Develop- ment Research Business Units Innovation at 50x 031616

Innovation at 50x Steve Blank @sgblank www.steveblank.com 03/16/16

What is Innovation?

Innovation ≠ Incubator Innovation ≠ Accelerator Innovation ≠ Startup Innovation ≠ Lean Anything Innovation ≠ Open Innovation ≠ Reorganization

Innovation ≠ Incubator Innovation ≠ Accelerator Innovation ≠ Startup Innovation ≠ Café’s These are all physical places to do innovation

Innovation ≠ Incubator Innovation ≠ Accelerator Innovation ≠ Startup Innovation ≠ Café’s These are all physical places to do innovation Having them does not guarantee any innovation will happen

Innovation Development • Places: – R&D, Engineering, Incubators, Accelerators, Hackathons, Open Innovation, etc. • Methodology: – Waterfall, Agile, Lean • Pedagogy: – Lean LaunchPad/I-Corps, Case-studies, Mentors • Metrics: – KPI’s, StateGate®, ECV, Pivots, IRL, TRL, … • Funding – VC, Corporate VC, M&A, etc.

Innovation Development • Places: – R&D, Engineering, Incubators, Accelerators, Hackathons, Open Innovation, etc. • Methodology: – Waterfall, Agile, Lean • Pedagogy: – Lean LaunchPad/I-Corps, Case-studies, Mentors • Metrics: – KPI’s, StateGate®, ECV, Pivots, IRL, TRL,… • Funding – VC, Corporate VC, M&A, Incremental, etc.

Innovation Is? Satisfying users current or future wants/needs by turning an idea into a product or service

Innovation Is? Satisfying users current or future wants/needs by turning an idea into a product or service with speed and urgency, using minimal resources and costs

Innovation Succeeds • Where there is a path to adoption • When it fits into the overall mission and strategy • Because it performs, has metrics, … • It is managed as an innovation portfolio • And has management support (the spirit of “yes”)

Continuous disruption requires Continuous Innovation Steve Blank

Continuous disruption requires Continuous Innovation Steve Blank what’s this mean? ∧

20th Century Corporate Lifecycle

21st Century Corporate Lifecycle

Continuous disruption requires Continuous Innovation Steve Blank

Continuous Innovation requires new management tools Steve Blank

Continuous Innovation requires new management tools Lean Innovation Management Steve Blank

Why Lean Innovation Management? 10x the number of initiatives in 1/5 the amount of time 50x

Can You Create an Organization that Executes and Innovates?

Can You Create an Organization that Executes and Innovates? It’s Called an Ambidextrous Organization Source: James March, Charles O’Reilly, Michael Tushman

An Ambidextrous organization achieves breakthrough innovations Source: James March, Charles O’Reilly, Michael Tushman

An Ambidextrous organization achieves breakthrough innovations while relentlessly improving the way they execute current business model Source: James March, Charles O’Reilly, Michael Tushman

An Ambidextrous organization achieves breakthrough innovations while relentlessly improving the way they execute current business model and serve existing customers Source: James March, Charles O’Reilly, Michael Tushman

Here’s How

Types of Company Innovation Steve Blank

Three Horizons of Innovation Source: Baghai, Coley, White Mature Business our established capabilities Rapidly Growing Business Emerging Business

Three Horizons of Innovation Source: modified Baghai, Coley, White our established capabilities

New Three Horizons of Innovation Known Unknown Partially Known Level of innovation is defined by whether the business model is being executed, extended or explored! Execute Explore Extend

Three Horizons of Innovation Existing Business Model: Process Innovation Execute Core Mission Known

Three Horizons of Innovation Existing Business Model: Process Innovation Execute Known Partially Known New Opportunities via Business Model Innovation Extends Core Business

Three Horizons of Innovation Existing Business Model: Process Innovation Execute New Opportunities via Business Model Innovation Execute/Search Known Unknown Partially known New/Disruptive Business Model Explores

Capabilities/Risk Assessment Existing Capabilities Low Risk Need New Capabilities High Risk Some Capabilities Moderate Risk Known Unknown Partially known

Innovation Allocation Across the Horizons Known Unknown Partially known 60-70% 20-30% 5-10%

Return on Investment by Horizon Known Unknown Partially known ROI 1-3 years • Improve • Partner • Acquire ROI 4-6 years • Extend • Invest • Partner • Acquire ROI 4-10 years • Incubate • Invent • Invest • Acquire Evangelos Simoudis/Steve Blank

Process Innovation Product Mgmt Is the Current Process for Horizon 1 Horizon 1 Extend the core Product Management Known Stakeholders Use traditional methodologies for Horizon 1 projects Steve Blank

Horizon 1: Roadmap Driven R&D • Use product roadmap • Success: use in next gen product – With “better” performance than last gen • Corporate competence: Predictable product improvement • Assets: IP, Advanced design Model$1:$Roadmap`Driven$ Example:$Processor$Roadmap$$ Features$$and$ Performance$ Target$of$2018?$ Source: Ikhlaq Sidhu, UC Berkeley

Horizon 1: Market/Customer Driven • R&D decide their own projects with signals from: – Pilot studies – Business Unit or CTO priorities – External: start-ups and academic – Demo days or open interfaces to suppliers, customers, universities • Projects must be relevant to core competencies • Success: is awareness, market perception, $’s+ profit • Assets: IP, Advanced design, External Industry Leadership Source: Ikhlaq Sidhu, UC Berkeley

Process Innovation Disruptive Innovation Continuous Innovation Lean Is the Process for Horizon 2 & 3 Innovation Horizon 1 Extend the core Horizon 2 Horizon 3 Speed & Urgency Lean Use Lean Methodologies for Horizon 2 and 3 projects Steve Blank

Lean Innovation Delivers Products and Services that users want and need in a fraction of the time

The Lean Methodology

Lean = 3 parts Business Model Canvas Part 1 Customers Channels Customer Relationships Revenue Model Value Proposition Activities Resources Partners Costs Source: Alexander Osterwalder- Business Model Generation

Business Model Canvas = hypotheses of how you create and deliver value for the company and its customers Part 1 Customers Channels Customer Relationships Revenue Model Value Proposition Activities Resources Partners Costs Source: Alexander Osterwalder- Business Model Generation

1. Frame Hypotheses • Frame Hypotheses 

1. Frame Hypotheses • Frame Hypotheses Business Model Canvas

Business Model Canvas Source: Alexander Osterwalder- Business Model Generation

2. Test Hypotheses • Frame Hypotheses • Test Hypotheses Business Model Customer Development   Customer Development is how you search for the model

Customer Development Turning the Business Model Canvas Into Facts

9 Guesses Guess Guess Guess Guess Guess Guess Guess GuessGuess Customers Channel Customer Relationships Revenue Model Source: Alexander Osterwalder- Business Model Generation

Customer Development is Hypothesis Testing

3. Build Incrementally & Iteratively • Frame Hypotheses • Test Hypotheses • Build the product incrementally & Iteratively Business Model Customer Development Agile Engineering   

The Minimum Viable Product (MVP) • Smallest feature set that gets you the most … - learning, feedback, failure, orders, … - incremental and iterative • It is not a prototype • It is not a deployable version with the fewest features • It is what enables a test of a hypothesis • It may be a drawing, a slide, a wireframe, clickable workflow, etc…

The Pivot • Definition: A substantive change to one or more of the business model canvas components • Iteration without crisis • Fast, agile and opportunistic • Weeks and $100K

Pivot Cycle Time Matters • Speed of cycle minimizes cash needs • Minimum feature set speeds up cycle time • Near instantaneous customer feedback drives feature set Customer Discovery Customer Validation Company Building Customer Creation ExecutionSearch Pivot

Part 1 Agile Engineering + Part 2 Part 3 Elements of Lean Startup

Lean Gets Theory Customer Development 2003 Blank Agile Engineering 2011 Ries Business Model Canvas 2010 Osterwalder HBR Cover 2013

Lean Gets Practice MS&E 297: “Hacking for Defense”: Solving National Security issues with the Lean Launchpad In a crisis, national security initiatives move at the speed of a startup yet in peacetime they default to decades-long acquisition and procurement cycles. Startups operate with continual speed and urgency 24/7. Over the last few years they’ve learned how to be not only fast, but extremely efficient with resources and time using lean startup methodologies. In this class student teams will take actual national security problems and learn how to apply “Lean Startup” principles, ("business model canvas," "customer development," and "agile engineering”) to discover and validate customer needs and to continually build iterative prototypes to test whether they understood the problem and solution. Teams take a hands-on approach requiring close engagement with actual military, Department of Defense and other government agency end-users. Team applications required in February. Limited enrollment. Course builds on concepts introduced in MS&E 477. Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP) Instructors: Blank, S. (PI) ; Byers, T. (PI) ; Felter, J. (PI) 2015-2016 Spring • MS&E 297 | 4 units | Class # 47395 | Section 01 | Grading: Letter (ABCD/NP) | LEC • 03/28/2016 - 06/01/2016 - with Blank, S. (PI); Byers, T. (PI); Felter, J. (PI) Lean LaunchPad For Students 2011 1250+ teams Taught in 75 Universities 760+ teams Taught by 50 Universities I-Corps For SBIR/STTR 2012 I-Corps For Life Sciences 2014 I-Corps For NSA 2015 ~250,000 on-line students Udacity.com

How Does This Really Work?

Example 1: Stanford Team

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

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

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

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

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 Enterpris e Week 1-3

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

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

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

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 ✘

What about the consumer?

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

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

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

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

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

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

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

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

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

“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

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

And heard from companies that Amazon data is big pain point Enterprise Week 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 Enterprise Week 5-6

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 5-6blue = 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 Enterprise Week 5-6

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

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

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

As a result: 3-sided market Consumer 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 Segment Consumer: • Millennials • People who donate to charity Segment Consumer Week 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 Consumer Week 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 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

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.

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

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

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

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

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 Enterprise Week 4

...for 3 generic enterprise segments Enterprise Week 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?

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

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

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

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

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?

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

...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

Other learnings

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

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

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

Example 2: National Science Foundation Team

Team 198 ©Technology Review An energy efficient, temperature sensitive switchable window coating that blocks or transmits heat TAM $172 Billion Total Customer Interviews – 105 Tech Video Lessons Learned Video http://youtu.be/RIR2SiQd1pk http://youtu.be/8q8l5i3ISeU

Sarbajit Banerjee – Principal Investigator • Associate Professor in Chemistry at University at Buffalo Brian Schultz – Entrepreneurial Lead • 2013 Ph. D. Candidate in Chemistry at the University at Buffalo • Panasci Technology Entrepreneurship Competition Winner 2013 Martin Casstevens – Mentor • Director of Directed Energy at the University at Buffalo • Business Formation and Commercialization Manager – STOR Team 198

Version 1 Window OEMs Glass OEMs Architectural Firms Architectural Paint OEM Fortune 50 Chip Manufacturer UB STOR – Incubator, IP, Networking & Mentoring NYSERDA - Directed Energy IP Assignment R & D / Engineering Strategic Partnering End User Behavior LEED & Energy-STAR Cert. Increase Energy Savings for End Users Better Daylighting LEED Points Durability Ease of Use & Integration Higher Profit Margins Faster Memory & Computer PerformanceIP, Patents & Trade Secrets Personnel Nondilutive Support Strong Visibility (MIT TR35) UB & STOR Support Raw Material Suppliers Specially Engineered Equip. OEM Engineering Support LEED & Energy-STAR Cert. Tradeshows Prototyping and Demos Windows Residential Commercial Auto Interior Glass Architectural Paints Interior Cool Roofing Electronics Cell Phones Computers Tablets Flash Drives OEM Distribution Chains Contractors Architects Building Managers Home owners Retrofitting Renovations B2B Strategic Partnering with OEM Personnel Equipment, Tools, Raw Materials, Supplies, & Lab Space Research & Development Standardized Ratings Proprietary Material Sales IP Licensing Engineering Services Team 198

Market Size Total Addressable Market – $172 Billion • Total window and door sales worldwide Serviceable Available Market – $29.5 Billion • North American window and door sales Target Market – $6.6 Billion • Green Windows & Doors and Smart Glass Sales in North America Source - Custom Syracuse Report, Syracuse University New Technologies Law Center, 2013 Freedonia Research Report & bcc Research and Forecasting, 2010 Team 198 TAM $172 Billion

First Hypotheses Value Proposition – Energy Savings • End users will pay for energy savings? • Interview end users Customer Segments • Will OEMs partner with a startup on new products? • Interview OEMs and review past behavior Channels • Are there any choke points between the OEM and end user? • Investigate channels, i.e. architects, integrators, distributors Revenue Model • What premium will end user improved performance? • Customer interviews Team 198 Home Depot

Ecosystem – Version 1 SOLARMINDER Materials Eng. support Glass Manufacturer Integrator Window Brands - Retail outlets - Homebuilders - General contractors HOMEOWNERS - Const. Engineers - Architects BUILDING OWNERS Residential Commercial SOLARMINDER is a startup that seeks to license/partner with window manufacturers to maximize (1) market penetration and (2) profit margins Team 198

Version 2 - Expanded Team 198

Customer Discovery Team 198 LOWES BLAINE Window Repair Thompson Creek Windows Banner Glass Inc.

Customer Discovery Team 198 Ryan McPhearson – Chief Sustainability Officer Albert Gilewicz – Associate Director Utilities Operations Ann Brozek – Sustainability Architect Martha Bohm - Architect Jennifer – Architect Ray McGowan – Senior Program Manager NFRC David Macleod – Principal at Cannon Design Ron Foley – Head Engineer MaXPro Window Films Joseph Murray – Ace Energy Joanne – Sales at Old Castle Building Envelope Bob – Artic Window Tinting Woody Maggard – Former Ind. Developer

Archetypes Team 198 Customer OEM Archetypes National – PPG, Guardian, ASG, MaXPro Window Films International – CSR Australia, NSG (Pilkington) Regional – Thompson Creek, Comfort Windows & Doors Influencer Archetypes Sustainability driven architects Energy Consultants Enduser Archetypes High-end commercial buildings often public Commercial rehab and retrofits

Ecosystem – Version 3 SOLARMINDER Materials Eng. support Window Manufacturer Window Film Manufacturer Glass Manufacturer Retail outlets Wholesale Distribution Homebuilders General contractors Const. Engineers Energy Consultants Architects INTEGRATORS Residential BRANDS Sales Reps HOMEOWNERS BUILDING OWNERS INFLUENCERS CHANNELS NFRC Ratings Self Rate CommercialDoE Energy-STAR CUSTOMERS END USERS 3rd Party Testing Team 198

Advanced Energy Conference Team 198 Jacobs Javits Center Exterior Curtain Wall Advanced Energy Conference 2013 - NYC

Revenue Streams SolarMINDER Window Manufacturer ($65-75 per sq meter) Window Film Manufacturer ($45-50 per sq meter) Glass Manufacturer ($45-55 per sq meter) Customer Segments Sell Products Activities Eng. Services $20-30 per sq meter $150-200 per hour Payments Architects (5-8%) MEP Engineers Energy Modeling Residential BRANDS Sales Reps % commission HOME OWNERS BUILDING OWNERS Commercial Glazing Int. ($70-85) Architects (5-8%) Energy Cons. (Contract) Home Builders Developers Contractors (Bid??) Architects (5-8%) Retail Outlets (2-5%) Wholesale Distribution Contractors (Bid??) Window Installers Window Dist. ($65-75) Window Dist. ($70-75)

Strategic Partners • National – PPG, Guardian, ASG, MaXPro Window Films • International – CSR Australia, NSG (Pilkington) • Regional – Thompson Creek, Comfort Windows & Doors SolarMINDER Sell Products Eng. Services Window Manufacturer Window Film Manufacturer Glass Manufacturer Customer Segments Potential Key Partners Team 198

Competitive Threats • Manufacturers of Low-E • “Flip-of-a-switch” (electrochromics) • Metallic Low-E Plus • Transitions (photochromics) • Lowering Energy Costs Team 198 electrochromics

Next Steps • Continue Strong Visibility • Funding: SBIR, Angels, Venture Capital, NYSERDA • Demo Projects: 3 sites identified • Pilot testing with window film OEM • Direct Engagement with OEMs • Explore window curtain integrator in ecosystem • NFRC accredited testing Team 198

Team 198 ©Technology Review GO Tech Video Lessons Learned Video http://youtu.be/RIR2SiQd1pk http://youtu.be/8q8l5i3ISeU

Disruptive Innovation Continuous Innovation Lean Means Getting Out of Your Office Horizon 2 Horizon 3 Speed & Urgency Lean Steve Blank • If you’re not talking to 100’s of customers, it’s not lean • If you’re not building iterative and incremental minimum viable products, it’s not lean

Managing Three Horizons of Innovation - Current Existing Business Model: Process Innovation Execute New/Disruptive Business Model Search New Opportunities via Business Model Innovation Execute/Search Known Unknown Partially known Lean Innovation Mgmt Process Mgmt

Managing Three Horizons of Innovation - Goal Existing Business Model: Continuous Innovation Execute New/Disruptive Business Model Search New Opportunities via Business Model Innovation Execute/Search Known Unknown Partially known Lean Innovation Mgmt

Innovation Metrics

NASA

NASA/DOD Technology Readiness Level (TRL) • Formal Way to assess project maturity • Quantify Relative Risks • Data Driven • Adopted by NASA, DOD, FAA, ESA…

NASA/DOD Technology Readiness: Levels 1 & 2 Basic Technology Research • Basic principles observed • Technology concept formulated Concept

NASA/DOD Technology Readiness Levels 3 & 4 Research to prove Feasibility • Experimental proof of concept • Breadboard validation in lab Research Concept

NASA/DOD Technology Readiness Levels 5 & 6 Demo Prototype • Breadboard validation outside the building • System demo in real-world Research Concept Demo

NASA/DOD Technology Readiness Levels 7, 8, 9 Deployment • System Development • System deployed in real-world Research Concept Demo

What Can We Do With Customer Discovery Data? The Investment Readiness Level

Investment Readiness Level We can do the same for new ventures

Investment Readiness Level We can do the same for new ventures Emphasis is on data

Investment Readiness Level • A Formal Way to Quantify Relative Risks • Data Driven • Analog to NASA/DOD Technology Readiness Level (TRL) • Use IRL as a way to establish immediate funding increments

Investment Readiness: Levels 1 & 2 Hypotheses • Value Proposition summarized • Canvas hypotheses articulated Hypotheses

Investment Readiness: Levels 3 & 4 Problem / Solution Fit • Problem Solution fit • Low fidelity MVP Hypotheses Problem/Solution

Investment Readiness: Levels 5 & 6 Validate • Product/Market fit • Right side of canvas Hypotheses Problem/Solution Product/Market fit

Investment Readiness: Levels 7 & 8 Validate • Left side of canvas Hypotheses Problem/Solution Product/Market fit

Investment Readiness: Levels 9 Metrics That Matter Hypotheses Problem/Solution Product/Market fit Left side of the canvas

Technology Readiness Level Problem/Solution Hypotheses Product/Market Fit Validate Right side of Canvas Validate Left side of Canvas Metrics that Matter Investment Readiness Level

Horizon 1 Procedures Meets a Horizon 3 Project Steve Blank

Horizon 3 Project

Horizon 1 Management

The Problem

Startups/New Corporate Initiatives Start as Innovation Engines New/Disruptive Innovation • Disruptive • Business Model Innovation • Better/faster/cheaper • Innovation requires no restrictions by plans, procedures or processes • Success = finding a repeatable and scalable business model • Grows and scales Steve Blank

Horizon 3 Horizon 3 Needs To Leave Home Process Innovation Continuous Innovation Disruptive Innovation • Physically separate from operating divisions • Company Incubator, etc • Their own plans, procedures, policies, incentives and KPI’s • They operate with speed and urgency • Goal is to find a repeatable and scalable mission model Steve Blank

Success Creates “Debt” Success creates • Technical debt • Organizational debt • Refactoring “cleans up” debt by restructuring it Refactoring Steve Blank New/Disruptive Innovation

Type of Innovation Innovation Becomes Execution Process Execution Disruptive Innovation • Success means scale • Scale requires plans, procedures, processes, incentives, KPI’s • Innovation becomes execution Refactoring Group Steve Blank Continuous Innovation

Horizon 3 Refactoring is an Integral Part of Innovation Process Innovation Disruptive Innovation • Horizon 3 takes shortcuts • Technical shortcuts add up and become what is called Technical debt • People/process shortcuts are Organizational debt • Refactoring “cleans up” debt by restructuring it • You need a process organization dedicated to refactoring Horizon 3 projects Refactoring Group Steve Blank Horizon 1

Type of Innovation Innovators Leave or Start New Initiatives Process Execution Disruptive Innovation • Founders/early employees don’t fit in execution organizations • Short-sighted companies: innovators leave • Far-sighted companies: they start the next cycle of innovation Refactoring Group Steve Blank Continuous Innovation Disruptive Innovation

“Get to Yes” Corporate support of Innovation in All 3 Horizons Process Innovation Refactoring Group Company support orgs Steve Blank • Task Support Organizations to work inside Horizon 2/3 • Assign Finance, Legal, HR, etc. • Job is helping all Horizon projects “get to yes” • leverage existing assets and capabilities is critical Disruptive Innovation

Company Incentives & Goals In support of Innovation in All 3 Horizons Disruptive Innovation Steve Blank • Companies operate on goals and incentives • Incent mavericks, incent support, incent adoption Process Innovation Refactoring Group Company support orgs

Company Incentives & Goals In support of Innovation in All 3 Horizons Disruptive Innovation Steve Blank • Company operates on goals and incentives • Incent mavericks, incent support, incent adoption If there are no Horizon 2/3 incentives in the company then there is no real commitment to innovation Process Innovation Refactoring Group Company support orgs

Company Incentives & Goals In support of Innovation in All 3 Horizons Disruptive Innovation Steve Blank • Company operates on goals and incentives • Incent mavericks, incent support, incent adoption If supporting Horizon 2/3 is not part of Company goals & incentives then there is no real commitment to innovation Process Innovation Refactoring Group Company support orgs Positive – Financial Awards, Performance Bonuses, & Honorary Awards Negative – You can lose product funding

Type of Innovation Innovation Becomes Execution Horizon 1 Adopts Horizon 2 & 3 Process Execution Steve Blank Horizon 3 support orgs Refactoring Group Continuous Innovation • Success means scale • Scale requires plans, KPI’s procedures, processes, incentives • Innovation becomes execution Disruptive Innovation Horizon 2 Horizon 3 Horizon 1

Intrapreneurs are (Good) Rebels Bad Rebels Anger Pessimist Energy-sapping Alienate Problems Vocalize Problems Worry That Point Fingers Doubt Social Loner Assertions Me-focused Break Rules Complain Good Rebels Passion Optimist Energy-generating Attract Possibilities Socialize Opportunities Wonder if Pinpoint Causes Believe Social Questions Mission-focused Change Rules Create Source: Carmen Medina www.rebelsatwork.com

Horizon 3 Protects Mavericks Horizon 1 Fires Mavericks • In Horizon 1 – Pains in the butt – Always looking at something different – Doesn’t get with the program • In Horizon 3 – The head of your innovation project – Invents your next capability

Why Innovation Fails

Shiny Objects • Tech founder becomes enamored with new tech (shiny object) • Company still dependent on Horizon 1 until new tech is adopted Solution: • Make sure that $’s, people, and infrastructure are in place to cross the Tech Transfer “Valley of Death”

Leadership is Focused on Now • Leadership managing for current business & quarterly earnings • CEO and/or mgmt incentives all on current mission and goals Solution: • Align incentives • Appoint a Corporate Chief Innovation Officer

Innovation Is a Buzzword • Stop using it to describe everything Solution: • Use the Horizon 1, 2 & 3 metaphor

Failure is Career Retarding • In a company a failed project is to be avoided at all costs • In a Lean organization failure is part of the process • Pivoting from a failure gets us learning

Bottleneck: The Intransigent Middle Turning Go into No • Top of the organization says, “Do it” • Bottom of the organization (innovators) ready to go • Middle management kills it – Actively – Sabotage – Benign Neglect • Innovation programs die Steve Blank Innovation Groups Ready Middle Mgmt Barrier Executive Buy-In GO NO

Why the Bottleneck? • Threat – Power, ownership, turf, prestige, pay • Confused – Job spec’s are still the same – No training on how to support, participate • No incentives to change behavior • No penalty for ignoring it Steve Blank

Sales Freezes Talking to Customers • Sales says “no one can talk to our customers” Solution: • Customer Discovery is not pitching new products Steve Blank

Engineering Is Not Talking to Customers • Engineering believes innovation is about technology Solution: • Focus the organization on understanding customer problems • Focus on solving current or future problems Steve Blank

Towards New Horizons Rethinking the Enterprise

Towards New Horizons Rethinking the Enterprise take best practices from startups and apply it to the corporation

Hor Known Business Model The Limits of Current Horizons Evangelos Simoudis/Steve Blank Horizon 1

The Limits of Current Horizons 189 Develop- ment Research Evangelos Simoudis/Steve Blank Horizon 1

Develop- ment Research Business Units Horizon 1 The Limits of Current Horizons Evangelos Simoudis/Steve Blank

Extend the Business Model The Limits of Current Horizons Evangelos Simoudis/Steve Blank Horizon 2

The Limits of Current Horizons Develop- ment Research Business Units Customers Customers Customers Evangelos Simoudis/Steve Blank Horizon 2

The Limits of Current Horizons Develop- ment Research Business Units Customers Customers Customers Evangelos Simoudis/Steve Blank • 90% of R&D dollars support existing products • Research = adv development to support existing products Horizon 1 & 2

Unknown Business Model The Limits of Current Horizons Evangelos Simoudis/Steve Blank Horizon 3

The Limits of Current Horizons Develop- ment Research Business Units Customers Customers Customers Evangelos Simoudis/Steve Blank Horizon 3

Copyright 2016 Evangelos Simoudis Research Development Business Units In most companies, Horizon 3 research $’s are eliminated or outsourced e.g., university funding, government labs consortia Today R&D’s mission Has Changed Horizon 3

Develop- ment Research Business Units Customers Customers Customers Innovation Outpost(s) and Inpost(s) Solution = Innovation Outposts/Inposts Evangelos Simoudis/Steve Blank

Innovation Outposts Bus Dev Strategy & Corp Dev Corp VC Ecosystem Specific R&D Corp Incubators Steve Blank/Evangelous Simoudis Innovation Outpost • Standalone unit for Horizon 2 and 3 innovation • May contain as needed: • Corp VC • Incubator • Specific R&D • Bus Development

Innovation Outposts Bus Dev Strategy & Corp Dev Corp VC Ecosystem Specific R&D Corp Incubators Business Units Business Units Business Units Technology innovations Business problems & context Steve Blank/Evangelous Simoudis Innovation Outpost Business model & Technology Innovations Spin ins New Business Unit Startups Startups Startups

Innovation Outposts Bus Dev Strategy & Corp Dev Corp VC Ecosystem Specific R&D Corp Incubators Business Units Business Units Business Units Technology innovations Business problems & context Steve Blank/Evangelous Simoudis Innovation Outpost Spin ins New Business Unit • Outposts operate under many degrees of freedom • e.g., investments, incubation • Launches many experiments (investments, incubated teams) inexpensively to test out innovation-related hypotheses

Innovation Outposts – Moonshot Support Bus Dev Strategy & Corp Dev Corp VC Ecosystem Specific R&D Corp Incubators Business Units Business Units Business Units Technology innovations Business problems & context Steve Blank/Evangelous Simoudis Innovation Outpost Business model & Technology Innovations Spin ins New Business Unit • Moonshot = large commitment of resources for a Horizon 3 goal • Requires H1 & H3 collaboration

New Unit New Unit As new business units created by the Innovation Outpost grow, they hire employees with different culture than that of the H1 corporate parent H1 Corporation Existing BU Existing BU Outposts Change the Culture New Employees Evangelos Simoudis/Steve Blank

New Unit New Unit Existing BU H1 Corporation Existing BU 2) augmenting the H1 corporation through their presence. Outposts Change the Culture Startups Evangelos Simoudis/Steve Blank

Summary • Lean Innovation Management is not about efficiency and innovation • It’s about developing the capabilities necessary to offset competitors who may have equal or better technologies • It’s how to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments • It’s about survival in the 21st Century

Thanks! sblank@kandsranch.com

Removing the Bottlenecks • Prove that this can work • Then: Communicate, communicate, communicate – Big idea – shared goal/mission – Strategy – big picture of how the pieces work together – Tactical implementation • Update job specs to include innovation support • Change incentives to include innovation support • Shower those who came before with appreciation • Support those who try and fail and try again Steve Blank

How to Start an Innovation Engine- 0 • Reorganize around Mission + Innovation • Each Horizon 1 division needs a Chief Innovation Officer • Drives Continuous Innovation • Finds Horizon 2 opportunities • Starts and Funds 10x the new initiatives for MVP’s • Company needs a COO of Innovation • Runs/funds Horizon 3 incubators with I-Corps methodology • Runs open innovation incubators • Provides staff and infrastructure support for Divisional Innovation Steve Blank

How to Start an Innovation Engine- 1 • Adopt Common Language: Horizons, Lean, Pivots, MVPs, etc. • Identify Lean Innovation Vehicles • R&D, Engineering, Incubators, Accelerators, etc. • Adopt Lean Product Development: Digital Services Playbook.. • Adopt Lean Metrics: Hypotheses tested, Pivots, IRL, TRL, … • Adopt Lean Funding: TRLs & IRL • Adopt Lean Pedagogy: Lean LaunchPad/I-Corps • Use Lean Mgmt processes – Agree how to “Hand-off” and “scale” small efforts (hard) – Develop organizational processes/procedures/incentives that support innovation (hard) Steve Blank

Start an Innovation Engine - 2 • Educate the company on Innovation – Communicate goals – Communicate process (hard) • Everyone expects detailed specs like Horizon 1 - bad – Consolidate innovation efforts (hard) – Recruit teams (3-4 people) – Recruit mentors - one per team (hard) – Get divisional cooperation (hard) – Train the Trainers Steve Blank

Start an Innovation Engine- 3 • Design Programs – Emphasis on speed, urgency, evidence, pivots – 1½ day “Train-the-Trainers” – 6/8-week “I-Corps” programs – Investments and adoption of H1 and H2 by divisions • Run Programs Steve Blank

Start an Innovation Engine - 4 • Rally around a mission not theory • Pick something everyone agrees is a good goal and congruent with the company’s mission • Legitimatize the need for exploration and exploitation Steve Blank

Start an Innovation Engine -5 • Leadership that is capable of managing the issues associated with multiple simultaneous Horizons – Resource allocation – Incentives – Etc. • Needs to balance a culture of risk taking, speed = mitigation, quick to opportunities, receptive to innovation Steve Blank

Thanks! sblank@kandsranch.com

Innovation at 50x Steve Blank @sgblank www.steveblank.com 2/1/16