2. + +
 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

3. Nathan Blumberg
Principal Internal Auditor, LSI Corporation Mentors
Role: Finance and Auditing
Sumeet Jain
Partner, CMEA Capital
Ranjit Jose
Director, Global Product & Solutions Marketing, Model N
Role: Sales and Marketing
Spencer Looney
President, Grove Land Pete Vlastelica
Founder, Yardbarker
Role: Event Production & Strategy
Praveen Rutnam
Group Product Planner, Microsoft Corporation
Role: Product Management - TV and Gaming Industry

4. 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 group
Software development and maintenance
advertising
Advertising Support
From Internet Users; Free/Freemium

5.  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

6.  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)

7.  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.

8. Key Partners Key Activities Value Customer Customer
Proposition Relationships Segments
- Data Acquisition Users: Users: - Businesses
-Ticketmaster and (info on events • Most comprehensive • Automated services providing
other ticket sellers from list of events tailored and communities experiences
- Spotify, Last.fm, 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 Generation
Software development and maintenance
Marketing Data
Advertising Support
Selling empty seats or ticket sale revenue share
Personalized marketing data

9.  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….

10.  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

11.  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

15. We started too broad
 We started with all types of events – and then decided to focus on Fairs and Festivals for
San Francisco
Parents 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 events
Acquiring users solely through advertising is expensive!!! ($10/user acquisition cost)
 Viral user acquisition is key
User Engagement of 60% might actually be too good to be true
 Developer might have been testing Facebook share functionality
Solid 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

16. 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 Generation
Software development and maintenance
Marketing Data
Advertising Support
Selling empty seats or ticket sale revenue share
Personalized marketing data

17. 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 customers
Virality is our next major hurdle and biggest
concern:
•Reduction of Customer Acquisition Costs
Viral customer acquisition absolutely crucial to the
model, and we were unable to prove or disprove our
mechanism – will shortly
•Brings scale appropriate for the revenue models

18. 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 team
Interviews, surveys, lacking – turned to outsourcing quickly and effectively
competitors, 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 was
ignore what we were time differences dysfunctional – operational
seeing/hearing • Inability to experiment rapidly and time constrains
•Real validation & tracking • Would have been worth the compounded issues
more useful and we learned to upfront investment finding •Alignment of goals on
build it into the product quality talent team

19. User Acquisition Multi-Sided Network
• Paying for traffic is easy, but not sustainable • More difficult to validate, craft a viable
at $5 to $10 per user business model, and decide where to
• Adwords/Facebook– only good for kicking focus first
things 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 driving
traffic – craigslist spam, Twitter, Facebook
groups
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

20. Social Planning Tool + Recommendation
Engine
+ Discounts and
Targeted Group Deals
The 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

22. Recurring E-mail
Product Refinement Event Listing
& Specs (Web)
Information Detailed Event
Page
• Getting real, useful information from customers is painful – can be
misleading
• Looking at usage and customer patterns is invaluable– but implementation
is critical.
• Event recommendation ratings, user clicks, and social sharing of event
information crucial to prove for our model – we’re there but with no
conclusion

23. Beta / Kickoff Final Model
Various Sources of
Incentives
• Technically Local Data
challenging
given local Crowdsourced Event • Still an
We Borrowed Without
source Asking Data unknown
differences given our
Company or selected
• More work Crowdsourced Quality feature set
than Control was to test
expected in our user
driving value
quality and Web & E-mail Event Information proposition
relevance •0
from the
data
• 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