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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
5Ghose 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$erns  of  doctors,  nurses  and  patients.
Thank You!
Email: aghose@stern.nyu.edu"
"
Twi$er  @  aghose
"

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MIXX Canada 2015: Towards Revolutionizing New Frontiers in Mobile Marketing Using Randomized Field Experiments - Anindya Ghose

  • 1. 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
  • 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.