share and tell stanford 2016

Lessons learned after 130 interviews

Yegor Tkachenko, MS Marketing AnalyticsMachine Learning

Eric Peter, CS & MBAConsumer Insight ExpertManagement Consulting

Scott Steinberg, MBAMarketing Growth StrategyManagement Consulting

Karan Singhal, Undergrad CSWeb Development

User Interface Design

Share&Tell

Share&Tell

Yegor Tkachenko, MS Marketing AnalyticsMachine Learning

Eric Peter, CS & MBAConsumer Insight ExpertManagement Consulting

Scott Steinberg, MBAMarketing Growth StrategyManagement Consulting

Karan Singhal, Undergrad CSWeb 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.

NowWe 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

Cost StructureFixed - 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 Streams1. Custom research studies2. Per-feedback fees (surveys, video interviews, focus groups)3. Sales of raw data / data with automated analytics on top4. Subscriptions to the platformPricing 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 1Customer 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 empowermentEnterprises• 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 = consumerblack = enterprise

What we thought: Enterprise VPblue = consumerblack = 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


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

Replace with real data

What we did: Talk to companies who use surveys for market researchHypothesis: 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

EnterpriseWeek 1-3

What we found: Not that muchpain 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."

EnterpriseWeek 1-3

What we found: Not that muchpain 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."

EnterpriseWeek 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

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:

EnterpriseWeek 1-3

So, we focused on changing the value prop to answer new questions for marketers




consumer target?




Customer Needs Identified through Customer Discovery:

EnterpriseWeek 1-3

Value Proposition

Enterprises• Linking real-behavior with opinions (vs. stated behavior)• Ability to follow up with consumer• Faster turnaround

Value PropositionEnterprises

• 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

✘

What about the consumer?


Variable - Payment to consumers for use of their data, profit-sharing model (dividends) with consumers, consumer service reps

Revenue Streams1. Custom research studies2. Per-feedback fees (surveys, video interviews, focus groups)3. Sales of raw data / data with automated analytics on top4. Subscriptions to the platformPricing based on sample size/type, data type/amount, number of questions, feedback time

Key Resources


Value Proposition


Channels

What we thought: Consumer VPCustomer 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





Consumers• Profit sharing• Targeted ads in line with customer’s tastes• Sense of empowermentEnterprises• 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 = consumerblack = enterprise

Consumer Value Proposition Hypothesis:

Get paid for your dataFeel 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

First consumer testHypothesis: People will provide their data and opinions for money

Tested through: ~25 Customer Discovery focused consumer interviews

ConsumerWeek 1-3

Experiment: Take an MVP on an iPad to the mall

ConsumerWeek 1-3

What we learnedHypothesis: People will provide their data and opinions for money

ConsumerWeek 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

As a result: What if we offered equity instead of cash?

Solves all business needs! ● panel retention and engagement● identity verification● quality of data

ConsumerWeek 4

Google Consumer Survey: n = 500

Oh Wait… Need to Isolate VariablesAlways 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!

ConsumerWeek 4

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 1-4ConsumerWeek 1-4

Consumer:• Control over data• ???

Value Proposition

Week 1 Week 3 Week 4

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

What we did: Customer Validation!




consumer target?




✘ ✘

EnterpriseWeek 4

14 more enterprise interviews to (in)validate our hypothesized value props and identify the most acute needs

“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!

EnterpriseWeek 4

Well, why not focus on data that’s easiest to get?

Most Sensitive

Least Sensitive

Google Survey

ConsumerWeek 5

And heard from companies that Amazon data is big pain point

EnterpriseWeek 5

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

Focused value prop and segments:

Help CPG and retailers understand consumer

trends on Amazon

EnterpriseWeek 5-6


Variable - Payment/donations for use of their data, consumer service reps

Revenue Streams1. Subscriptions to insights / platform2. Per-survey fees3. Custom research studies4. Linking data to client databasesPricing based on sample size/type, data type/amount, number of questions, feedback time

Key Resources


Value Proposition

Customer Segments


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





Consumer• Website• Mobile appEnterprise• 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

EnterpriseWeek 5-6blue = consumer

black = enterprise

• Understand purchasing trends on Amazon by demographic group • Retail (traditional)

• Retail (e-commerce)• CPG with online sales

As a result: Develop low-fi MVP

EnterpriseWeek 5-6

Now, how do we incentivize consumers to provide Amazon data?

ConsumerWeek 5

We identified a few possiblealternatives to cash...

Pay cash

Provide a valuable service

$5 / $10 cash

Donate your data

(to benefit a charity)

Receive targeted

promotions

Personalized product recommen

dations

✘Had learned previously consumers more

willing to share data if they get some intrinsic value

ConsumerWeek 5

What we did: 10+ Customer Discovery interviews...and 2,000+ survey

responses

ConsumerWeek 5

What we found: “Donate your data”best meets the business’s needs

Gets Amazon data?

Retention / engageme

nt? 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

ConsumerWeek 5

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

ConsumerWeek 5

As a result: 3-sided marketConsumer

Week 6

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

SegmentConsumer:• Millennials• People who donate to charity

Segment

ConsumerWeek 6

✘✘

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

ConsumerWeek 6

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

✘

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

Share&Tell

Marketing research agencies

TNS Qualitative, ,Conifer Research,Horowitz Research,Nielsen, Kantar, IPsos,dunnhumby

Our Competitive Set Has Evolved too

Removed through pivotsOnline Survey ToolsTraditional survey panelsOnline qualitative research

Behavioral Consumer Panels (w/ or w/o surveys)

Nielsen, NPD, IRI, LuthResearch,VertoAnalytics, RealityMine, comScore

SHARE & TELL

ConsumerWeek 6

Nonprofits might not be the right routeWhat 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?

NonprofitsWeek 7-9

Non-profits may not be most

efficient consumer acquisition path.

What we did: Tested ‘direct to consumer’ using a high fidelity MVP...

https://www.datadoesgood.com

ConsumerWeek 7-9

https://www.datadoesgood.com/

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%

ConsumerWeek 9


Variable - Payment/donations for use of their data, consumer service reps

Revenue Streams1. Subscriptions to insights / platform2. Per-survey fees3. Custom research studies4. Linking data to client databasesPricing based on sample size/type, data type/amount, number of questions, feedback time

Key Resources


Value Proposition

Customer Segments


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 itNonprofits??• 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

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

Thank you, George!

Appendix

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

EnterpriseWeek 4

...for 3 generic enterprise segments

EnterpriseWeek 4

Retailers

Traditional

E-Commerce

1

2

CPG

With online sales

Without online sales

3

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

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?


2 Market research team writes a survey that will inform the answer



Emotions / Feelings● Why did you buy?● What matters to you?● ...

Survey

5 - 10 minutes of questions

10 - 15 minutes of questions


3 Survey sent to consumers through a ‘panel provider’




you?● ...

Survey

$ / person

Panel ProviderMarket Research team




you?● ...

Survey


4 Consumers answer survey based on their memory


Self reported data


5 Market research team analyzes data to develop an answer

Market Research team

Insight & recommended business action




you?● ...

Survey

...Where we thought we fit in




Why can’t this be based on actual (vs. self reported)

data?




you?● ...

Survey





...let’s be a “next gen” panel provider that merges real

data with opinions


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

Other learnings

Presenting

Share the key insights that led to a decision or answer.

Don’t just share the answer

Example: Equity IdeaWe 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

Group work

1. Set up regular recurring meetings at least twice a week

2. Carefully consider if the task is best performed by a group or by an individuala. 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

3. If there is any tension, discuss it explicitly

4. Don’t take criticism of your ideas personally

5. Humor helps

Launchpad Methodology/Process1. Applying the scientific method to business model is extremely

usefula. treating all ideas as hypotheses prevents attachment to bad

ideasi. 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

2. Interviewsa. what people initially say is not what they would actually do

i. need to push commitment to see what they actually dob. interviews with experts are a quick way to get a lay of an

industryc. 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 peoplei. early interviews should focus on figuring out who they are

share and tell stanford 2016

Education