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Understanding experience:
a mobile in-store shopping
case study

Sheldon Monteiro
John Cain
T J McLeish

Image	
  by	
  Ma...
§ A brief history of now: technology
and the connected consumer

The story in 3
parts

§ Moving from what to why:
develo...
01

A brief history of now
Private & Confidential, SapientNitro © 2013
http://info.cern.ch/hypertext/WWW/MarkUp/Tags.html
Private & Confidential, SapientNitro © 2013
2013
Private & Confidential, SapientNitro © 2013
Private	
  &	
  Confiden7al,	
  SapientNitro	
  ©	
  2013	
  	
  
Image	
  by	
  Google	
  Chrome	
  team	
  
customer
experience

8
enterprise
technology

9
Source:	
  go-­‐globe.com	
  
The potential of all this data?
Discovering the new,
beyond the edge
of current understanding
THE MORE THINGS CHANGE…

Copernicus, 1473

Étienne-Jules Marey
1901

Art Olson
Scripps Research Institute
2011
Data is next

13
But wait!

14
What is the value of a
single piece of data?

© 2013 SAPIENT CORPORATION | CONFIDENTIAL

Image:	
  Science	
  Educa/on	
  ...
What is the value of a
single piece of data?

© 2013 SAPIENT CORPORATION | CONFIDENTIAL

Image:	
  Science	
  Educa/on	
  ...
02

Moving from what to why
Instrumenting models of context, not isolated events
Common mistake:
poke and hope

Plan

“What is” today:
Data From the Real World

© 2013 SAPIENT CORPORATION | CONFIDENTIAL
...
A better way?
The more info AND
context we have,
the better!

Models of
understanding
Overall Business
Objectives
Insight
...
20
Hugh	
  Dubberly	
  EPIC	
  Keynote	
  ‘Why	
  Modeling	
  is	
  Crucial	
  to	
  Design	
  and	
  Design	
  Research’	
  ...
Hugh	
  Dubberly	
  EPIC	
  Keynote	
  ‘Why	
  Modeling	
  is	
  Crucial	
  to	
  Design	
  and	
  Design	
  Research’	
  ...
Hugh	
  Dubberly	
  EPIC	
  Keynote	
  ‘Why	
  Modeling	
  is	
  Crucial	
  to	
  Design	
  and	
  Design	
  Research’	
  ...
Hugh	
  Dubberly	
  EPIC	
  Keynote	
  ‘Why	
  Modeling	
  is	
  Crucial	
  to	
  Design	
  and	
  Design	
  Research’	
  ...
THE CONNECTED
RETAIL EXPERIENCE
Why are sales shrinking in my store?
Where do people enter the baby section?
Are smartphon...
How did we get there?
Private & Confidential, SapientNitro © 2013
Start with a Question
Often a question is framed in a way that
limits the power of data and analytics to
reveal a new para...
Where do good questions come from?

Aim of “question”
-answering a
broad or specific
level of
understanding

ASer	
  Donald...
Testable hypotheses
Instrumention plan

+

In-store cart sensing
-location
-movement

Digital Activity 24x7
–smartphone
-l...
03

Analytic Architectures
Engines for insight
A look behind the curtain	
  

Sense
§  Data e.g. noise
levels, light levels,
accelerometer,
selective video
§  “Anchore...
A look behind the curtain	
  

Sense

Store

Analyze
Event	
  
Event	
  
Storage
Storage

Distributed	
  
messaging	
  

E...
A look behind the curtain	
  

Sense

Store

Analyze
Event	
  
Event	
  
Storage
Storage

Distributed	
  
messaging	
  

G...
Connecting questions to sensors & analytics…

© 2013 SAPIENT CORPORATION | CONFIDENTIAL

34
Sensor library
Example ‘hard sensors’

What measured

Use/application

Microphone

Acoustic, sound, vibration

Sound recor...
From sensors to ‘instruments’
Custom hardware
bag tag

regime tracking
Position and gesture analysis

pill bottle motion d...
1.  Dare to question everything
–all understanding begins in doubt
2.  Expand your toy box
–no single data source contains...
SapientNitro is a new breed of Agency
redefining storytelling for an always-on world.
Highly Awarded for
Creative Excellenc...
Sheldon Monteiro
www.linkedin.com/in/sheldonmonteiro
John Cain
www.linkedin.com/pub/john-cain/0/285/a74
T J McLeish
www.li...
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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.

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  1. 1. Understanding experience: a mobile in-store shopping case study Sheldon Monteiro John Cain T J McLeish Image  by  Ma*  Bri*  
  2. 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. 3. 01 A brief history of now
  4. 4. Private & Confidential, SapientNitro © 2013
  5. 5. http://info.cern.ch/hypertext/WWW/MarkUp/Tags.html Private & Confidential, SapientNitro © 2013
  6. 6. 2013 Private & Confidential, SapientNitro © 2013
  7. 7. Private  &  Confiden7al,  SapientNitro  ©  2013     Image  by  Google  Chrome  team  
  8. 8. customer experience 8
  9. 9. enterprise technology 9
  10. 10. Source:  go-­‐globe.com  
  11. 11. The potential of all this data? Discovering the new, beyond the edge of current understanding
  12. 12. THE MORE THINGS CHANGE… Copernicus, 1473 Étienne-Jules Marey 1901 Art Olson Scripps Research Institute 2011
  13. 13. Data is next 13
  14. 14. But wait! 14
  15. 15. What is the value of a single piece of data? © 2013 SAPIENT CORPORATION | CONFIDENTIAL Image:  Science  Educa/on  Resource  Center,  Carleton  College  
  16. 16. What is the value of a single piece of data? © 2013 SAPIENT CORPORATION | CONFIDENTIAL Image:  Science  Educa/on  Resource  Center,  Carleton  College  
  17. 17. 02 Moving from what to why Instrumenting models of context, not isolated events
  18. 18. Common mistake: poke and hope Plan “What is” today: Data From the Real World © 2013 SAPIENT CORPORATION | CONFIDENTIAL In Market Execution
  19. 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. 20
  21. 21. Hugh  Dubberly  EPIC  Keynote  ‘Why  Modeling  is  Crucial  to  Design  and  Design  Research’   21
  22. 22. Hugh  Dubberly  EPIC  Keynote  ‘Why  Modeling  is  Crucial  to  Design  and  Design  Research’   22
  23. 23. Hugh  Dubberly  EPIC  Keynote  ‘Why  Modeling  is  Crucial  to  Design  and  Design  Research’   23
  24. 24. Hugh  Dubberly  EPIC  Keynote  ‘Why  Modeling  is  Crucial  to  Design  and  Design  Research’   24
  25. 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. 26. How did we get there? Private & Confidential, SapientNitro © 2013
  27. 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. 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. 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. 30. 03 Analytic Architectures Engines for insight
  31. 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. 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. 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. 34. Connecting questions to sensors & analytics… © 2013 SAPIENT CORPORATION | CONFIDENTIAL 34
  35. 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. 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. 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. 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. 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
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