ARCHAN MISRA SINGAPORE MANAGEMENT UNIVERSITY

Mobile Analytics: The Ongoing

Revolution in Consumer Sense-making

THE BIG PICTURE

Unique Insights into Human

Behavior in Urban Settings

=

UNDERSTANDING CONSUMERS

Offline

•Scanner Data

•Consumer Panel Data

Online

•Clickstream Analysis

•Online Search Data

In-Store

•Movement & Navigation

•Activity Patterns & Insights

Long Term Purchase

Behavior

Medium-term Browsing

and Search Behavior

Real-time Adaptation

and Intervention

CONSUMERS & SOCIAL INTERACTION

ONLINE BROWSING &

PURCHASE

PHYSICAL WORLD IN-

MALL/STORE BEHAVIOR

Collaborative

Filtering

Influence

Networks and

Propagation

Opinion &

Sentiment

Mining

Who are you

with?

What are you

doing together?

What parts of your

in-store browsing

are similar?

What are you (not)

interested in?

The Analogue?

MOBILE: JUST A BETTER SHOPPING TOOL?

•Only user-initiated

actions & preferences

•No system-inferred

adaptation to implicitly

infer consumer

activities/preferences.

What’s Missing?

Reproduced from: M. Sneathen, MobileU 2013 Summit, Heartland Mobile Council

Mobile Analytics & LiveLabs

Real-time Mobile

Sensing (Activity, Indoor location,

Browsing, SMS ...)

Real-time Individual and

Server Group Analytics (Dynamic Group Detection, Queuing,

Preferences)

Context-Driven

Intentions (Incentives, Promotions,

Recommendations ...)

AN EXAMPLE OF LIVELABS’ CAPABILITIES

7

LiveLabs Cloud Service

5 minutes later

Lifestyle

Company

If a group of 4 or more people exit

from Café after sitting down for 10

minutes, send SMS with a “Movie

Discount”

10 minutes later

4 in a group

sitting down at

a Café

4 in a group left

after 10 mins

LiveLabs software

continuously monitors

(location, activity, …)

Show this

notification and get

20% on all Movies

RESEARCH THREADS

• Analytics: Queuing Detection

• Analytics: Group Detection

• In-Store Shopper Classification

QUEUING 101

Japan

• Americans spend 4 years of their lives queuing.

• Average wait time at lunch in Singapore ~= 10+

mins

Western: Nothing is certain in life but death and taxes

Asian: Nothing is certain in life but death and QUEUING

Singapore

QUEUING: 3-TIER DETECTION ARCHITECTURE

QUEUING: CHALLENGES & SOLUTIONS

Variability in

Service Times

Concurrent HA

Detection

Start & End of Queue

Detection

Queue class Service Time (seconds)

Min. Ave. Max. Stdev.

Airport check-in (*1) 10.7 102.1 421.7 136.9

Airport boarding 4.3 7.3 10.9 1.7

Café and food court 10.3 32.9 77.0 17.9

Movie ticketing (*1) 5.0 7.9 11.5 2.3

*0: Numbers were based on serving (“dequeue”) intervals at the head of the queue.

*1: Multiple serving counters serving for one queue.

Queue detected at any profile

Location info.-based trigger

Leave detected

Queue

detection

Leave

detection

Disabled

“Food court” FrameLen=48sec

“Air. Checkin” FrameLen=120sec

“Air Boarding” FrameLen=10sec

Detection of signature activity of “Queue Leaving” e.g., “5 sec. continuous walking”

RESEARCH THREADS

• Analytics: Queuing Detection

• Analytics: Group Detection

• In-Store Shopper Classification

13

• Use spatiotemporal analytics to

identify individual vs. group

relationships among visitors. recommendations

• Identify group-based social

relationships.

• Predict future

locations/activities likely to be

visited/performed by

individual/group.

GROUP DETECTION ON INDOOR

MOVEMENT DATA

•Indoor Location Data

• With +-4/5 meter accuracy

•Dense, Indoor Urban Spaces

14

Unified Metric Learning Framework

● Combines spatial coordinates, semantic location labels and activity profiles

Visualization of group and individual movement patterns

● Test on LiveLabs testbeds (SMU, Malls)

● Combine with observations of social media data

Real-time measurement of

● User indoor location

● User context (e.g., sitting, walking)

ALGORITHM & RESULT

Outperforms individual trajectory or

social media based group detection

RESEARCH THREADS

• Analytics: Queuing Detection

• Analytics: Group Detection

• In-Store Shopper Classification

THE PROBLEM: CLASSIFYING IN-STORE

SHOPPER BEHAVIOR

Using Mobile Sensing to

Infer

Consumer Preferences,

Interests, Behavior

OVERALL CLASSIFICATION APPROACH

THINGS NOT OBVIOUS

What are the MA labels?

(Different for different

stores?)

What are the HA

classifiers?

How many classifiers?

AN INITIAL CONTROLLED STUDY

16 “shoppers” in a shopping mall in Singapore.

Provided with smartphone to collect sensor data.

Shadowed by observer for “ground truth”.

Given specific tasks corresponding to the HA labels.

A mix of male and female shoppers with varying physical

characteristics.

Pre and Post surveys to verify their shopping behavior profile.

2 Types of Stores: Clothing and Shoe/Accessories

Shopping episodes lasting 3 -10 mins.

RESULTS ON HA CLASSIFICATION..INITIAL

Classifying Focused versus Confused customers:

Classification run on (i) Entire data set (ii) gender-specific data

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Accu

racy

Grouping the dataset

Locomotive + Trajectory 71.43% 50% 100%

Only Locomotive 50% 66.67% 75%

M+F M F

Gender matters: It seems

that “focused” female

shoppers have a very

different characteristic

than “confused” female

shoppers.

Need a way to discover

and account for such

“attributes that matter”

OPEN CHALLENGES

21

Energy Overheads of

Continuous Sensing

•

Power Consumption

Observed on a Test

Samsung Galaxy S3

Improve Classification

Accuracy

•

Boxplot of Service

Time Estimates

(Queuing at F&B)

Privacy for Data

Need Anonymization and

Provenance Solutions!

ACKNOWLEDGMENTS

22

• LiveLabs Research Center: performed jointly with Assoc. Professor Rajesh BALAN (SMU)

• Queuing Research: Tadashi OKOSHI, Rahul MAJETHIA and Rajesh BALAN

• Group Detection Research: Siyuan Liu (CMU), Kasthuri Jeyarajah and Ramayya KRISHNAN (CMU),

• In-Store Shopping Research (jointly with IBM Research): Sougata SEN, Vigneshwaran SUBBARAJU, Dipanjan CHAKRABORY (IBM Research), Nilanjan Banerjee (IBM Research)