/on
Resource
Center,
Carleton
College

17. 02
Moving from what to why
Instrumenting models of context, not isolated events

18. Common mistake:
poke and hope
Plan
“What is” today:
Data From the Real World
© 2013 SAPIENT CORPORATION | CONFIDENTIAL
In Market
Execution

19. A better way?
The more info AND
context we have,
the better!
Models of
understanding
Overall Business
Objectives
Insight
Innovation
Working Business
Hypothesis
“What is” today:
Data From the Real World
© 2013 SAPIENT CORPORATION | CONFIDENTIAL
Plan
Measure &
Optimize
In Market
Execution

20. 20

21. Hugh
Dubberly
EPIC
Keynote
‘Why
Modeling
is
Crucial
to
Design
and
Design
Research’
21

22. Hugh
Dubberly
EPIC
Keynote
‘Why
Modeling
is
Crucial
to
Design
and
Design
Research’
22

23. Hugh
Dubberly
EPIC
Keynote
‘Why
Modeling
is
Crucial
to
Design
and
Design
Research’
23

24. Hugh
Dubberly
EPIC
Keynote
‘Why
Modeling
is
Crucial
to
Design
and
Design
Research’
24

25. THE CONNECTED
RETAIL EXPERIENCE
Why are sales shrinking in my store?
Where do people enter the baby section?
Are smartphones cannibalizing in-store sales?
25
Image
by
Internetretailing.net

26. How did we get there?
Private & Confidential, SapientNitro © 2013

27. Start with a Question
Often a question is framed in a way that
limits the power of data and analytics to
reveal a new paradigm
Questioning everything will create stratified
hierarchies of context
WHY?
WHY?
What are customers
doing in my store?
Retail example
“Why are my in-store sales shrinking?”
“What are people doing on their smartphones
in my store?”
Become “hunts”
Understand the shopping experience, not
just the buying moment.
“Birth of a sale” and the broader
customer journey

28. Where do good questions come from?
Aim of “question”
-answering a
broad or specific
level of
understanding
ASer
Donald
Stokes
Purpose/use
Context of imagined use
28

29. Testable hypotheses
Instrumention plan
+
In-store cart sensing
-location
-movement
Digital Activity 24x7
–smartphone
-laptops
-tablets
+
+
In-store observation
-video
-interviews
In-store engagement
-paths & heat maps
-attention
-interactions
Aggregated View
+
Transaction
data
Conversion data
-sales data
-shopping lists

30. 03
Analytic Architectures
Engines for insight

31. A look behind the curtain
Sense
§ Data e.g. noise
levels, light levels,
accelerometer,
selective video
§ “Anchored” to
people, places,
and things
Store
Analyze
§ Store + information
§ Machine learning,
extraction - real time and statistics
long term time series
§ Corroborating
§ “Lambda” architecture
evidence and
contextual awareness
© 2013 SAPIENT CORPORATION | CONFIDENTIAL
Model
§ Stories and typologies
of people and events
§ Narrated by hard data
§ Deduce meaning from
activity
31

32. A look behind the curtain
Sense
Store
Analyze
Event
Event
Storage
Storage
Distributed
messaging
Event
collector
1
Serialization
(Avro)
Event
Collection
Services
Event
collector
N
Stream
Services
Analysts
Analysts
Index
(ElasticSearch,
BigQuery,
CloudQuery,
etc)
Storm
Redis
Node
N
Indexing
A PI/
Console
Spark
streaming
MySQL
Samza
HDFS
Kafka
Node
N
Analysis
Random/
Interactive
Queries
Modeling
&
Visualization
Mahout
D3
ElasticSearch
Spark
Apache
Drill
Static
visualization
Web
pages
Custom
m ap
reduce/batch
jobs
Hive/Pig/Map
Reduce
PDF/PPTs
R
S3
Non-­‐blocking
I/O
using
Play!,
S cala,
Java
Sanity
testing,
build
algorithms,
etc
Kafka
Node
1
...
Non-­‐blocking
I/O
using
Play!,
Scala,
Java
Redis
Node
1
...
.
.
.
HBase
In
S tream
In
S tream
Processing
Processing
Engine
Engine
Querying
Querying
Model
Cloudera
Impala
Excel
sheets
Real
time
data
Archive
(S3/
HDFS
S ink)
ETL
Time
ranged
queries
External
sources
/
partners
Batch
A nalysis/Processing
output
Dynamic/real-­‐time
visualization
Visualization
services
(Non
blocking,
Play!,
S cala,
Java)
32

33. A look behind the curtain
Sense
Store
Analyze
Event
Event
Storage
Storage
Distributed
messaging
GridEyes
Event
collector
1
Event
Receivers
Collection
API
Serialization
(Avro)
Stream
Streams
Services
API
Non-­‐blocking
I/O
using
Play!,
S cala,
Java
Floor
viz
Sanity
testing,
build
algorithms,
Blob
etc
tracker
© 2013 SAPIENT CORPORATION
Analysts
APP
/
Analysts
demo
Messaging
...
Event
collector
N
Kafka
Node
1
Indexing
A PI/
Console
HBase
–
Time
series
Index
DB
(ElasticSearch,
BigQuery,
CloudQuery,
etc)
Storm
Services
Non-­‐blocking
I/O
using
Play!,
Scala,
Java
Spider
Redis
Node
1
...
.
.
.
HBase
In
S tream
In
S tream
Processing
Processing
Engine
Engine
Querying
Querying
Blob
detec7on
(in-­‐
Spark
stream)
streaming
MySQL
Model
Analysis
Random/
Interactive
Queries
Modeling
&
Visualization
Mahout
D3
ElasticSearch
Map
–
reduce
(custom
Apache
Drill
jobs)
Blob
Spark
detec7on
(batch
mode)
Custom
m ap
Static
visualization
D3
/
Processing
Viz
Web
pages
reduce/batch
jobs
Redis
Node
N
Samza
Kafka
Node
N
HDFS
Hive/Pig/Map
Reduce
PDF/PPTs
R
S3
Cloudera
Impala
Excel
sheets
Real
time
data
Archive
(S3/
HDFS
S ink)
ETL
| CONFIDENTIAL
Time
ranged
queries
External
sources
/
partners
Batch
A nalysis/Processing
output
Dynamic/real-­‐time
visualization
Visualization
services
(Non
blocking,
Play!,
S cala,
Java)
33

34. Connecting questions to sensors & analytics…
© 2013 SAPIENT CORPORATION | CONFIDENTIAL
34

35. Sensor library
Example ‘hard sensors’
What measured
Use/application
Microphone
Acoustic, sound, vibration
Sound recording, seismograph
Radio receiver, capacitive touch
Electric current, magnetic, radio frequency
GPS, NFC, RF detection, touchscreen
Chemical electrodes
Chemical presence, quantity
Fuel injector, smoke detector
Capacitive humidity sensor
Moisture, humidity
HVAC controls, weather, soil
Gas meter, water meter
Fluid velocity, flow
Utilities usage, air speed,
Accelerometer, bend sensor
Position, angle, displacement, acceleration
Human activity, movement, motion
Photodiode, photocell
Optical, light, imaging
Image recognition, light level detection
Load cell, piezoelectric sensor
Force, density, level
Digital home scale, vibration sensing
Thermistor, calorimeter
Thermal, heat, temperature
Digital thermometer
PIR motion sensor, RF transceiver
Proximity, presence
Distance gauge, motion detector
…and so on
35

36. From sensors to ‘instruments’
Custom hardware
bag tag
regime tracking
Position and gesture analysis
pill bottle motion detection
ambient environment sensor
hacked scales
bluetooth beacon
Device activity logging
© 2013 SAPIENT CORPORATION | CONFIDENTIAL
observation gnomes
36

37. 1. Dare to question everything
–all understanding begins in doubt
2. Expand your toy box
–no single data source contains the truth!
5 Key Takeaways
3. Create and instrument models of context
–models are central to human understanding
4. Beware the observer effect
–try to limit the impact of the measurement on
the observation
5. Data scientists must amp up hermeneutics
–it’s all about (your) intelligence
© 2013 SAPIENT CORPORATION | CONFIDENTIAL
37

38. SapientNitro is a new breed of Agency
redefining storytelling for an always-on world.
Highly Awarded for
Creative Excellence
Recognized for
Experience Design
#1 The Drum's Top 100 Digital Companies
#1 Advertising Age- Largest Digital Agency US
2012
2013 Gold Mobile Lion for RBS GetCash
Most Awarded Digital Agency at
Cannes 2010 and 2011
176 recognitions in 2013 to date
Forrester ranks SapientNitro a
leader in image-led and
transaction-led web design
Global Leader in
Omni-Channel Commerce
Leader in
Mobile Capabilities
Consistently Strong
Financial Performance
eCommerce Integrator for over 50 major
brands, including 2 of the top 5 most trafficked
eCommerce Platforms for Black Friday 2012,
Ranked #1 among US digital agencies
by Forrester, April 2012
Founded in 1990, ~11,000 People In 38
Offices in North & South America, Europe
and APAC, $1.1B+ 2012 annual revenues
#1 Agency
WE’RE HIRING!
© 2013 SAPIENT CORPORATION | CONFIDENTIAL
Page 38

39. Sheldon Monteiro
www.linkedin.com/in/sheldonmonteiro
John Cain
www.linkedin.com/pub/john-cain/0/285/a74
T J McLeish
www.linkedin.com/in/thomasjmcleish/
© 2013 SAPIENT CORPORATION | CONFIDENTIAL