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Content Cube ENGR 245 Lean LaunchPad Stanford 2018

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business model, customer development, e245, engr245, lean launchpad, lean startup, stanford, startup, steve blank

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Content Cube ENGR 245 Lean LaunchPad Stanford 2018

  1. CONTENTCUBE 143 interviews Niche Entrant A smart way to personalize news texts Helping journalists find product- market fit
  2. Kaveh PhD in Econ Janel B.S. Computer Science Michael Creative Dir. at SF Business Times Titus Investigative journalist Michelle B.S. Computer Science The Team
  3. News organizations haven’t been doing so well. Advertising & subscription revenue for U.S. newspapers (in billions) 60 50 40 30 20 10
  4. Advertising & subscription revenue for U.S. newspapers (in billions) We wanted to do something about this. CS 206: Computational Journalism 60 50 40 30 20 10
  5. Original MVP screenshots/gif here
  6. News organizations (e.g., NYT, WaPo) Software development Reader-side platform User data Writer-side platform Help news organizations attract new readers and better engage existing ones Make the news more informative and enjoyable for readers KEEP: Customer support GET: Free testing Direct / Sales Team Software development Customer acquisition Customer support Annual licensing fee Tech companies with large amounts of demographic data (e.g., facebook) Customer support Help readers understand different perspectives Direct / Internet GROW: Upsell to higher-end license Original Business Model Canvas News organizations Readers
  7. News organizations (e.g., McClatchy, NYT, WaPo) UX design, learning archetype engagement User data collection Writer-side platform KEEP: Customer support Direct / Sales Team News organizations Annual licensing fee structure News agencies Integrating with existing news CMS Direct / Internet GROW: Upsell to higher-end license Predict archetypes (branches) who should get a different version of the story Provide framework and structure to make archetyping easy for every story Identify archetypes by leveraging/clustering user data already collected by news orgs Advertising, PR, and marketing agencies Parsing data, collecting engagement stats Analytics platforms Ad server platforms Publisher onboarding Suite: Premium features Versioning for other industries Advertisers, PR, and Marketing agencies 143 Interviews Later... Current Business Model Canvas Software development Customer acquisition Customer support
  8. A Hero’s Journey. Journalist’s
  9. A Hero’s Journey. Journalist’s 0 interviews
  10. Experiment: Do readers enjoy personalized news texts?
  11. Experiment: Do readers enjoy personalized news texts?
  12. Experiment: Do readers enjoy personalized news texts?
  13. Experiment: Do readers enjoy personalized news texts?
  14. Experiment: Do readers enjoy personalized news texts? Nope. “That detail doesn’t belong in this article.” “All this one has is more numbers.” “Meh. This one doesn’t flow well.” “That statistic doesn’t fit in.”
  15. The First Pivot. 36 interviews
  16. Did we personalize the wrong way? Is personalized text the wrong solution?
  17. Did we personalize the wrong way? Is personalized text the wrong solution?
  18. Did we personalize the wrong way? Is personalized text the wrong solution? Let’s understand one customer segment really well and personalize the right way this time.
  19. Who would be our early evangelists? People who are... 1. Hungry for news 1. Dissatisfied with mainstream news 1. Making the extra effort to fill appetite
  20. Who would be our early evangelists? People who are... 1. Hungry for news 1. Dissatisfied with mainstream news 1. Making the extra effort to fill appetite
  21. Piecing together a picture of our customers
  22. The Innovators: Coin Founders, Early Investors “I’ve figured out my news sources already.”
  23. The Early Adopters: Experienced Investors “I get my crypto news from word of mouth. I have people I trust.”
  24. Early Majority: The Finance & Crypto Novice “It’s so hard to find credible sources.”
  25. We listened to their pain points. (41 interviews)
  26. But something felt wrong.
  27. DO YOU BELIEVE IN YOUR PRODUCT ? Do any of you even own a coin?! Do you believe in your product?
  28. Lesson Learned: It’s cheesy, but passion really does matter.
  29. A Passionate Pivot. 94 interviews
  30. We went back to our vision, but with a twist. This was from our Week 1 Presentation.
  31. Then we took our MVP to news agencies, who said...
  32. Irene Chang Product Manager, Text & Multimedia It’s not a super huge need. Maybe it’ll be more useful to publishers? Then we took our MVP to news agencies, who said... “Meh.”
  33. A solution for news agencies is a small market. $600 millionSubscriptions cost $10k to $1-2 million We estimated our annual revenue to be $15 million. Revenue of Top 3 in U.S.
  34. With nothing to lose, we asked news executives for thoughts on all our previous MVP iterations. VS MVP 1: Personalize news texts MVP 3: Augment wire text VS MVP 2: Automate style & standards
  35. MVP 1: Personalize news texts …and they jumped out of their seats for our very first iteration.
  36. …and they jumped out of their seats for our very first iteration. Wait, what? MVP 1: Personalize news texts
  37. Localization is really valuable, but at the community level. Daniel Schaub Corporate Director of Audience Development We want our journalists to write with a specific person in mind. But there’s no structure in place to suggest that right now. Tim Grieve VP of News What got them so excited?
  38. Journalists & editors felt the same way. Andre Taylor Desk Editor “If a story gets clicks or blows up, we want to know how to do that again. When something spikes, it’s like Christmas.” “People care about things you may not suspect they’d click on. Most of the time, the editor or reporter thinks they know what people like.” Arlene Washington Digital Editor
  39. Key Insight: There’s a gap between the way journalists write and the way publishers wish they would write. To journalists and publishers, our original MVP looked like a bridge between this gap.
  40. Back to our Origins, But Wiser this time 116 interviews
  41. A Hunch The original MVP tweaked sentences. What really moves people is stories. We need to inform writers about readers earlier in the story writing process.
  42. We changed how we pictured our readers Demographic data → Archetypes, Occupations, Interests (Consider the reader as a whole) We changed how we personalized to our readers Statistics & Detail → Tone, Structure, Anecdotes (Consider the narrative as a whole)
  43. Experiment: Do readers segmented by archetype enjoy personalized news narratives?
  44. Experiment: Do readers segmented by archetype enjoy personalized news narratives?
  45. Experiment: Do readers segmented by archetype enjoy personalized news narratives?
  46. Experiment: Do readers segmented by archetype enjoy personalized news narratives? YES! “It helped me imagine the actual product and made it sound cool.” (Tech) “I like that it sounds like it is written in a viewpoint of not needing to advertise but to just write the facts about the researchers' work.” (Biology) “I liked that it gave some background on the condition and some facts/figures...this could very well be an issue of interest though.” (Biology) “It provided me the opportunity to learn more.” (Tech) “I liked it more because it had a quote from Apple.” (Tech)
  47. Lesson Learned: Be suspicious of your assumptions, but don’t abandon your vision too quickly either.
  48. 143 interviews The Grand Finale.
  49. Our idea: We help journalists find product-market fit for their stories.
  50. Our idea: We help journalists find product-market fit for their stories. How? A writing tool that informs the writer about their target audience during the writing process.
  51. Advertising & subscription revenue for U.S. newspapers (in billions) We wanted to do something about this. 60 50 40 30 20 10
  52. Advertising & subscription revenue for U.S. newspapers (in billions) We will do something about this. 60 50 40 30 20 10
  53. Three of us will be continuing! Janel B.S. Computer Science Michael Creative Dir. at SF Business Times Michelle B.S. Computer Science
  54. The End.
  55. CONTENTCUBE 143 interviews Niche Helping journalists find product-market fit Q & A
  56. Appendix
  57. Now our customers are news organizations. $28 billionannual revenue (ads + subscription) Pains: Subscriber model in a digital age. Gains: Segment readers to drive subs and better target ads. If we can address even 3% of this pool, that’s a $1B market.
  58. Lesson 1: Good startups run like efficient code while (product_market_fit==0) { # define experiment; # define threshold for success; if (result < threshold) { next_step = next_step_fail; } else { next_step = next_step_pass; } } Week Experiment Result 1 The Value of Personalized News Fail (18%) 3 Is There Unmet Need for Crypto News? Fail (67%) 4 The Value of Personalized Crypto News Fail (44%) ... ... … 8 The Value of Personalized News, Part II PASS! (80%) KD
  59. Lesson 2: Like journalists, startups need key sources Michelle Park Interview Request from Stanford University Team To: Schaub, Daniel (McClatchy) ----------------------------------------------------------------- Hi Mr. Schaub, I’m an engineering student at Stanford working on a class project. I was hoping to talk with you about how news agencies could better serve readers. ... Titus Plattner Interview Request from Tamedia News To: Johansen, Ed (MU) ----------------------------------------------------------------- Hi Mr. Johansen, I’m a journalist at Tamedia writing a story on offshore tax havens. I would like to talk with you about your company based in the Cayman Islands. ... KD
  60. We pieced together every step of the wire editing workflow.
  61. Tone: Excitement about new product Structure: Surface tech-related information to the top Anecdote: Incorporate quotes from leading technologists Experiment: Do readers segmented by archetype enjoy personalized news narratives?
  62. Lesson 3: Passion is necessary (but not sufficient) Crypto News (MVP 2) Social News Personalized News Texts (MVP 1) KD
  63. Content Cube Vox NYT ProPublica RobbReader NYT WP nevalabshq Pyze.com SalesForce vhcl.co Facebook Twitter Google News Alexa Cortana Siri Competitor Leaf Diagram
  64. Kaveh Danesh Jihyeon Janel Lee Michael Grant Titus Plattner Michelle Park MG Econ PhD, former Duke Trustee, writer for Obama, RA for Raj Chetty, NCAA Division I soccer coach CS Major, Section Leader, KPCB Engineering Fellow, RA at Stanford AI Lab, Theater gal who likes corgis Knight Fellow, Creative director at the SF Business Times, Lead SF Chronicle, digital incubator training ground Knight Fellow, Investigative reporter, created a tool to share terabytes of data in newsrooms, Swiss CS Major, CS Course Asst, created platform to crowdsource education content, intern at Apple, Google, and NASA
  65. Small market: The three big wire agencies: $600M per year for text in the US (Subscriptions based on feeds + total readers, news orgs pay btw. a few 10k to a 1-2 M. per year) Content Cube max. revenue: 15M per year => “This is a hobby” said Steve W.
  66. Interesting market: Revenue of US newspapers ($28B in 2017) If we even can address 3% of this market by driving more subscriptions or better targeting ads, we would have a $1B market. 1. For news orgs, getting more online subscriptions is key. 2. Better targeting online ads would allow news orgs to compete again with the internet giants.
  67. TAM: Revenue of US newspapers in 2017: Ads: $18B Subscriptions: $10B Total: $28B SAM: Bigger US newspapers revenue in 2017: $14B Target: Address about one fifth of this market: >$2B Content Cube helps on both markets }

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