The explosive growth of smartphones and location-based services has contributed to the rise of mobile advertising. Anindya Ghose will present results from studies that are designed to measure the effectiveness of mobile marketing promotions. The overall mobile trajectory of each consumer can provide even richer information about consumer preferences. A new mobile advertising technique will be presented that leverages full information on consumers’ offline moving trajectories. This session will help marketers better understand the question of which kinds of mobile advertising are most effective and how machine learning techniques combined with statistical models and field experiments offer the right product to the right audience at the right time.

1. Towards Revolutionizing New Frontiers in
Mobile: Trajectory-Based Mobile Advertising!
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Anindya Ghose"
Professor of IT and Professor of Marketing"
Director, Center for Business Analytics"
NYU Stern School of Business"
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Twi$er @ aghose

2. Huge Potential in Mobile Advertising
@MaryMeeker 2014
$4bn by 2018 in
Canada"
Mobile ad is 24%
of Internet ad in
Canada"

3. My Work: Measuring Impact of Mobile"
§ Granular user-level data on mobile ads and mobile coupons"
§ Text/SMS, mobile video, mobile web, mobile app"
§ Location-based, context-based and trajectory-based mobile marketing"
§ Diverse (US, Europe and Asia) settings "
§ Data Science: Analytics using tools from statistical modeling, predictive
analytics, randomized field experiments, and machine learning techniques"

4. Outside Stores (Geo-fencing)
Inside Stores
5Ghose et al. 2013; Ghose et al. 2014; Andrews et al. 2015; Ghose and Han 2011
Shelf
10
m*

5. Mobile Ad Effectiveness: Hyper-Contextual
Targeting with Crowdedness

6. Can out-of-home (OOH) advertising benefit from
the digital revolution?"
5

7. Background
• Geographical and temporal information
• Consumers’ current context (i.e., crowdedness)
Static Location Snapshot vs. Shopping Trajectory

8. Goals
Ø Design a new mobile advertising strategy that leverages
not only static location/context information, but also
consumer’s shopping trajectory.
Ø Measure the impact of the trajectory-­‐‑based mobile
advertising on shopping behavior and revenues.

9. Field Experimental Se$ing: A Large Shopping Mall

10. Experimental Se$ing
§ A major large shopping mall:
• 1.3 million square feet
• 300+ stores
• 100,000 visitors per day; 200,000 visitors per day during
holidays
• WiFi localization system

11. Modeling Consumer Similarity: “Great Minds Move alike.”
Ø Define a “community” as a set of similar customers with
similar pa$erns of mobile trajectories.
Ø Define pairwise “similarity” as a function of different aspects
of individual mobile trajectory.
e.g., visit similar stores, visit at similar time (weekends vs. weekdays,
morning vs. afternoon), similar shopping speed (explorers, raiders), etc.
Ø Mine communities using graph-­‐‑based clustering (e.g., dense
sub-­‐‑graph detection).
Key: Measure similarity?

12. Consumer Similarity
Assume two customers i, i’.
1. Temporal: Start/End time stamps, Time and day indicators.
2. Spatial: Spatial alignments.
3. Semantic: Visit probability of each store; Time spent at store;
Transition probability from store A to store B; Time spent to transit
from A to B.
4. Velocity: Speed (normalized by travel length)
• The similarity S(i, i’) is a weighted combination of a set of similarities
calculated from the above four sources:
K-­‐‑ similarity score by using
various similarity functions
(cosine distance, kernels).
S(i,i’)= 𝑎1Kt+ 𝑎2Kp+ 𝑎3Ks+ 𝑎4Kv
a -­‐‑ Weight associated
with each dimension

13. Experimental Design
§ Group 0: Send nothing
§ Group 1: Send random promotion messages
§ Group 2: Send promotion messages based on static real-­‐‑time locations
§ Group 3: Send promotion messages based on our trajectory-­‐‑based
recommendation
ü On each day, randomly assign ~6000 consumers to one of the 4 groups;
ü 14 consecutive days, 83,370 unique user responses;
ü Promotions involve 252 participating stores;
ü Different types of coupons: e.g., “50% off” and “Buy one get one free”;
ü Coupons sent 15-­‐‑20 mins after walking into the mall;
ü Group 1 uses the exact same set of mobile promotions (format & price discount) as the
ones used in Groups 2 & 3, except randomly sent;

14. Trajectory-­‐‑based Mobile Advertising leads to
• Highest promotion response rate, fastest redemption action.
• Less time spent in the focal store, but more revenue.
• Overall more time spent in the mall.
• Most effective in aNracting high income group.
Key Findings

15. § On average, Trajectory-­‐‑based > Location-­‐‑based > Random.
§ Weekend > Weekday.
§ Trajectory à less effective during weekend.
§ Random à more effective during weekend.
Individual User-­‐‑Level Analyses: Key Results
Impulse buyers and explorers (random ads help exploration and variety-­‐‑seeking).

16. Trajectory-­‐‑based Advertising :
• Higher redemption rate and faster redemption action
• Especially effective in a$racting high income consumers.
• Positive effect on focal advertising store revenue and mall
revenue.
• Less effective for weekend and first-­‐‑time consumers (may
reduce exploration and impulse buy).
Summary of Main Findings

17. Group of adults
Individual
Smaller group of
adult/children
Other Field Experiments

18. Emerging Data Science Trends in Mobile
Ø Extract consumer preferences from large-­‐‑scale, fine-­‐‑grained
mobile trajectory data using statistical and machine
learning methods.
Ø Examine causal impact of new trajectory-­‐‑based mobile
advertising strategies.
Ø Establish link between user offline behavioral trace and
preference, and how it will benefit digital marketing.

19. Other examples of location-­‐‑based tracking involving
human activity
• Combination of wearable and mobile health
technologies for clinical and patient analytics.
• Improve efficiency in hospital workflow by mining
movement pa$erns of doctors, nurses and patients.

20. Thank You!
Email: aghose@stern.nyu.edu"
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Twi$er @ aghose
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