This document discusses how location data can provide insights for enhancing customer relationships and retail experiences. It notes that mobile shopping is increasing and location analytics can power just-in-time messaging. Statistics show rising app usage, social sharing of purchases, and willingness to share locations. Combining location data with other sources strengthens understanding of customer intent. Specific use cases are proposed around digital promotions, merchandising, and marketing tailored to detected patterns and locations. Case studies of mall networks demonstrate opportunities to leverage real-time insights about customer movements and hangout spots.

1. Location infused insights
for profitable relationships
Norbert Herman – Retail Industry Solutions Group

2. 2
You continue to see so many stats about what
consumers say; so what now?
53%
30%
48%
19%
36%
12%
0% 10% 20% 30% 40% 50% 60%
Visit social site
multiple times a
day
Post about items
purchased
2014 - Global
2014 - AUS
2013 - AUS

3. 3
Mobile influenced shopping is enhanced with timely
delivery of messages through the shopping journey
Make data analytics
empowered
decisions with real-
time updates
Amplify your
message with metrics
driven customer
engagement
Increase your reach
with just-in-time
mobile influenced
commerce

4. 4
A couple of 2014 stats that are highly relevant…
Source: http://techcrunch.com/2015/01/06/app-usage-grew-76-in-2014-with-shopping-apps-leading-the-way/

5. 5
Customers location sharing is on the rise
One Example: Simon Group Malls
• 200MM Mall network monthly shopper
visits with $20B+/month in
• 3,000+ unique retailers with over 25,000
storefronts
Retail Malls
Airport Malls
“The Sixth Continent”
• In 2013 travel retailers sold around $60
billion of goods, according to Generation
Research, a Swedish firm
• Sales at airports alone will grow by 73%
from 2013 to 2019
19%
36%
2011
2013
2013 — Willingness to share
Social handle = 32%
Mobile # = 38%
Willing to share current location (GPS)
IBV Study 2014

6. 6
The fundamental challenge “what customers really want”
is simple to state, but difficult to prove

7. 7
Location insights can bring you one step closer to
finding not just the perfect customer, but customer(S)

8. 8
People cannot always explain what they want deep
down, but their location actions provide a strong signal
LifestyleDemographicPersonality
• 32 year old
• Single female with kids
• Lives in Irving, CA
• Long NYC stays (3 months/year)
Lilly
• Global Traveler ~200K / year
• Travels business class 75%
• Most global trips are to London
• Prefers Delta
• Uses car service in NYC not Taxi
• Prefers Hilton in Time Square
• Uses Amex for all purchases
• Needs
• Values
• Personality
• Social Behavior

9. 9
Location data combined with other sources increases
the customer intent signal through the shopping journey
• Location Movement Detection
•In the mall
•At different malls across US
•In the store
•In specific zones in the mall
Enters the Denver
Mall (2:32 pm Sat)
• Analytics Driven Offer
Selection
•Next Best Action and /
or Offer analytics based
on insights inferred from
location data
combined with other
sources
• Location Prediction
•Predictive analytics for most
profitable location patterns
•Location pattern detection for
optimal timing of engagement
Enters the Mall of GA (12:32
pm 1 week later)
Dwells at
Starbucks
Enters the Denver Mall
(5:12 pm 2 weeks later)
Message
based on
presence
event
Recognized
nationwide
pattern
Analytics
based
hangout
pattern
Predictive
traffic pattern
for optimal
engagement

10. 10
Where does one get the data for mall customers?
Current Network
Coming Soon
Current Network
Coming Soon
Provided by an IBM
Partnership with

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The location data driven use cases can make an impact
to all aspects of the retail store
In-Store path
enhancements
Merchandizing (shelf-
fit) enhancements
Local Marketing
Campaign
Enhancements
Email remarketing with
BOPIS
Online (.Com)
promotion
Point of Sale Based
promotions
Opt-In Onboarding
Point of Sale Based
Promotions
Beauty Spot
promotions
Customer Input and
Participation
Associate Guidance in-
store
Opt-In
Anonymous
White Glove Opt-In
Onboarding
Personalized events
and promotions
Mobile based price
checker promotions
Word of Mouth
Marketing Rewards
In-Zone promotions
Store Focused Associate Driven Digital Promotions
Associate Guidance in-
store
Location Insights Use Case Catalog – Traffic to store, Demand Signaling, Marketing Spend,
Competition (share of customer attention)
Profitable Customer
driven shelf fit

12. 12
Hangout detection allows for optimal engagement timing
 What is a hangout
– Given uncertain location samples from a moving object, did the
object dwell at a space-time box for at least t time units?
– Useful for classify moving objects (e.g., daily grinder, couch
potato, globe trotter), detecting anomalies (e.g., shifts in
hangout patterns) co-location analysis (e.g., where does entity
e hangout? who did entity e hangout with?) for surveillance
applications
 Solution
– Using geo-spatial toolkit in Streams / SPSS.
– Handles uncertainty in location data
– Fexible to work across multiple domains (e.g., ships, cars,
asteroids, person on foot)
 Customers
– Telco Malaysia, TelcoPhilippines, Telco Thailand, TelcoTurkey, Telco
USA
Spent at least 15 mins in 76m box
in some one hour time window

13. 13
Pattern detection and anomaly insights based on
location data reveals key omni-channel opportunities
Leverage Point 5:
Advocate Compensation Use platforms like Pinterest to
complement marketing efforts
through the help of the extreme
loyalist
Leverage Point 6:
Application To Person Use of the mobile platform to
send intelligent, timely & location
focused messages to mobile
users

14. 14
Location insights will identify new profitable clusters
Rank Action Cluster % of Customers % of Spend
1 Extreme Loyalists—Profitable 9% 30%
2 Extreme Loyalists—Unprofitable 8% 18%
3 Family Shopper—Profitable 6% 14%
4 Family Shopper—Unprofitable 8% 8%
5 New Customers 7% 7%
6 Credit Inclined 6% 6%
7 Home Multi-channel 10% 3%
8 Infrequent Modern Seeker 7% 2%
9 Lapsing Occasional Shopper 17% 7%
10 Lapsed Big Basket 4% 2%
11 Lapsed One-and-done 13% 2%
12 Clearance Shopper 5% 1%

15. 15
Lily’s language (social) and interests (location data) with
System-U analytics can enhance the message accuracy

16. 16
A use case driven approach is needed for any data use,
omni-channel options and/or creative store experiences
Business Metrics
Determine the set of measurable
benefits that presence zones can
impact
Customer Target
Determine the characteristics of the
prototypical customer targeted by
the use
Business Objective
What opportunity or problem is
going to be addressed by this use
case
Campaigns
Identify existing or propose
promotions needed to expose and
engage customers in the journey
System Capabilities
Determine the range of functions,
and analytics (current or
proposed) to support the journey
Available Data & Capability
Identify what data and capability is
available / being considered for this
use case
Customer Journey
Draw the customer journey that will
be addressed by this use case with
timing details
Business Case Correlation
Map out the business case logic
that will be proven by the use case
using the defined metrics
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4
1
2
5
6
7
8
Use Cases with ROI in
a business objective
context