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Omnyscope e245 march 2014 final

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Information about Omnyscope e245 march 2014 final
Education

Published on March 5, 2014

Author: sblank

Source: slideshare.net

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omnyscope Lessons Learned Interviews: 107 / Hypotheses: 66 (Interviews this week: 10) Initial idea: Sentiment Centered News Aggregation We provide an easy interface to consume news articles with diverse opinions Final Video: http://www.youtube.com/watch?v=9srMlWcgzKA

Arijit Banerjee Pararth Shah Shantanu Joshi Sujeet Gholap Background MS CS MS CS MS CS MS CS Systems UI/UX Hustler Hacker, Designer Expertise Algos AI/Machine Learning Role on team Hacker, Designer Hustler, Product Picker -- Four engineers with zero business experience --

Our Journey Ton of learnings! Product/Market Fit! Optimized cooking: Engineer’s fantasy! Got into the class! Resegment! Reco-as-a-service MVP Not a viable business Wireframe Recipe Recommendations First presentation Restart Data access issues Pivot No revenue stream! No market “This is either the dumbest or the smartest idea I have seen lately”

Week 1: Sentiment based news aggregation Key Partners Key Activities Content Generators: Forming new partnerships to obtain data News Sites Editorials Personal blog sites Building web and mobile MVP Key Resources IT infrastructure Intellectual Property Cost Structure Value Propositions 360 degree view on a topic of your choice Bursting the filter bubble Provides an easy medium for collecting a diverse set of opinions MVP: Specialised product for one customer segment Customer Relationships Customer Segments Feedback on content quality Professional users: Recommendation s Investors Business analysts Channels PR firms Individuals: Content aggregator - web and mobile app Browser plugin Revenue Streams Server and bandwidth Advertisements Paid APIs for data access Freemium model Researchers Young adults Travel planners

Here’s what we did • Talked to: o Day traders o Investors o Brokers o Research analysts o Journalists • Built a wireframe:

Here’s what we found • • Summary of key hypothesis: Burst the filter bubble and provide a holistic view of a topic of interest to an investor Customer feedback/insights: o Not useful for professional investors → Bloomberg terminal + direct access to analyst reports o Individual investors lack the time to learn about a stock in detail and either invest in well known stocks or mutual funds o Rely on understanding general trends rather than specifics o Google and Yahoo finance + other crowdsourced websites are more than satisfactory for personal investors o Domain experts refer to research articles o "... too much technical analysis was of little use and I would rather look at the total environment using Google searches"

RESTART: Here’s why • • • • After two and a half weeks, we did not find a definite product-market fit with either investors or journalists All of us were excited about the original idea (bursting the filter bubble) but it had no market None of us were excited about the “investor” market segment We had not anticipated that news content would be so closely guarded by organizations like Bloomberg, Reuters

Week 3: Hassle-free cooking

Week 3: Hassle-free cooking

Results from talking to customers Summary of key hypothesis: Optimize recipes, provide step by step navigation, adapt to multiple cooks What we did: Interviewed students from different backgrounds, majors What we learned: • • • • • Most people who cook frequently consider cooking as a recreational/ therapeutic activity, and were not looking to “optimize” Most recipes already have step by step navigation, however, they are missing visual and aromatic cues Most grad students almost never have all the ingredients for a recipe, so they use recipes as a reference and improvise Recipe is unnecessary to most after cooking it twice or thrice Students only cook the stuff that they know well in order to save time

Week 4: Grocery inventory management

Just one problem:

Results from talking to customers • • Summary of key hypothesis: Solve the “manual entry problem” and provide personalized recommendations Customer feedback/insights: o Hard to keep track of everything, end up throwing stuff bought in bulk o Don’t use inventory apps - too much manual entry • Our solution: o Leverage the fact that people buy the same kind of ingredients o Have them enter the first couple of times and we learn from that • Teaching team: Build an MVP and get tangible feedback

There’s a better way ● ● ● ● ● Provide personalized recommendations from the start Approximate inventory based on behavior on the website Solve cold start problem with a very short survey ( Thanks mor.sl and Netflix) No manual entry The more you use it the better it becomes

Week 5: Personalized recipe recommendations

Results from talking to customers Summary of key hypothesis: Focus more on personalized recommendations rather than inventory management What we did: • • Built an MVP and showed it to customers for feedback Ran Google AdWords campaign to measure signup rate What we learned: • • • Recipe recommendations based on preferences and available inventory was a “must-have” or “nice-to-have” for most customers we interviewed CAC from AdWords is very high - must rely on virality for demand creation MVP Feedback o Some users prefer videos over text (provide both) o Recommendations should not provide a barrage of recipes with same

Let talk about Money

The Search For Revenue

Revenue Flows (1) Advertisers Users Clicks $$ $$ Online food delivery services omnyscope Recommendation engine to improve user engagement Recipe Recommendations $$ Data to improve recommendations

Results from talking to customers Potential Revenue Stream: Targeted ads and premium version. What we did: • • Interviewed grad students to gauge willingness to pay Interviewed recipe website owners to understand their revenue model What we learned: • Large fraction of students not willing to pay, while some others find our solution appealing enough to pay o Range from $1 one time fee to $20 annual subscription o Will pay for content not recommendation. • Grad students is a bad market segmentation for targeted ads o More attractive segmentations: vegetarian, vegan, gluten-free, diabetic

Revenue Flows (2) Users Recipe Recommendations $$ Online food delivery services omnyscope Recommendation engine to improve user engagement $$ Data to improve recommendations

Week 7: Recommendations-as-a-service

Results from talking to customers Potential Revenue Stream: Recommendation as a service What we did: Interviewed recipe websites and online foodstuff delivery oriented startups What we learned: • High traffic recipe websites are not keen on having a third-party recommendation service on their site o Allrecipes.com, Yummly have working recommendation engine developed in house o SimplyRecipes.com does not want to modify a working model • Food-related services which sell directly to consumer have a greater incentive to increase user engagement and retention o But they are not willing to share data with 3rd party services

So here’s where we ended up

omnyscope

omnyscope

Here’s what we did • • Summary of key hypothesis: Resegment the market to target frequent cooks with personalized recommendations and novice cooks with amateur cooking instruction videos We talked to frequent cooks to gauge their willingness to pay for personalized recipe recommendations ● We recorded cooking instruction videos and asked novice cooks to compare them with videos by professional chefs https://www.youtube.com/watch?v=Jgf-a6Tmmb8

Here’s what we found • • • Frequent cooks willing to pay not more than a one-time charge of $3-$5 for the convenience of getting relevant recipe recommendations Novice cooks are interested in tips/hacks related to common cooking methods for amateur cooks, but did not see any huge value addition in the amateur videos as compared to professional videos We posted our cooking video on the Stanford India Association FB group: No. of members on Facebook Group 474 No. of video views 116 CTR No. of subscribers Signup rate CAC 24.5% 4 3.45% N/A

Revenue Flows (3) $3-$5 one time payment Expert Premium Version Curated Training Videos omnyscope Free Version Novice

Customer Relationships Improved recommendations 1. Free advertising on Social media 2. University mailing lists Recipe suggestions Post on FB group: CTR: 24.5% Signup rate: 3.45% Curated Cooking Videos

MVP: http://www.omnyscope.com

Not a viable business! • • • High customer acquisition costs o AdWords are very expensive and signup rate is low o Not sure whether service will be viral enough to drive demand creation Shaky revenue model o Some customers have shown willingness to pay, but majority do not perceive it as a major value addition over the free services available in this space o Advertisements will not generate significant revenue from the target customer segment We are not going to pursue this idea further

Lessons Learned • Customer discovery o Not just “get out of the building”, also necessary to “get out of the mindset” by interviewing people who think differently from you o Keep all boxes of the canvas under consideration during every interview in every week, don’t just focus on the topic of the week • • Competition o In an overcrowded space, if you find an unserved customer need, ask why nobody else has filled the need yet. There might be learnings about impracticality of solution or non-existence of revenue streams Teamwork o Customer discovery is painful, it’s important for everyone to be motivated o Choose a product which excites everyone on your team

Domain Knowledge Acquired In addition to lessons learned, we acquired tangible domain knowledge about many areas, as a by-product of our customer interviews and numerous pivots: • • • • • • • How day traders, personal investors and brokers make their decisions How financial information flows between research analysts and key decision makers, via organizations like Reuters, Bloomberg, etc How journalists manage their research, interviews and writing process How grad students manage their cooking activities How high-traffic recipe websites increase engagement, and what are the current challenges they face (eg. monetizing mobile traffic) How online delivery services manage logistics issues and partnerships How startups decide on building technology in-house or paying a 3rd party service

What we’d have done differently • • • Come prepared o Doing initial customer discovery before the start of the class would have saved us from the painful first two weeks “Blue Ocean” is better than “Red Ocean” o We went from a product with no market to a product with an overcrowded market - we wish we’d have chosen a relatively underserved market Prioritize search for revenue model early on o We spent significant time finding a product/market fit with grad students, only to later realize that they are a difficult customer segment to monetize

Investment Readiness Level: 4 9. Validate metrics that matter 8. Validate left side of canvas 7. High-fidelity MVP 6. Validate right side of canvas 5. Validate product/market fit 4. Low-fidelity MVP 3. Problem/solution validation 2. Mkt size/competitive analysis 1. Complete first pass canvas 4

Business model canvas timeline

Week 1: Sentiment based news aggregation Key Partners Key Activities Content Generators: Forming new partnerships to obtain data News Sites Editorials Personal blog sites Building web and mobile MVP Key Resources IT infrastructure Intellectual Property Cost Structure Value Propositions 360 degree view on a topic of your choice Bursting the filter bubble Provides an easy medium for collecting a diverse set of opinions MVP: Specialised product for one customer segment Customer Relationships Customer Segments Feedback on content quality Professional users: Recommendation s Investors Business analysts Channels PR firms Individuals: Content aggregator - web and mobile app Browser plugin Revenue Streams Server and bandwidth Advertisements Paid APIs for data access Freemium model Researchers Young adults Travel planners

Week 2: Flexible summaries for investment research

Week 3: Hassle-free cooking

Week 4: Grocery inventory management

Week 5: Personalized recipe recommendations

Week 6: Personalized recipe recommendations

Week 7: Recommendations-as-a-service

Week 8: Personalized recipe recommendations

Week 9: Personalized recipe recommendations

Thank You

Channel Economics Recipe Licensing Development cost Profit App store share (30%) $5000 $15,000 $85,000 $45,000 COGS R&D, SG&A Revenue: $105,000 Cost to consumer: $3 per download Assuming 50K users Distributor

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