The document discusses optimizing a matching platform between early adopters and startups, noting that feedback loops may be more important than direct matching and that finding early adopters in places like Best Buy may not be effective. It provides statistics on email campaigns and social media promotions for the platform as well as results from interviews at retail locations that provided feedback on improving the landing page and shortening questionnaires. Key next steps mentioned are confirming high quality matches, determining startups' willingness to pay, and optimizing the feedback loop.
6. *Users prefer *Users prefer
browsing curated list
*Feedback loop *Startups prefer
is more matching
important than
matching
7. Risk assumptions Priority Results Dependencies
over
time
Early Adopter 1 -> 1 Strong initial Technology,
Demand traction website quality
Startup Demand 2 -> 1 Challenging Quality of early
early results by adopters. Feedback
# of signups loop
Startup willingness to 2 -> 1 Insufficient Match quality. Cost
pay data of substitute
services.
Optimizing match 1 -> 2? Promising early New Registration.
between users and results High quality users
startup
11. 20 Interviews @ BestBuy + Apple
6 Early adopters
12. 9 Said questionnaire is too long
5 Said the language on landing site is
sub optimal
13. “You want early adopters. You’re
in f*cking Best Buy. What about
mass-market big-box retail screams “Your questionnaire is
Early – adopter….go where rich too damn long; and
people shop / wealth could be tied your response
to early-adopters…” categories should be
condensed”.
“What’s up with
the copy on your
landing page – too
cool for school and
cheesy”?