Elbow Room Presentation


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Elbow Room Presentation

  1. 1. Elbow Room: The App u u u By Andrew Cohen For General Assembly UX11 Presented Dec. 17, 2013
  2. 2. RESEARCH NOTES Utilizing open-ended “Ohno Circle” methodology, I sat in a restaurant near my home and just observed the customers while taking notes.
  3. 3. FINDINGS Many of the patrons who came in after 2pm came not for the food (which is excellent) but in search of a quiet place to • Think • Talk • Listen • Read • Work • Observe
  4. 4. PERSONAS My research identified several user groups likely to seek out such mid-afternoon “third place” establishments. Among them: • High School and College Students • Stay-at-Home Mothers of Young Children • Freelancers • Professionals Seeking a Quiet Getaway
  5. 5. CHALLENGE Find a way for people who like to hang out in quiet, uncrowded restaurants or cafes to immediately locate estblishments where they can sit with friends or a book for a long time without being rushed.
  6. 6. SOLUTION 1 An app/website that uses data analysis to determine at which times a given establishment is likely to be uncrowded based on of sales at different times of day. Example: It could tell you that the nearest Starbucks is a mob scene on Mondays at 8am but a hermit’s dream at 11am.
  7. 7.   2 SO LUTIO N 2 An app service that encourages restaurants and cafes to install webcams that allow prospective customers to see how crowded it is in real time before making the trip. This could be an add-on to services (Yelp, Zagat, Foursquare) that already help customers find venues. 1
  8. 8. d   THE IMPLEMEN TATIO N : Elbow Room An app that uses predictive technology to help you find a quiet place to go at any time of the day.  
  9. 9.         TASK FLOW • Choose type of venue • Choose time and location • Choose from list of results Algorithms predict present and future user density by factoring in such factors as: • C redit-card data • Fire code requirements • W eather reports • Real-time transactions • Doorway sensors • C ell phone density • W ebcam analysis
  10. 10.   User criteria creates a ranked list of nearest venues matching desired attributes, giving each a “crowd index” number and a corresponding color. • G reen = Under 50% full • Yellow = 50% to 100% full • Red = More than 100% full (expect a wait)
  11. 11.   Venue-level page gives detailed information, including a graph showing that day’s crowd estimates, along with other data one would typically find on Yelp, Foursquare, or Zagat: • Address, phone number • Hours, attributes, price • Travel information • Live webcam (if available)