mixx canada 2015: towards revolutionizing new frontiers in mobile marketing using randomized field...
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Towards Revolutionizing New Frontiers in Mobile: Trajectory-Based Mobile Advertising !
"Anindya Ghose"
Professor of IT and Professor of Marketing"Director, Center for Business Analytics"
NYU Stern School of Business"""""
Twi$er @ aghose
Huge Potential in Mobile Advertising
@MaryMeeker 2014
$4bn by 2018 in Canada"
Mobile ad is 24% of Internet ad in
Canada"
My Work: Measuring Impact of Mobile"
§ Granular user-level data on mobile ads and mobile coupons"
§ Text/SMS, mobile video, mobile web, mobile app"
§ Location-based, context-based and trajectory-based mobile marketing"
§ Diverse (US, Europe and Asia) settings "
§ Data Science: Analytics using tools from statistical modeling, predictive analytics, randomized field experiments, and machine learning techniques"
Outside Stores (Geo-fencing)
Inside Stores
5
Ghose et al. 2013; Ghose et al. 2014; Andrews et al. 2015; Ghose and Han 2011
Shelf
10 m*
Mobile Ad Effectiveness: Hyper-Contextual Targeting with Crowdedness
Can out-of-home (OOH) advertising benefit from the digital revolution?"
5
Background
• Geographical and temporal information
• Consumers’ current context (i.e., crowdedness)
Static Location Snapshot vs. Shopping Trajectory
Goals Ø Design a new mobile advertising strategy that leverages not only static location/context information, but also consumer’s shopping trajectory.
Ø Measure the impact of the trajectory-‐‑based mobile advertising on shopping behavior and revenues.
Field Experimental Se$ing: A Large Shopping Mall
Experimental Se$ing § A major large shopping mall:
• 1.3 million square feet • 300+ stores • 100,000 visitors per day; 200,000 visitors per day during holidays
• WiFi localization system
Modeling Consumer Similarity: “Great Minds Move alike.”
Ø Define a “community” as a set of similar customers with similar pa$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?
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
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;
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
§ 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).
Trajectory-‐‑based Advertising :
• Higher redemption rate and faster redemption action
• Especially effective in a$racting high income consumers.
• Positive effect on focal advertising store revenue and mall revenue.
• Less effective for weekend and first-‐‑time consumers (may reduce exploration and impulse buy).
Summary of Main Findings
Group of adults
Individual
Smaller group of adult/children
Other Field Experiments
Emerging Data Science Trends in Mobile
Ø Extract consumer preferences from large-‐‑scale, fine-‐‑grained mobile trajectory data using statistical and machine learning methods.
Ø Examine causal impact of new trajectory-‐‑based mobile advertising strategies.
Ø Establish link between user offline behavioral trace and preference, and how it will benefit digital marketing.
Other examples of location-‐‑based tracking involving human activity
• Combination of wearable and mobile health technologies for clinical and patient analytics.
• Improve efficiency in hospital workflow by mining movement pa$erns of doctors, nurses and patients.
