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Exodus Lessons Learned H4Dip Stanford 2016

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Stanford University
Stanford University

agile, mission model, corporate innovation, customer development, h4d, h4dip, hacking for diplomacy, lean, lean launchpad, lean startup, stanford, steve blank, state department

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Kian Katanforoosh MS MS&E + CS Jenna Nicholas Duncan Turnbull Berk Çoker Katie Joseff MBA2 MS Computer Science MBA2 BS Human Biology
11 7INTERVIEW S COMPLETE TEAM: Katie Joseff Berk Coker Duncan Turnbull Jenna Nicholas Kian Katanforoosh SPONSOR: Allison Listerman Bureau of Population, Refugees, Migration Original Problem: The current information sharing mechanisms fail to encompass the full breath of the humanitarian assistance on the ground and facilitate the entry of new individual and organizational actors. Current Problem: Refugees are not getting the information they need and they do not have a trusted source of information that is easily accessible.
Our journey The Contact Inventory Node Map One-sided platform Two-sided platform Smart Matching with decision tree Chat-based intelligent virtual assistant How did it started ?
What is coordination?
MVP 1 : List of contacts The Contact Inventory General feedback: “Not user friendly” First Hypothesis: To coordinate, people need to be aware of other stakeholders on the Syrian Refugee Crisis Foreign Service Officers: “no link showing relations”
Observation and Feedback from Private Sector: “Some isolated clusters!”
MVP Story listings two-sided connecting people VP Charity : “Can you match us automatically ?” Second Hypothesis: Companies want to help but don’t know how to.
Companies are lazy
MVP 3: the decision tree Record Data Smart MatchingFilter and Search “Have you heard of TechSoup ?” How it works “Have you heard of Tent ?”
Key Pivot Private sector Refugees The needs based on feedbacks Trusted Information Publicly Easily Accessible Mobile OrientedInformation SharingCrowd-Sourced Knowledge Database Chat-based Assistant Final Hypothesis: There is no centralized trusted information database accessible for refugees Organisations are failing at reaching massive numbers of refugees
Mission Model Canvas
Final MVP: chat-based intelligent virtual assistant Facebook Messenger Telegram “We want to work on that with you” (UHNCR) “That’s exactly what I need” (Refugee)
MVP « I need clothes » « You can find donated clothes it here » « I don’t know… but I know who to ask! I’ll be right back » How the MVP works « Sure, here is a map of where refugee can find donated clothes » Chatbot gets smarter with each user Refugee Knowledge Base « You can find donated clothes it here »
Buy-in and Support Syrian refugees arrive in Istanbul, Turkey Partner with an established organization Where do we go? How do we get food? Localized Facebook advertisement
Key Partners UNHCR: “we would like to work with you on a chatbot”
Next steps hand in hand with UNHCR Continue building the technology Give it to more refugees Stay LEAN
Team Exodus “Thank you for your attention”
Exodus Lessons Learned H4Dip Stanford 2016

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Exodus Lessons Learned H4Dip Stanford 2016

  • 1.
  • 2. Kian Katanforoosh MS MS&E + CS Jenna Nicholas Duncan Turnbull Berk Çoker Katie Joseff MBA2 MS Computer Science MBA2 BS Human Biology
  • 3. 11 7INTERVIEW S COMPLETE TEAM: Katie Joseff Berk Coker Duncan Turnbull Jenna Nicholas Kian Katanforoosh SPONSOR: Allison Listerman Bureau of Population, Refugees, Migration Original Problem: The current information sharing mechanisms fail to encompass the full breath of the humanitarian assistance on the ground and facilitate the entry of new individual and organizational actors. Current Problem: Refugees are not getting the information they need and they do not have a trusted source of information that is easily accessible.
  • 4. Our journey The Contact Inventory Node Map One-sided platform Two-sided platform Smart Matching with decision tree Chat-based intelligent virtual assistant How did it started ?
  • 6. MVP 1 : List of contacts The Contact Inventory General feedback: “Not user friendly” First Hypothesis: To coordinate, people need to be aware of other stakeholders on the Syrian Refugee Crisis Foreign Service Officers: “no link showing relations”
  • 7. Observation and Feedback from Private Sector: “Some isolated clusters!”
  • 8. MVP Story listings two-sided connecting people VP Charity : “Can you match us automatically ?” Second Hypothesis: Companies want to help but don’t know how to.
  • 10. MVP 3: the decision tree Record Data Smart MatchingFilter and Search “Have you heard of TechSoup ?” How it works “Have you heard of Tent ?”
  • 11. Key Pivot Private sector Refugees The needs based on feedbacks Trusted Information Publicly Easily Accessible Mobile OrientedInformation SharingCrowd-Sourced Knowledge Database Chat-based Assistant Final Hypothesis: There is no centralized trusted information database accessible for refugees Organisations are failing at reaching massive numbers of refugees
  • 13. Final MVP: chat-based intelligent virtual assistant Facebook Messenger Telegram “We want to work on that with you” (UHNCR) “That’s exactly what I need” (Refugee)
  • 14. MVP « I need clothes » « You can find donated clothes it here » « I don’t know… but I know who to ask! I’ll be right back » How the MVP works « Sure, here is a map of where refugee can find donated clothes » Chatbot gets smarter with each user Refugee Knowledge Base « You can find donated clothes it here »
  • 15. Buy-in and Support Syrian refugees arrive in Istanbul, Turkey Partner with an established organization Where do we go? How do we get food? Localized Facebook advertisement
  • 16. Key Partners UNHCR: “we would like to work with you on a chatbot”
  • 17.
  • 18. Next steps hand in hand with UNHCR Continue building the technology Give it to more refugees Stay LEAN
  • 19. Team Exodus “Thank you for your attention”

Hinweis der Redaktion

  1. Coordination = Cooperative effort resulting in an effective relationship
  2. This was an MVP but it made us understand that there is not coordination between some clusters