SapientNitro Strata_presentation_upload

Understanding experience: a mobile in-store shopping case study

Image by Ma* Bri*

Sheldon Monteiro John Cain

T J McLeish

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§ A brief history of now: technology and the connected consumer

§ Moving from what to why: developing intelligence from the internet of things

§ Engines for insight: analytic architectures

The story in 3 parts

01 A brief history of now

2013


Private & Confiden7al, SapientNitro © 2013

Image by Google Chrome team

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customer experience

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enterprise technology

Source: go-‐globe.com

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

Étienne-Jules Marey 1901

Copernicus, 1473 Art Olson Scripps Research Institute 2011

THE MORE THINGS CHANGE…

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Data is next

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But wait!

What is the value of a single piece of data?

Image: Science Educa/on Resource Center, Carleton College © 2013 SAPIENT CORPORATION | CONFIDENTIAL

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

“What is” today: Data From the Real World

In Market Execution

Common mistake: poke and hope

Plan

© 2013 SAPIENT CORPORATION | CONFIDENTIAL

“What is” today: Data From the Real World

Overall Business Objectives

In Market Execution

A better way? The more info AND context we have, the better!

Models of understanding

Insight

Measure & Optimize

Working Business Hypothesis

Innovation

Plan


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

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Why are sales shrinking in my store? Where do people enter the baby section? Are smartphones cannibalizing in-store sales?

THE CONNECTED RETAIL EXPERIENCE

Image by Internetretailing.net

How did we get there?


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 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

What are customers doing in my store?

WHY?

WHY?

Retail example

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Where do good questions come from?

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

Purpose/use Context of imagined use

ASer Donald Stokes

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

Conversion data -sales data -shopping lists

Transaction data

+ + + +

Aggregated View

03 Analytic Architectures Engines for insight

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A look behind the curtain

Sense Store Analyze Model §  Data e.g. noise

levels, light levels, accelerometer, selective video

§  “Anchored” to people, places, and things

§  Store + information extraction - real time and long term time series

§  “Lambda” architecture

§  Machine learning, statistics

§  Corroborating evidence and contextual awareness

§  Deduce meaning from activity

§  Stories and typologies of people and events

§  Narrated by hard data


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Event Collection Services

Non-‐blocking I/O using Play!, Scala, Java

Event collector 1

Event collector N

Serialization(Avro)

Distributed messaging

.

.

.

Visualization services(Non blocking, Play!, Scala, Java)

Dynamic/real-‐time visualization

ETL

Event StorageEvent Storage

HBase

Index(ElasticSearch, BigQuery,

CloudQuery, etc)

S3

MySQL

Modeling & Visualization

D3

Web pages

PDF/PPTs

Staticvisualization

Excel sheets

Mahout

R

AnalysisRandom/Interactive Queries

Batch Analysis/Processing outputArchive (S3/HDFS Sink)

Stream Services

Non-‐blocking I/O using Play!, Scala,

Java

Real time data

Kafka Node 1

Kafka Node N

...

Redis Node 1

Redis Node N

...

Spark

Time ranged queries

Custom map reduce/batch

jobs

HDFS

In Stream Processing Engine


Storm

Spark streaming

Samza

External sources / partners

AnalystsAnalysts

Sanity testing, build algorithms,

etc

QueryingQuerying

ElasticSearch

Apache Drill

Hive/Pig/Map Reduce

Indexing API/Console

Cloudera Impala

AnalystsAnalysts


Sense Store Analyze Model

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Event Collection Services

Non-‐blocking I/O using Play!, Scala, Java

Event collector 1

Event collector N

Serialization(Avro)

Distributed messaging

.

.

.

Visualization services(Non blocking, Play!, Scala, Java)

Dynamic/real-‐time visualization

ETL

Event StorageEvent Storage

HBase

Index(ElasticSearch, BigQuery,

CloudQuery, etc)

S3

MySQL

Modeling & Visualization

D3

Web pages

PDF/PPTs

Staticvisualization

Excel sheets

Mahout

R

AnalysisRandom/Interactive Queries

Batch Analysis/Processing outputArchive (S3/HDFS Sink)

Stream Services

Non-‐blocking I/O using Play!, Scala,

Java

Real time data

Kafka Node 1

Kafka Node N

...

Redis Node 1

Redis Node N

...

Spark

Time ranged queries

Custom map reduce/batch

jobs

HDFS



Storm

Spark streaming

Samza

External sources / partners

AnalystsAnalysts

Sanity testing, build algorithms,

etc

QueryingQuerying

ElasticSearch

Apache Drill

Hive/Pig/Map Reduce

Indexing API/Console

Cloudera Impala

AnalystsAnalysts


Sense Store Analyze Model GridEyes

Spider

Floor viz

Receivers API

Streams API

Messaging Blob

detec7on (in-‐stream)

HBase – Time series

DB

Map – reduce (custom jobs)

Blob detec7on (batch mode)

D3 / Processing Viz

Blob tracker APP / demo


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Connecting questions to sensors & analytics…


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What measured Acoustic, sound, vibration Electric current, magnetic, radio frequency Chemical presence, quantity Moisture, humidity Fluid velocity, flow Position, angle, displacement, acceleration Optical, light, imaging Force, density, level Thermal, heat, temperature Proximity, presence

Sensor library

Example ‘hard sensors’ Microphone Radio receiver, capacitive touch Chemical electrodes Capacitive humidity sensor Gas meter, water meter Accelerometer, bend sensor Photodiode, photocell Load cell, piezoelectric sensor Thermistor, calorimeter PIR motion sensor, RF transceiver …and so on

Use/application Sound recording, seismograph GPS, NFC, RF detection, touchscreen Fuel injector, smoke detector HVAC controls, weather, soil Utilities usage, air speed, Human activity, movement, motion Image recognition, light level detection Digital home scale, vibration sensing Digital thermometer Distance gauge, motion detector

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pill bottle motion detection

bag tag regime tracking

Position and gesture analysis

bluetooth beacon

hacked scales

ambient environment sensor

observation gnomes

Custom hardware

Device activity logging


From sensors to ‘instruments’

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5 Key Takeaways

1.  Dare to question everything –all understanding begins in doubt

2.  Expand your toy box –no single data source contains the truth!

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


Recognized for Experience Design #1 Agency

#1 The Drum's Top 100 Digital Companies #1 Advertising Age- Largest Digital Agency US 2012

Highly Awarded for Creative Excellence 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 eCommerce Integrator for over 50 major brands, including 2 of the top 5 most trafficked eCommerce Platforms for Black Friday 2012,

Leader in Mobile Capabilities Ranked #1 among US digital agencies by Forrester, April 2012

Consistently Strong Financial Performance Founded in 1990, ~11,000 People In 38 Offices in North & South America, Europe and APAC, $1.1B+ 2012 annual revenues

SapientNitro is a new breed of Agency redefining storytelling for an always-on world.

WE’RE HIRING! © 2013 SAPIENT CORPORATION | CONFIDENTIAL


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/

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