--- title: "ENTR 242 ยท Start your own business" publisher: Maestro College (Maestro Open) lessons: 43 source: https://open.maestrocollege.edu/start-your-own-business full: https://open.maestrocollege.edu/start-your-own-business/llms-full.txt --- # Start your own business (ENTR 242) Find a problem worth solving, test your riskiest assumptions with experiments and MVPs, and plan how to reach your market. Startup Development & Lean Methodology focuses on entrepreneurship principles, Lean Startup model implementation, idea validation, and rapid product prototyping techniques. ## Course objectives - Apply Lean Startup principles to frame entrepreneurial opportunities as testable assumptions and evaluate them using customer evidence. - Develop evidence-informed business models that align customer needs, value propositions, revenue approaches, and operating costs. - Design and assess minimum viable product experiments to validate key assumptions and guide iteration or strategic pivots. - Analyze market attractiveness, positioning, customer acquisition approaches, pricing, and unit economics to develop a go-to-market strategy. - Develop an evidence-led next-stage startup plan addressing financing, legal considerations, launch milestones, operations, and investor communication. ## Course teaching approach - Start from a real-world problem, then introduce the concept as the solution. - Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning. - Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks. - End with one fun closed question about the topic. ## Lessons ### Unit 1: Finding problems worth solving #### 1. Problems first, solutions later - Distinguish a solution-first idea from a problem-first opportunity, where a specific customer has an important, frequent or costly problem that current options serve poorly. - Identify the problem hypothesis and customer hypothesis behind a real startup idea, naming who has the problem, what it is and how often or costly it is. Teaching notes: Contrast Airbnb's founders renting air mattresses to conference visitors when hotels were full with Google Glass as a technology looking for a problem. #### 2. Jobs-to-be-done and current workarounds - Understand a job-to-be-done as the progress a customer is trying to make in a specific situation, and identify a customer's job-to-be-done and the current workaround they use to get it done, including doing nothing. - Distinguish a must-solve pain from a nice-to-have annoyance by judging how frequent, costly and urgent the problem is. Teaching notes: Use Dropbox, whose founder kept forgetting his USB drive while people emailed files to themselves, and Calendly, whose users booked meetings through long email back-and-forth. #### 3. Drafting testable problem hypotheses - Create a set of problem and customer hypotheses for a startup idea, each linked to a job-to-be-done and a current workaround. - Fix a vague hypothesis into a testable one that names who will do what, measured how, with a pass bar set before any evidence is gathered. Teaching notes: Scenario: SwitchPrep, a gen-AI interview-prep coach idea for adults switching careers; so far it is only the founder's idea with no customer evidence. Learners draft its hypotheses with pass bars. #### 4. Asking non-leading interview questions - Distinguish behavior-based evidence (past actions, money or time spent) from opinions, compliments and promises about the future. - Fix leading, hypothetical or pitch-style interview questions by rewriting them as questions about specific past behavior. Teaching notes: Use the interview rules from Rob Fitzpatrick's The Mom Test, with examples of founders misled by friendly feedback from people they knew. #### 5. Writing a discovery interview guide - Create a discovery interview guide that opens with context, asks for the story of the last time the problem happened, and probes the workaround and its cost. - Choose whom to interview and how to reach them, based on the customer hypothesis. Teaching notes: Scenario: SwitchPrep, a gen-AI interview-prep coach idea for adults switching careers; the founder's hypothesis is that career switchers freeze on behavioral questions and lack affordable practice. Learners write the interview guide that tests it. #### 6. Running a discovery interview - Conduct a discovery interview that follows up on vague answers and avoids pitching the solution. - Record interview notes that separate facts, quotes and behaviors from opinions, and capture commitment signals, where the interviewee gives up time, money or reputation or agrees to a concrete next step. Teaching notes: Scenario: SwitchPrep, a gen-AI interview-prep coach idea for adults switching careers. The tutor role-plays three short interviews with switchers: one enthusiastic who has never practiced, one who paid a career coach, and one who practices with free AI chatbots. #### 7. Running AI-assisted desk research - Use an AI assistant to gather desk research on competitors, market signals and customer complaints, and verify each claim against a named source. - Distinguish what desk research can show from what only customer interviews can reveal, and label each finding as supporting or weakening a hypothesis. Teaching notes: Scenario: SwitchPrep, a gen-AI interview-prep coach idea for adults switching careers, with three discovery interviews done. Learners use a free AI assistant with web search (such as ChatGPT, Gemini or Perplexity) to research tools like Google Interview Warmup, Yoodli and Big Interview and their user reviews. #### 8. Affinity mapping discovery evidence - Create an affinity map that groups interview notes and desk-research findings into themes. - Identify which themes are backed by behavior from several customers and which rest on one person's opinion. Teaching notes: Scenario: SwitchPrep, a gen-AI interview-prep coach idea for adults switching careers. The tutor supplies about 20 short notes from five interviews and desk research, mixing past behaviors, complaints, compliments and wishes. #### 9. Calling an opportunity verdict - Compare discovery evidence with the hypotheses' pass bars and issue a go, no-go or keep-exploring verdict. - Support the verdict with at least three pieces of behavior-based evidence and name the biggest open question for the next stage. Teaching notes: Scenario: SwitchPrep, a gen-AI interview-prep coach idea for adults switching careers. The tutor gives an affinity map showing most switchers cram alone the night before, few have paid for coaching, and several already use free AI chatbots. ### Unit 2: Business models and riskiest assumptions #### 10. Reading a Business Model Canvas - Understand the nine building blocks of the Business Model Canvas (what each one describes) and identify them in a real startup's business model. - Understand a business model as a connected system of how a startup creates, delivers and captures value, and trace how changing one block ripples into the others. Teaching notes: Use Airbnb's business model, with hosts and guests as two linked segments and service fees charged to both. Create an illustration that lays out the nine Business Model Canvas blocks filled in for Airbnb. #### 11. Finding early adopters in a segment - Distinguish a broad market from a narrow, reachable segment, and identify the early adopters in it who feel the problem most and already hack together a solution. - Fix a vague customer segment, such as 'busy people' or 'small businesses', by narrowing it to a specific group a founder can name and reach. Teaching notes: Use Facebook's launch to Harvard students only, followed by a college-by-college rollout. #### 12. Mapping fit with a Value Proposition Canvas - Create a customer profile of jobs, pains and gains from discovery evidence on an early adopter. - Create a value map of pain relievers and gain creators, and judge its fit against the customer's current alternative, including doing nothing. Teaching notes: Scenario: FixFlow, an app that lets small landlords (1-10 rental units) receive, track and assign tenant maintenance requests; discovery found the early adopters are self-managing landlords with 3-10 units who use texts, spreadsheets and a phone list of contractors. The tutor role-plays one such landlord for a short interview and supplies discovery notes, then learners build the Value Proposition Canvas. #### 13. Revenue streams and cost structure - Identify common startup revenue streams (subscription, transaction fee, freemium, advertising) and match them to what a segment can and will pay. - Distinguish fixed from variable costs in a startup's cost structure and judge whether revenue per customer could cover the cost to serve. Teaching notes: Use Dropbox's freemium model and Duolingo's mix of free ad-supported learning and a paid subscription. #### 14. Drafting a Lean Canvas - Understand the Lean Canvas boxes that replace Business Model Canvas blocks (problem, solution, key metrics, unfair advantage) and distinguish when an early startup should use each canvas. - Create a Lean Canvas that fills all nine boxes (problem, customer segments, unique value proposition, solution, channels, revenue streams, cost structure, key metrics, unfair advantage) and marks every box as evidence-backed or assumed. Teaching notes: Scenario: FixFlow, an app that lets small landlords receive, track and assign tenant maintenance requests, aimed at self-managing landlords with 3-10 units who juggle texts, spreadsheets and contractor calls. Its Value Proposition Canvas shows fit on never losing a request, and it plans a $5 per unit per month subscription; learners pull this into its first Lean Canvas. #### 15. Desirability, feasibility, and viability risks - Understand desirability (do customers want it), feasibility (can we build and deliver it) and viability (can it make money) risks, and distinguish them in a startup's assumptions. - Use the Quibi, Theranos and MoviePass cases to identify which type of risk sank each startup and what early evidence could have revealed it. #### 16. Ranking risks on an assumption map - Identify at least six assumptions across a business model and label each as a desirability, feasibility or viability risk. - Rank the assumptions on an assumption map by importance and existing evidence, and choose the riskiest one to test first. Teaching notes: Scenario: FixFlow, an app that lets small landlords (1-10 rental units) receive, track and assign tenant maintenance requests, aimed at self-managing landlords with 3-10 units and a planned $5 per unit per month subscription. Learners pull assumptions from its Lean Canvas and Value Proposition Canvas onto the map; create an illustration that shows the assumption-map grid of importance versus evidence. #### 17. Writing falsifiable hypotheses for top risks - Understand a value hypothesis (whether customers get enough value to keep using or paying) and a growth hypothesis (how new customers will find and adopt the product), and distinguish them in a startup's assumptions. - Create falsifiable hypotheses for the top three assumptions, each with a metric, a pass bar and a desirability, feasibility or viability label. Teaching notes: Scenario: FixFlow, an app that lets small landlords (1-10 rental units) receive, track and assign tenant maintenance requests. Its assumption map ranks three top risks: landlords will stop texting, tenants will submit requests in the app, and landlords will pay $5 per unit per month. #### 18. Challenge: Lean Canvas and risk map - Judge the discovery evidence and correct and complete the Lean Canvas so it reflects only what the evidence supports. - Create an assumption map for the startup and turn its riskiest assumption into a falsifiable hypothesis with a pass bar. Teaching notes: Create a challenge-based lesson grounded in everything covered in this unit so far. Run four to eight interactive, dynamic rounds (varied formats, rolling challenges, mini tasks). Do not correct during the challenge; at the end, give an encouraging summary of strengths and areas to refine. ### Unit 3: Experiments, MVPs, and pivots #### 19. The build-measure-learn loop - Understand the build-measure-learn loop as a way to produce validated learning (evidence from real customer behavior that confirms or refutes a hypothesis), measured with actionable metrics tied to a hypothesis, such as pre-order rate, rather than vanity metrics, such as total page views, with each loop ending in an explicit, recorded decision about what to do next. - Use the IMVU case to identify where the team built before learning and what customer behavior finally showed. #### 20. Smoke-test MVPs - Understand an MVP as the cheapest experiment that produces credible evidence on one hypothesis, not a smaller version of the product, and a smoke test as an MVP that asks people to act on an offer before the product exists, where a click, email sign-up, pre-order or deposit gives progressively stronger evidence. - Use the Dropbox and Buffer cases to identify how a smoke test measured demand before the product existed. #### 21. Building a smoke-test landing page - Create a smoke-test landing page that states the value proposition and asks for one clear action. - Choose the call to action by evidence strength (click, email sign-up, pre-order or deposit) and set its conversion pass bar. Teaching notes: Scenario: ListingLift, a gen-AI tool that writes Etsy product titles, descriptions and tags in the seller's own voice, testing whether sellers will pay $12 a month. The tutor opens html-css-editor with a simple landing-page template (headline, value statement, one call-to-action button) that learners fill in for ListingLift, using AI help for the copy. #### 22. Concierge and Wizard of Oz MVPs - Distinguish a concierge MVP, where customers know a person delivers the service, from a Wizard of Oz MVP, where people secretly run what looks automated. - Use the Zappos and Rent the Runway cases to identify what each manual test proved before anything was automated. #### 23. Choosing the cheapest test - Choose the cheapest test type (smoke test, concierge or Wizard of Oz) for a hypothesis, weighing its cost against the strength of evidence it produces. - Create a test card that states the hypothesis, the chosen test, the metric, a pass bar and a sample size or timebox, all set before the test runs. Teaching notes: Scenario: ListingLift, a gen-AI tool that writes Etsy product titles, descriptions and tags in the seller's own voice; its landing page got 9% email sign-ups but only 3% pre-orders at $12 a month, short of its 5% pass bar, so demand is still unproven. Before deciding on demand, the founder wants to test whether AI listings sound like the seller, so learners choose a test for that risk and write its test card. #### 24. Prototyping with AI and no-code tools - Create a working prototype, in a free AI or no-code tool, of the single step tied to the riskiest hypothesis. - Decide which parts of the flow to automate and which to fake by hand, keeping the prototype focused on the riskiest hypothesis. Teaching notes: Scenario: ListingLift, a gen-AI Etsy listing writer (9% sign-ups but 3% pre-orders against a 5% pass bar, so demand is unproven) whose test card passes if at least 7 of 10 sellers publish an AI listing with only minor edits. Learners build the 'paste product notes, get a listing' step in a free tool such as Google AI Studio, a Gemini Gem or any free AI chat with a saved prompt, then paste a sample input and the prototype's output into chat for review. #### 25. Running a usability test and revising - Run a usability test with three users on a prototype, giving each a short, realistic task and asking them to think aloud (say what they are doing and thinking) without helping. - Prioritize findings by severity (how badly a problem blocks the task) and by what they show about the hypothesis the prototype tests, then make and document one revision to the prototype or its spec. Teaching notes: Scenario: ListingLift, a gen-AI tool that writes Etsy listings in the seller's own voice, testing whether sellers would publish its AI listings with only minor edits. The tutor role-plays three sellers (a jewelry maker, a vintage reseller and a print artist) who each do a single task on a described prototype the tutor supplies. #### 26. Persevere, iterate, or pivot - Understand persevering (keep the strategy and keep testing), iterating (adjust the MVP within the same strategy) and pivoting (change one core part of the business model while keeping what was learned), and identify common pivot types: customer segment (same product, new customer), zoom-in (one feature becomes the whole product), problem (same customer, new problem), channel (new way to reach customers) and revenue model (new way to charge). - Use the Instagram, Slack and YouTube cases to identify the evidence that triggered each pivot and which part of the business model it changed. #### 27. Recording validated learning and decisions - Create a learning card that records the hypothesis, the observed result, the learning and the decision, and judge whether the result can be trusted (sample size, traffic source, vanity vs actionable metric). - Create a decision memo that justifies persevering, iterating or pivoting, names the business-model block a pivot would change, and specifies the next change to the MVP with its retest pass bar. Teaching notes: Scenario: ListingLift, a gen-AI Etsy listing writer: 6 of about 200 visitors (3%, mostly from one Reddit post) pre-ordered at $12 a month against a 5% pass bar, while total page views hit 1,500. Testers loved the time saved but found listings generic, and vintage resellers used it most. #### 28. Challenge: experiment cycle - Choose the riskiest assumption in a new startup's model, write its test card and pick the cheapest MVP to test it. - Interpret simulated results against the pass bar and justify a persevere, iterate or pivot decision in a learning card. Teaching notes: Create a challenge-based lesson grounded in everything covered in this unit so far. Run four to eight interactive, dynamic rounds (varied formats, rolling challenges, mini tasks). Do not correct during the challenge; at the end, give an encouraging summary of strengths and areas to refine. ### Unit 4: Market attractiveness and go-to-market #### 29. Top-down vs bottom-up market sizing - Understand TAM, SAM and SOM as nested layers of total, serviceable and obtainable market, and distinguish top-down market sizing from bottom-up sizing built from countable customers, purchase frequency and price. - Use the Airbnb 2008 pitch deck case to identify the assumptions behind each market layer and how a bottom-up version would rebuild them. Teaching notes: Create an illustration that shows TAM, SAM and SOM as nested circles, with the Airbnb 2008 pitch deck figures beside each layer. #### 30. Building a bottom-up market estimate - Create a bottom-up TAM, SAM and SOM estimate with every assumption and its source written down. - Judge market attractiveness by weighing size, growth, reachability, pain severity and competition, not headline size alone. Teaching notes: Scenario: GearShare, a peer-to-peer app where people rent camping, climbing and paddling gear from owners nearby; discovery showed renters want gear for 2-4 trips a year rather than buying it. Learners size the market for a Denver launch with AI-assisted desk research. #### 31. What makes a good beachhead - Understand a beachhead market as the validated early-adopter segment that a startup sets out to win completely, then uses to reach neighboring segments. - Use the Amazon and Uber cases to compare candidate beachheads on pain, reachability, ability to pay and word of mouth, and identify how each startup expanded from its first segment. #### 32. Who you really compete with - Identify a product's full set of competitive alternatives, starting from the customer's current workaround and adding rival products and doing nothing. - Use the Slack and Zoom cases to identify how each framed its unique value against one specific alternative. #### 33. Choosing a beachhead and positioning - Choose a beachhead segment from several candidates and justify the choice with evidence. - Create a positioning statement that names the target segment, the competitive alternative, the unique value and the proof. Teaching notes: Scenario: GearShare, a peer-to-peer app where people rent camping, climbing and paddling gear from owners nearby, launching in Denver; candidate segments are newcomers to the city, college students, families trying camping and visiting tourists. Learners pick the beachhead and position against REI rentals, buying used gear and borrowing from friends. #### 34. Testing willingness to pay - Understand price as a testable hypothesis about willingness to pay, shown by behavior rather than stated opinion. - Distinguish pricing tests such as pre-orders, price-split landing pages and price surveys by evidence strength and cost. - Conduct a short past-spending interview to gather willingness-to-pay evidence without asking hypothetical 'would you pay' questions. Teaching notes: Use Superhuman, which charged $30 a month and onboarded users one by one, and Buffer's early pricing-page test. For the interview, the tutor role-plays a busy professional who already pays for productivity tools. #### 35. First channels and sales approach - Identify candidate acquisition channels for a beachhead and the cheapest test of each. - Use the Dropbox referral program and Airbnb Craigslist cases to identify how each startup found a channel that reached its early adopters. - Understand self-serve (customers sign up and buy on their own, suited to low prices), founder-led (the founder sells directly, suited to higher prices and early learning) and partner-led (another business sells or refers for you) sales, choose the one that fits a startup's price point and beachhead, and outline the steps from lead to paying customer. #### 36. Setting funnel targets for go-to-market tests - Identify the stages of the startup funnel (acquisition, activation, retention, referral, revenue) and set a target conversion rate for each. - Create a test card for one channel test that names the growth hypothesis it tests, with funnel-stage metrics and pass bars. Teaching notes: Scenario: GearShare, a peer-to-peer app where people rent camping, climbing and paddling gear from owners nearby, launching for newcomers to Denver and positioned against buying gear they will rarely use; it plans a 15% booking fee on rentals averaging $40. Learners plan one channel test in local outdoor Facebook groups or Reddit. #### 37. Checking unit economics with CAC and LTV - Understand CAC (acquisition spend divided by customers acquired) and LTV (revenue per customer per period, such as per year, times margin times expected customer lifetime in periods), and calculate both from channel spend, price, margin and retention figures. - Interpret channel and pricing test results against their pass bars and the LTV-to-CAC ratio (roughly 3:1 or better is healthy), and decide whether to persevere with, iterate or pivot the go-to-market approach, naming the lever to fix. Teaching notes: Open with MoviePass, whose $9.95-a-month movie-a-day plan, paying theaters full ticket price, made LTV negative. Then use GearShare, a Denver peer-to-peer gear-rental app: $28 CAC per booked renter, a $6 fee per booking with about 80% margin after payment and insurance costs, about 3 bookings a year for 2 years, and a split test where a 20% fee kept about 90% of bookings. #### 38. Challenge: go-to-market test plan - Choose a beachhead from candidate segments and write its positioning statement. - Design one pricing or channel test with funnel pass bars, then interpret the simulated results and a CAC/LTV check. - State the initial go-to-market approach (segment, message, channel, price, sales approach) and decide whether to persevere, iterate or pivot. Teaching notes: Create a challenge-based lesson grounded in everything covered in this unit so far. Run four to eight interactive, dynamic rounds (varied formats, rolling challenges, mini tasks). Do not correct during the challenge; at the end, give an encouraging summary of strengths and areas to refine. ### Unit 5: Next-stage startup plan #### 39. Financing options and runway - Distinguish four or five early-stage financing options (bootstrapping, friends and family, crowdfunding, angels or accelerators) by what each costs and what evidence each expects, including what giving up equity (ownership and dilution) means compared with taking on debt. - Calculate runway from cash and monthly burn, and size a funding ask that reaches the next evidence milestone rather than a full-scale launch, so money is committed only as fast as the evidence supports. Teaching notes: Open with Mailchimp, which bootstrapped for about 20 years before Intuit bought it for about $12 billion. Then use ListingLift, a gen-AI Etsy listing writer that pivoted to vintage resellers and raised its price from $12 to $15 a month: 40 paying users, 80% three-month retention, $35 CAC, an estimated $160 LTV and $8,000 of founder savings. #### 40. Legal structure and IP basics - Distinguish a sole proprietorship, an LLC and a C corporation, and choose the one that fits the funding plan. - Identify the protections and agreements that matter most at this stage (founder agreement, trademark, terms of service and privacy policy for user data), recognize when a patent would be needed, and understand that purely AI-generated content may not be copyrightable in the US and that the AI provider's terms of service set how it can be used. Teaching notes: Open with the Facebook co-founder equity dispute, where Eduardo Saverin's stake was diluted and lawsuits followed, as the case for a founder agreement. Then use ListingLift, a gen-AI listing writer for vintage resellers (40 paying users at $15 a month, up from $12 after its pivot, and $8,000 of founder savings) whose founder is weighing a small angel round. #### 41. Launch roadmap and evidence-led pitch - Create a 90-day launch roadmap whose milestones are each gated by an evidence pass bar, and include key operations (customer support, quality checks on AI output, and who does what) as milestones. - Create the outline of a short evidence-led startup pitch (problem, evidence, solution, market, business model, traction, the ask and use of funds, open risks) that cites a test result for each claim. Teaching notes: Scenario: ListingLift, a gen-AI Etsy listing writer that pivoted to vintage resellers and raised its price from $12 to $15 a month, has 40 paying users, 80% three-month retention, a $35 CAC through reseller YouTube channels, an estimated $160 LTV and $8,000 of founder savings. Learners build its 90-day roadmap toward 150 paying users. #### 42. Challenge: investor pitch and Q&A - Build a short evidence-led pitch from a next-stage plan outline, tracing each claim to a test result or marking it as an open risk. - Defend the plan against at least three investor challenge questions using evidence from discovery, experiments and go-to-market tests. Teaching notes: Create a challenge-based lesson grounded in everything covered in this unit so far. Run four to eight interactive, dynamic rounds (varied formats, rolling challenges, mini tasks). Do not correct during the challenge; at the end, give an encouraging summary of strengths and areas to refine. #### 43. Challenge: full venture review - Audit a startup's evidence from discovery to go-to-market, separating behavior-based evidence from opinions and identifying the riskiest open assumption. - Recommend whether to raise money, keep testing or pivot, and justify it with the evidence and unit economics. Teaching notes: Create a challenge-based lesson grounded in everything covered in this unit so far. Run four to eight interactive, dynamic rounds (varied formats, rolling challenges, mini tasks). Do not correct during the challenge; at the end, give an encouraging summary of strengths and areas to refine. ## How to use this document You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates. Start with lesson 1 unless the learner asks for a specific lesson. Keep track of which lesson you are on. ## When a lesson is done Teach one lesson at a time. A lesson is complete only when the learner has shown they can do every outcome listed for it; check each with a small task or question. When it is complete: 1. Say clearly: "Lesson N complete: ." (use the lesson's number and title). 2. Give a two-line recap of what they learned. 3. Name the next lesson, say "Lesson N+1 of 43: ", and ask if they're ready to start it. Start it only when they say yes. After the last lesson, congratulate them on finishing the course.