Rel events final


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  • Beta/final appendix
  • Rel events final

    1. + + Social planning tool, with a recommendation engine to personalize the experience, providing discounts and targeted group deals as the primary revenue model Simple way to find things to do, that cuts through the clutter of existing event websites/competitors Opportunity: $800 million target market, based on a Groupon-like group-deal model for the events space
    2. Nathan BlumbergPrincipal Internal Auditor, LSI Corporation MentorsRole: Finance and Auditing Sumeet Jain Partner, CMEA CapitalRanjit JoseDirector, Global Product & Solutions Marketing, Model NRole: Sales and MarketingSpencer LooneyPresident, Grove Land Pete Vlastelica Founder, YardbarkerRole: Event Production & StrategyPraveen RutnamGroup Product Planner, Microsoft CorporationRole: Product Management - TV and Gaming Industry
    3. Key Partners Key Activities Value Customer Customer Proposition Relationships Segments - Data Acquisition • Social plan options Automated services - Active social- Facebook, Google+, (info on events) tailored to your and communities networkers Linkedin - Software preferences and past - Advertisers engineering behavior - Businesses • Leverage social providing network preferences experiences • Targeted advertising (promoters, to organically create venue owners, Key Resources groups Channels community orgs - Premium - Data on events conferences, - Facebook, corporate Google+, events, small Linkedin businesses, universities - Mass market and Internet Users Cost Structure Revenue Streams From businesses providing experiences: targeted groupSoftware development and maintenance advertisingAdvertising Support From Internet Users; Free/Freemium
    4.  What we learned from end What we learned from event users: organizers:  Validated consumer value prop  Local event organizers need of initial idea w/ 24 of 25 efficient ways to raise awareness consumers interviewed saying & fill excess capacity they were interested in our  It difficult to know how much proposed offering they spend on user acquisition,  Advanced discovery of events they don’t track it well and are was the greatest pain point not willing to share  Interest was split among large  Daily group deals not ticketed events (i.e. concerts) appropriate for event market and smaller local events because of limited frequency  Users preferred to get  Group deals are geared personalized event towards customer recommendations via email (vs. acquisition for lifetime going to a website) value
    5.  We received 139 responses 1 What we learned: 1  Print media plays a more significant role in local event discovery (vs. large ticketed events and business events)  Greatest interest in our service was 2 around local events  Parents emerged as a potential archetype“; 69% of people who answered they’d “very likely” be interested in our service were married 2 with kids, (vs. 40% overall) We tried to use Facebook ads and a $25 gift card to generate more responses  While we received 20K impressions this translated into only 6 clicks and ZERO completed surveys (over 1 week)
    6.  What we learned from some of them:  Sonic Living  Referral revenues from concert ticket sales do not provide sufficient revenue to be a scalable startup even at scale  Lucky Cal  Similar to sonic living focused on larger ticketed events (i.e. concerts) and concluded there is not sufficient revenues from an affiliate model  Triporati  Importance of defining an event taxonomy for use in the personalized recommendations.
    7. Key Partners Key Activities Value Customer Customer Proposition Relationships Segments - Data Acquisition Users: Users: - Businesses-Ticketmaster and (info on events • Most comprehensive • Automated services providingother ticket sellers from list of events tailored and communities experiences- Spotify,, producers/users) Businesses: to you (promoters, Pandora, iTunes - Software • Don’t miss anything • Advertising and venue owners,- Yelp engineering promotion support you’re interested in community orgs • Simple (cut through - Marketing clutter) Firms/Data Key Resources • Status (super users) Channels Users Businesses: - 18-44 Y/O - Scrapable data on - Provide social graph Internet Users - E-mail driven web events (only for intelligence to - Parents looking interface kick-off) advertisers/Targeted for family - Web - Existing user base Ads activities - Mobile Apps (low of other services - Lead Generation priority feature - Excess event capacity only) clearing Cost Structure Revenue Streams Advertising and paid results Lead GenerationSoftware development and maintenance Marketing DataAdvertising Support Selling empty seats or ticket sale revenue share Personalized marketing data
    8.  We started working with the various potential Revenue Models  Targeted and General Advertising  Excess capacity fulfillment  Lead Generation  Selling demographic data Tried to connect with business users who could help validate the millions they were going to pay us But….
    9.  Tough to connect with & extract info from these event organizers/business users So – it was time to seek help from our mentors and the teaching team
    10.  The mentors/ teaching team’s advice was to focus on users  Could we get them?  Will they interact regularly?  Will they share with friends?  Will they attend events we suggest? Validating this meant going full force on building out our user- focused Minimum Viable Product
    11. We started too broad  We started with all types of events – and then decided to focus on Fairs and Festivals for San FranciscoParents are not our target customer  The users who signed up through our Ad-Words campaign tended to be interested in more of the singles and couples eventsAcquiring users solely through advertising is expensive!!! ($10/user acquisition cost)  Viral user acquisition is keyUser Engagement of 60% might actually be too good to be true  Developer might have been testing Facebook share functionalitySolid technical talent that can conceptualize the business goals & communicate well is CRITICAL! (Who would have thought?)Without a Data Strategy, we are dead in the water  Scraping data at scale difficult due to data inconsistencies  Challenges in locating data sources to expand geographically
    12. Canvas V3 Key Partners Key Activities Value Customer Customer Proposition Relationships Segments- Public specialists - Data Acquisition Users: (Crowdsourced) (info on events Users: - Businesses- Ticketmaster and from producers & ‐ Most comprehensive ‐ Automated services providing other ticket sellers users) list of events tailored and communities experiences to you ‐Businesses: (promoters, - Software engineering ‐ Don’t miss anything ‐ Advertising and venue owners, you’re interested in promotion support community orgs ‐ Simple (cut through - Marketing clutter) Firms/Data Key Resources ‐ Status (super users) Channels Users - Existing user base Businesses: - E-mail driven web - 18-44 Y/O of other services - Provide social graph interface Internet Users - Public/ crowd intelligence to - Web - Parents looking sourced experts advertisers/Targeted - Mobile Apps (low for family - Viral introduction Ads priority feature activities to site/service - Lead Generation only) through event - Excess event capacity sharing clearing Cost Structure Revenue Streams Advertising and paid results Lead GenerationSoftware development and maintenance Marketing DataAdvertising Support Selling empty seats or ticket sale revenue share Personalized marketing data
    13. We have customers ~40 customers in our small selected market & limited set of events We have a (feature reduced) product We provided our scrappy, hacked together, product - surprised that we were serving customersVirality is our next major hurdle and biggestconcern:•Reduction of Customer Acquisition CostsViral customer acquisition absolutely crucial to themodel, and we were unable to prove or disprove ourmechanism – will shortly•Brings scale appropriate for the revenue models
    14. We have to do what, …It was a lot of getting Looking back -(this is Steve? out of the building supposed to be happy)Feedback Local vs. International Talent Team Dynamics• Unexpectedly easy to obtain: • Our technical skill set was • Difficult to organize teamInterviews, surveys, lacking – turned to outsourcing quickly and effectivelycompetitors, partners, advisors to India • Class (group) vs. Real• We had trouble deciding • Cheap but more management Startup (leader driven)when to listen and when to than expected, especially with the • Our team wasignore what we were time differences dysfunctional – operationalseeing/hearing • Inability to experiment rapidly and time constrains•Real validation & tracking • Would have been worth the compounded issuesmore useful and we learned to upfront investment finding •Alignment of goals onbuild it into the product quality talent team
    15. User Acquisition Multi-Sided Network• Paying for traffic is easy, but not sustainable • More difficult to validate, craft a viableat $5 to $10 per user business model, and decide where to• Adwords/Facebook– only good for kicking focus firstthings off and testing hypotheses • We spent a lot of time figuring out• Need a proven mechanism for adoption where to focus next… Thank you advisors• Crafty but time consuming ways of drivingtraffic – craigslist spam, Twitter, Facebookgroups Validate then Pivot or Move Forward? • We struggled when and why a pivot might be warranted • Setting targets up front saved us time and pain, but we got smarter -eventually
    16. Social Planning Tool + Recommendation Engine + Discounts and Targeted Group DealsThe replacement for the community newspaper event guide –a personalized, simple way to find local events… Reduction of Risk With Next Steps Viral & Social Crowdsourced Data Revenue Model Sharing Acquisition Validation Even though our execution fell behind… we are testing the viral component next week – since it is a vital component to our model, if we don’t get 10% of users sharing recommended events with friends (or if we have unexpected results with user engagement), we pivot / quit
    17. Recurring E-mailProduct Refinement Event Listing & Specs (Web) Information Detailed Event Page• Getting real, useful information from customers is painful – can bemisleading• Looking at usage and customer patterns is invaluable– but implementationis critical.• Event recommendation ratings, user clicks, and social sharing of eventinformation crucial to prove for our model – we’re there but with noconclusion
    18. Beta / Kickoff Final Model Various Sources of Incentives• Technically Local Datachallenginggiven local Crowdsourced Event • Still an We Borrowed Withoutsource Asking Data unknowndifferences given our Company or selected• More work Crowdsourced Quality feature setthan Control was to testexpected in our userdriving valuequality and Web & E-mail Event Information propositionrelevance •0from thedata • Product algorithm and event taxonomy needs refinement • Served our needs in the initial stages but likely will not scale to a broader set of events