agile, business model, corporate innovation, customer development, lean, lean startup, lean launchpad, stanford, steve blank, video, engr 245

1. Lessons learned
after 130 interviews
Yegor Tkachenko, MS
Marketing Analytics
Machine Learning
Eric Peter, CS & MBA
Consumer Insight Expert
Management Consulting
Scott Steinberg, MBA
Marketing Growth Strategy
Management Consulting
Karan Singhal, Undergrad CS
Web Development
User Interface Design
Share&Tell

2. Share&Tell
Yegor Tkachenko, MS
Marketing Analytics
Machine Learning
Eric Peter, CS & MBA
Consumer Insight Expert
Management Consulting
Scott Steinberg, MBA
Marketing Growth Strategy
Management Consulting
Karan Singhal, Undergrad CS
Web Development
User Interface Design
Day 1 (Clarified)
We create a way for consumers to
make money by actively sharing
their behavioral data and
opinions.
Through this data, we help
companies unlock previously
unattainable insights.
Now
We help retailers and CPG
companies understand online
shopping behavior.
We do this by creating a platform
for people to donate their
Amazon shopping history
to raise money for charity.
130
Interviews
3,500+
Survey
responses

3. Cost Structure
Fixed - Infrastructure, servers, team of data scientists,
corporate sales force, project managers & analysts,
product & user experience development team
Variable - Payment to consumers for use of their data, profit-
sharing model (dividends) with consumers, consumer
service reps
Revenue Streams
1. Custom research studies
2. Per-feedback fees (surveys, video interviews, focus groups)
3. Sales of raw data / data with automated analytics on top
4. Subscriptions to the platform
Pricing based on sample size/type, data type/amount, number of questions,
feedback time
Key Resources
Key ActivitiesKey Partners Value Proposition Customer
Relationships
Channels
Business Canvas - Week 1
Customer
Segments
Consumers
• Millennials/students
• Lower income
consumers with
smartphones
• Existing research
participants
Enterprises
• Marketing agencies,
consulting
• Marketing
departments at large
companies
• Marketing
departments at non-
large CPG companies
• Panel acquisition, retention,
incentivization, quality control
• Automated seamless
insights extraction
• Data security
• Empowered customer
service (for consumer)
• Sales force, customer
service knowledgable about
market research design &
execution
• Historical granular data
• Automated platform for
seamless insights
extraction
• Expertise in market
research methodology,
execution, statistics
Consumers
• Profit sharing
• Targeted ads in line
with customer’s tastes
• Sense of empowerment
Enterprises
• Unique data,analysis
• Easy and fast way to do
it
Consumer
• Website
• Mobile app
Enterprise
• Direct web portal
• Resold through market
research agencies
• Custom consulting &
research design services
Consumers
• Getting paid for data that
has already been shared,
but from which individuals
are not profiting
• Provide sense of
empowerment and control
over data
• Offers a natural, effortless
way to share opinions
• Feel heard and that
opinion matters
Enterprises
• Linking real-behavior with
opinions (vs. stated
behavior)
• Ability to follow up with
consumer
• Faster turnaround
• Data API providers
• Data aggregators
• Marketing agencies
• Panel participants
blue = consumer
black = enterprise

4. What we thought: Enterprise VP blue = consumer
black = enterprise
Enterprise Value Proposition:
Replace traditional survey providers by:
● Linking real behavior with opinions (vs.
stated behavior)
● Ability to follow up with consumer
● Faster turnaround
Key
Resources
• Historical granular
data
• Automated
platform for
seamless insights
extraction
Demographics
● Age?
● Gender?
● ...
Behavior
● Where did you buy?
● What? How much?
● ...
Emotions / Feelings
● Why did you buy?
● What matters to you?
● ...
Survey
Surveys are based on
SELF REPORTED data

5. What we did: Talk to companies
who use surveys for market research
Hypothesis:
We can replace existing
panel vendors if we have
real behavioral data
(as opposed to self-reported
data)
What we did:
12 Customer Discovery
interviews with companies
that conduct market research
using surveys
Enterprise
Week 1-3

6. What we found: Not that much
pain with self-reported data...
“Self-reported data isn’t
great, but it’s directionally
good enough.”
“With real data, we’d get the
same insight as we do now,
but perhaps we’d be slightly
more confident.”
“In order to switch
vendors, you need to be
able to answer a question
we can’t answer today”
“We have to use [vendor] -
we have a long term
contract through our HQ."
Enterprise
Week 1-3

7. What we found: Not that much
pain with self-reported data...
“Self-reported data isn’t
great, but it’s directionally
good enough.”
“With real data, we’d get the
same insight as we do now,
but perhaps we’d be slightly
more confident.”
“In order to switch
vendors, you need to be
able to answer a question
we can’t answer today”
“We have to use [vendor] -
we have a long term
contract through our HQ."
Enterprise
Week 1-3
Adding behavioral data alone does
not make us 10x better.
We need to be able to answer a specific
question that marketers can’t answer
today

8. So, we focused on changing the value prop
to answer new questions for marketers
How should I
identify my
consumer target
(SMB Businesses)
How do I better
understand my
consumer target?
What is the path to
purchase for online
and omnichannel
shopping?
What are current online
shopping trends?
Customer Needs Identified through Customer Discovery:
Enterprise
Week 1-3

9. So, we focused on changing the value prop
to answer new questions for marketers
How should I
identify my
consumer target
(SMB Businesses)
How do I better
understand my
consumer target?
What is the path to
purchase for online
and omnichannel
shopping?
What are current online
shopping trends?
Customer Needs Identified through Customer Discovery:
Enterprise
Week 1-3
Value Proposition
Enterprises
• Linking real-
behavior with
opinions (vs. stated
behavior)
• Ability to follow up
with consumer
• Faster turnaround
Value Proposition
Enterprises
• Identify target
consumers to
increase marketing
ROI
• Deeper and more
accurate behavioral
understanding of
consumer
segments
• Understand
online/omnichannel
path to purchase
• Understand online
market trends at
consumer level
Week 1 Week 3
✘

10. What about the consumer?

11. Cost Structure
Fixed - Infrastructure, servers, team of data scientists,
corporate sales force, project managers & analysts,
product & user experience development team
Variable - Payment to consumers for use of their data, profit-
sharing model (dividends) with consumers, consumer
service reps
Revenue Streams
1. Custom research studies
2. Per-feedback fees (surveys, video interviews, focus groups)
3. Sales of raw data / data with automated analytics on top
4. Subscriptions to the platform
Pricing based on sample size/type, data type/amount, number of questions,
feedback time
Key Resources
Key ActivitiesKey Partners Value Proposition Customer
Relationships
Channels
What we thought: Consumer VP
Customer
Segments
Consumers
• Millennials/students
• Lower income
consumers with
smartphones
• Existing research
participants
Enterprises
• Marketing agencies,
consulting
• Marketing
departments at large
companies
• Marketing
departments at non-
large CPG companies
• Panel acquisition, retention,
incentivization, quality control
• Automated seamless
insights extraction
• Data security
• Empowered customer
service (for consumer)
• Sales force, customer
service knowledgable about
market research design &
execution
• Historical granular data
• Automated platform for
seamless insights
extraction
• Expertise in market
research methodology,
execution, statistics
Consumers
• Profit sharing
• Targeted ads in line
with customer’s tastes
• Sense of empowerment
Enterprises
• Unique data,analysis
• Easy and fast way to do
it
Consumer
• Website
• Mobile app
Enterprise
• Direct web portal
• Resold through market
research agencies
• Custom consulting &
research design services
Consumers
• Getting paid for data that
has already been shared,
but from which individuals
are not profiting
• Provide sense of
empowerment and control
over data
• Offers a natural, effortless
way to share opinions
• Feel heard and that
opinion matters
Enterprises
• Linking real-behavior with
opinions
• Ability to follow up with
consumer
- Faster turnaround
• Give additional context in
traditional surveys
• Data API providers
• Data aggregators
• Marketing agencies
• Panel participants
blue = consumer
black = enterprise
Consumer Value Proposition
Hypothesis:
Get paid for your data
Feel in control of your data
Feel heard and that opinions matter
...and, that consumers are willing
to provide all these data types:
• Social media likes & posts
• Email purchase receipts
• Credit card purchase history
• Amazon.com purchase history
• GPS location history
• Web and search history

12. First consumer test
Hypothesis:
People will provide their
data and opinions for
money
Tested through:
~25 Customer Discovery
focused consumer interviews
Consumer
Week 1-3

13. Experiment: Take an MVP on
an iPad to the mall
Consumer
Week 1-3

14. What we learned
Hypothesis:
People will provide their
data and opinions for
money
Consumer
Week 1-3
Findings:
People will provide data and opinions for money, BUT
Only younger and poorer consumers were interested
Cash-based model had other problems too:
● Doesn’t support retention and engagement
● Misaligned incentives
● Not scalable to get to large # of consumers
Tested through:
~25 Customer Discovery
focused consumer interviews

15. As a result: What if
we offered equity instead of cash?
Solves all business needs!
● panel retention and engagement
● identity verification
● quality of data
Consumer
Week 4
Google Consumer Survey: n = 500

16. Oh Wait… Need to Isolate Variables
Always be skeptical of your data!
Consumers aren’t interested in concept of being a
partial owner - they cared about the extra cash!
Designing a good experiment just
saved us 49% of our equity...phew!
Consumer
Week 4

17. Value Proposition
Consumer:
• Getting paid for
data that has
already been
shared, but from
which individuals
are not profiting
• Provide sense of
empowerment and
control over data
• Offers a natural,
effortless way to
share opinions
• Feel heard and
that opinion matters
By Week 4, We Had No Idea What
Consumer Value Prop Should Be
Value Proposition
Consumer:
• Getting
compensated for
data that has
already been
shared
• Provide sense of
empowerment,
control over data
• Partial
ownership of
company
Week 1-4
Consumer
Week 1-4
Consumer:
• Control over data
• ???
Value Proposition
Week 1 Week 3 Week 4

18. Let’s first focus on narrowing down
enterprise value prop to see what
data we need.

19. What we did: Customer Validation!
How should I
identify my
consumer target
(SMB Businesses)
How do I better
understand my
consumer target?
What is the path to
purchase for online
and omnichannel
shopping?
What are current online
shopping trends?
✘ ✘
Enterprise
Week 4
14 more enterprise interviews to (in)validate our
hypothesized value props and identify the most acute needs

20. “Great value prop guys, but I
challenge you - if you had to do
something tomorrow as an MVP,
what would it be? This is a LOT to
do!”
Note: Quote paraphrased, concept of “Big Idea” was likely referenced
Key learning: A startup can’t do everything. It needs to
do one thing well!
Enterprise
Week 4

21. Well, why not focus on data
that’s easiest to get?
Most
Sensitive
Least
Sensitive
Google Survey
Consumer
Week 5

22. And heard from companies that
Amazon data is big pain point
Enterprise
Week 5

23. As a result: An aha moment...
Share & Tell…
...helps better understand your target's online &
omnichannel shopping & purchasing behavior
• What is purchased on Amazon.com?
• What is my online/omni market share? Why?
• Where else does my target shop? Why?
• What does my target do before they buy? What
is their shopping path? Why?
• What products does my customer buy / not
buy? What do they buy with my product? Why?
...helps better understand your target's persona /
where to reach them
• What online behaviors (sites, apps, etc…)?
• What media consumption habits?
• What do they search for online?
• What activities, interests, hobbies?
• What demographics?
...provides ability to more directly and
narrowly communicate with your target
• Direct messaging / promos on S&T platform
• Better targeting on existing ad networks
Enterprise
Week 5-6

24. Cost Structure
Fixed - Infrastructure, servers, team of data scientists, corporate
sales force, project managers & analysts, product & user
experience development team
Variable - Payment/donations for use of their data, consumer
service reps
Revenue Streams
1. Subscriptions to insights / platform
2. Per-survey fees
3. Custom research studies
4. Linking data to client databases
Pricing based on sample size/type, data type/amount, number of questions,
feedback time
Key Resources
Key ActivitiesKey Partners Value Proposition Customer
Segments
Customer
Relationships
Channels
Resulting Business Canvas
Consumers
• Smartphone using
consumers who shop online
• Millennials
• Existing research
participants
• People who currently give
to charity
Enterprises
• Retail (traditional)
• Retail (e-commerce)
• CPG with online sales
• Panel acquisition, retention,
incentivization, quality control
• Automated seamless
insights extraction
• Data security
• Empowered customer
service (for consumer)
• Sales force, customer
service knowledgable about
market research design &
execution
• Historical granular data
• Automated platform for
seamless insights
extraction
• Expertise in market
research methodology,
execution, statistics
Consumer
• Website
• Mobile app
Enterprise
• Direct web portal
supported by research-
experience B2B sales force
• Projects sold through
market research & strategy
firms
Consumers
• Get: Charities send invitations
• Get/Keep: Shopping discovery
+ targeted discounts app
• Keep: Reports / comparisons
of your data
Enterprises
• Get:partnership,telesales,PR
• Keep: Unique data, analysis
• Easy and fast way to do it
Consumers
• Feel good by donating data
to charity
• (potentially) Service to
discover, get discounts on,
and buy stuff online
Enterprises
• Understand purchasing
trends on Amazon by
demographic group
• Data API providers
• Data aggregators
• Marketing agencies
• Panel participants
• Charities/non-profits
Enterprise
Week 5-6
blue = consumer
black = enterprise
• Understand purchasing
trends on Amazon by
demographic group • Retail (traditional)
• Retail (e-commerce)
• CPG with online sales

25. As a result: Develop low-fi MVP
Enterprise
Week 5-6

26. Now, how do we incentivize
consumers to provide Amazon data?
Consumer
Week 5

27. We identified a few possible
alternatives to cash...
Pay
cash
Provide a
valuable service
$5 / $10 cash
Donate your
data
(to benefit a
charity)
Receive
targeted
promotions
Personalized
product
recommenda
tions
✘
Had learned previously consumers more willing to
share data if they get some intrinsic value
Consumer
Week 5

28. What we did: 10+ Customer Discovery
interviews...and 2,000+ survey responses
Consumer
Week 5

29. What we found: “Donate your data”
best meets the business’s needs
Gets Amazon
data?
Retention /
engagement? Quality? Large #? Outcome
$5 / $10
cash
✔ Cash is king! ✘ May be
transactional /
one-shot deal
✘ Limits to low
income
✔ ~>50%
interested
Kill for now or
use in combo
w/ donations
Donate
your data
✔ Interest in
‘doing good’
✔ Donation
implies opp to
ask for future
donation
✔ Consumer
leads verified
through charities
✔ ~27%
interested
Focus for class;
need to
understand
impact of bias
Targeted
promos
✘ Does not
solve major pain,
already available
✔ Creates clear
gain w. reason
to come back
✔ Can verify
respondent
behavior
✘ Quant test
running,
qualitatively
poor reaction
Test for “keep /
grow” insteadProduct
recs
✘ Limited
interest - does
not solve pain,
not 10X better
than others
✔ Creates clear
gain w. reason
to come back
-- Unclear if able
to verify
respondent
• Need 0.75% of TAM to register (1M / 150M)
• Of those interested, ~3% will register
• Implies >25% interested
Consumer
Week 5

30. What we found: Consumers
skeptical of donation scams
“I’d donate my Amazon
data to raise money for
charity X, but only if that
charity asked me too”
“I probably would not
donate to a random
startup unless I knew for
sure that they were legit”
Nonprofits should send
out communication
asking people to donate
their data
Nonprofits are a
customer acquisition
channel and a new
customer segment
Consumer
Week 5

31. As a result: 3-sided market
Consumer
Week 6

32. Value Proposition
Consumer:
• Control over data
• ???
Consumer:
• Feel good by
donating data to
charity
• Doesn’t cost
money to donate
Value Proposition
Week 3 Week 5
Resulting BMC changes (I)
Consumer:
• Millennials &
students
• Lower income
consumers with
smartphones
• Existing research
participants
Segment
Consumer:
• Millennials
• People who
donate to charity
Segment
Consumer
Week 6
✘
✘

33. Value Proposition
Non-Profit:
• A new revenue
stream
• A new way to
engage with donor
base
• A way to get
donations without
pushback
Value Proposition
Week 3 Week 5
Resulting BMC changes (II)
Segment
Non-Profit:
• All non-profits
Segment
Consumer
Week 6

34. Resulting BMC changes (III)
Consumer
Week 6
Consumer:
• Targeted ads in
line with
customer’s tastes
• Sense of
empowerment
Cust. Relationship
Consumer:
• Get: Charities
send invitations
Cust. Relationship
Need to test this
✘

35. eCommerce Data
& Insight
Companies
Data aggregators
Online Donation
Tools and
Platforms
Slice, Clavis,
Profiteero, One
Click Retail,
Profiteero,
Return Path,
Paribus?
Data Wallet,
Datacoup, Infoscout,
Axciom, Experian,
LiveRamp, SuperFly
Razoo, CrowdRise,
Causes, Survey Monkey,
One Big Tweet,
GoodSearch,
AmazonSmile
Marketing research
agencies
TNS Qualitative, ,
Conifer Research,
Horowitz Research,
Nielsen, Kantar, IPsos,
dunnhumby
Our Competitive
Set Has Evolved
too
Removed through pivots
Online Survey Tools
Traditional survey panels
Online qualitative research
Behavioral
Consumer Panels
(w/ or w/o surveys)
Nielsen, NPD, IRI,
LuthResearch,
VertoAnalytics,
RealityMine,
comScore
SHARE
& TELL
Consumer
Week 6

36. Nonprofits might not be the right
route
What we did:
Interviewed 10+
nonprofits
Tested email
campaign to 60
nonprofits to gauge
interest
What we learned:
● Only nonprofits who value
smaller donations (<$100)
from larger base of people
were interested in the
model
● Nonprofits are slow to
make decisions and risk-
averse
So what?
Focus more efforts on
testing viability of
direct to consumer
route.
Key hypothesis to test:
Can we build enough
trust through social
media and website?
Nonprofits
Week 7-9
Non-profits may not be
most efficient
consumer acquisition
path.

37. What we did: Tested ‘direct to
consumer’ using a high fidelity MVP...
https://www.datadoesgood.com
Consumer
Week 7-9

38. What we learned: ‘Direct to consumer’
might be a viable route
Arrived to the
landing page
Clicked
‘donate now’
Logged in with
Facebook
Shared Amazon
data
Filled out
demographics
100%
~18%
~6%
~6%
~5%
~80%
~95%
~55%
Choose
a charity ~11%
~60%
25%
Consumer
Week 9

39. Cost Structure
Fixed - Infrastructure, servers, team of data scientists, corporate
sales force, project managers & analysts, product & user
experience development team
Variable - Payment/donations for use of their data, consumer
service reps
Revenue Streams
1. Subscriptions to insights / platform
2. Per-survey fees
3. Custom research studies
4. Linking data to client databases
Pricing based on sample size/type, data type/amount, number of questions,
feedback time
Key Resources
Key ActivitiesKey Partners Value Proposition Customer
Segments
Customer
Relationships
Channels
Consumers
• Online shoppers
• Current charity givers
• Millennials
• Existing research
participants
Enterprises
• Buyers at e-commerce
retailers
• Marketers at CPG with
online sales
Nonprofits??
• Hungry for donations and
values small donations from
large # of donors
• Private donations are main
revenue stream
• Donor acquisition??
• Donor retention and
engagement??
• Data quality control
• Data security and storage
• Automated analytics
• Custom analytics
• Sales force
• Legal
• Physical - workspace, servers
• Additional human (short-term) - Full-
stack software engineer, Database
architect, Security consultant, Legal
Consultant, Advisors/Industry Movers
(long-term) - Sales team, Analytics
team, Security team, Engineering
team, Advisors
• Intellectual - Trademarks, Contracts
with clients, Proprietary analytic tools,
Software copyright
• Financial - angel/venture funding
Consumers
• Website
• Mobile app
Enterprises
• Web portal supported by
B2B sales force
• Projects through market
research & strategy firms
Nonprofits??
• Web portal
Consumers
• Get: Social media campaigns &
charities send invitations
• Keep: Reports / comparisons
of your data
Enterprises
• Get:partnership,telesales,PR
• Keep: Unique data, analysis
• Easy and fast way to do it
Nonprofits??
• Get: telesales, PR
Consumers
• Feel good by donating data
to charity
• Donating is free & easy
Enterprises
• Understand purchasing
trends on Amazon by
demographic group.
brand preference
Nonprofits??
• A new revenue stream
• A new way to engage with
donor base
• A way to get donations
without pushback
Short Term:
• Charities/non-profits
• Nonprofit
hubs/associations
• Legal
• Other collectors of
online purchase history
Long Term
• Data API providers
• Data aggregators
• E-commerce retailers
• Ad networks and
programmatic ad
buyers?
Final Business Model Canvas Week 10

40. So...what’s next...
We are going to continue working on this
after the class.
Can we gain traction with
consumers?
Several additional experiments we want to
run incorporating feedback from our MVP.
● Facebook “nominations”
● Linking more directly to causes
● Many improvements to the MVP
Can we get a letter of intent
from any businesses?
We continue to hear companies say they
are interested and that this data is
valuable. Is one willing to sign a non-
binding letter of intent
First Priority Second Priority

41. Thank you, George!

42. Appendix

43. What we learned: Refined value
proposition for enterprise...
Share & Tell…
...helps better understand your target's online &
omnichannel shopping & purchasing behavior
• What is purchased on Amazon.com?
• What is my online/omni market share? Why?
• Where else does my target shop? Why?
• What does my target do before they buy? What
is their shopping path? Why?
• What products does my customer buy / not
buy? What do they buy with my product? Why?
...helps better understand your target's persona /
where to reach them
• What online behaviors (sites, apps, etc…)?
• What media consumption habits?
• What do they search for online?
• What activities, interests, hobbies?
• What demographics?
...provides ability to more directly and
narrowly communicate with your target
• Direct messaging / promos on S&T platform
• Better targeting on existing ad networks
Enterprise
Week 4

44. ...for 3 generic enterprise segments
Enterprise
Week 4
Retailers
Traditional
E-Commerce
1
2
CPG
With online sales
Without online sales
3

45. What is market research?
Comes in many forms...
1. Surveys to understand consumer opinions
/ emotions
2. Data to understand market trends
Initial hypothesis:
“disrupt” survey-based market research

46. A quick primer:
How do surveys work?
What features do
my customers care
about?
1 Business asks a question about their customer
What does my
most valuable
customer look
like?
What drives
customer loyalty?

47. A quick primer:
How do surveys work?
2 Market research team writes a survey that will inform the answer
Demographics
● Age?
● Gender?
● ...
Behavior
● Where did you buy?
● What? How much?
● ...
Emotions / Feelings
● Why did you buy?
● What matters to you?
● ...
Survey
5 - 10 minutes of
questions
10 - 15 minutes
of questions

48. A quick primer:
How do surveys work?
3 Survey sent to consumers through a ‘panel provider’
Demographics
● Age?
● Gender?
● ...
Behavior
● Where did you buy?
● What? How much?
● ...
Emotions / Feelings
● Why did you buy?
● What matters to you?
● ...
Survey
$ / person
Panel ProviderMarket Research team

49. Demographics
● Age?
● Gender?
● ...
Behavior
● Where did you buy?
● What? How much?
● ...
Emotions / Feelings
● Why did you buy?
● What matters to you?
● ...
Survey
A quick primer:
How do surveys work?
4 Consumers answer survey based on their memory
Panel ProviderMarket Research team
Self
reported
data

50. A quick primer:
How do surveys work?
5 Market research team analyzes data to develop an answer
Market Research team
Insight &
recommended
business action

51. Demographics
● Age?
● Gender?
● ...
Behavior
● Where did you buy?
● What? How much?
● ...
Emotions / Feelings
● Why did you buy?
● What matters to you?
● ...
Survey
...Where we thought we fit in
4 Consumers answer survey based on their memory
Panel ProviderMarket Research team
3 Survey sent to consumers through a ‘panel provider’
Why can’t this be based on
actual (vs. self reported)
data?

52. Demographics
● Age?
● Gender?
● ...
Behavior
● Where did you buy?
● What? How much?
● ...
Emotions / Feelings
● Why did you buy?
● What matters to you?
● ...
Survey
...Where we thought we fit in
4 Consumers answer survey based on their memory
Panel ProviderMarket Research team
3 Survey sent to consumers through a ‘panel provider’
...let’s be a “next gen” panel
provider that merges real
data with opinions

53. ...Where we thought we fit in
What data?
• Social media likes & posts
• Email purchase receipts
• Credit card purchase
history
• Amazon.com purchase
history
• GPS location history
• Web and search history
Opinions how?
• Record short video / audio
clips
• Take <5 min surveys
• Write reviews
• 1-1 text chats

54. Other learnings

55. Presenting
Share the key insights that led
to a decision or answer.
Don’t just share the answer
Example: Equity Idea
We learned a, b, & c...therefore we want to
do “x”
VS.
We want to do “x”. Here is some rationale
for why.
Preempt question the
audience might ask and prepare
responses.
Don’t bullshit if you don’t know
the answer. It’s okay to say need
time investigate it.
1 2

56. Group work
1. Set up regular recurring meetings at least twice a week
1. Carefully consider if the task is best performed by a group or by an
individual
a. Everyone wants to participate in decision making, but it is often more
efficient if a single person completes 80% of the task and the group
then finishes the rest
1. If there is any tension, discuss it explicitly
1. Don’t take criticism of your ideas personally
1. Humor helps

57. Launchpad Methodology/Process
1. Applying the scientific method to business model is extremely useful
a. treating all ideas as hypotheses prevents attachment to bad ideas
i. also encourages rapid iteration to get to better ideas faster
b. using MVPs as tests of ideas rather than finished products avoids
wasting tons of development time
1. Interviews
a. what people initially say is not what they would actually do
i. need to push commitment to see what they actually do
b. interviews with experts are a quick way to get a lay of an industry
c. it’s surprisingly easy to get interviews with experts with a warm intro,
student status, and the purpose of learning as much as we can
d. need to clarify customer segment as early as possible to interview the
right people
i. early interviews should focus on figuring out who they are