entrep-lab worked example · insight → impact venture

EXFrom insight to impact venture — a worked example

One social problem, taken end to end through the entrepreneurial process exactly as the EMP team project asks: framing the opportunity, isolating and testing the riskiest assumption, synthesising customer discovery, filling a Value Proposition Canvas, defining an MVP, building the Business Model Canvas, sizing the market, working the unit economics and runway, and estimating social impact (SROI) — ending in a two-minute pitch. Every framework below is filled in with concrete, illustrative numbers so you can see what a finished artifact looks like. Replace the numbers with your own validated evidence.

The venture: Aula Viva — a structured after-school numeracy programme, delivered by trained near-peer university tutors, for first-generation secondary students in under-resourced public schools in Madrid. The worked example follows the team through the process from a vague hunch ("kids are falling behind in maths") to a defensible, evidenced go decision.

Goal
Validate a desirable, feasible & viable impact venture
Venture
Aula Viva (numeracy tutoring)
Beneficiary
1st-gen secondary students
SDG
SDG 4 · Quality Education
Deliverable
Process portfolio + 2-min pitch
Sessions exercised
8–26

What this example exercises

It walks the full arc of the EMP program: opportunity framing (Sessions 8–10), hypothesis-driven testing and the Riskiest Assumption Test (Sessions 12–14), customer discovery and validation (Sessions 21–22), the value proposition (Session 20), the business model and market sizing (Sessions 19, 23, 29), financing and viability (Session 25), and impact (theory of change & SROI). Each step links back to the matching interactive demo so you can re-run the numbers yourself.

Jump to a step

1The problem & the opportunity

A problem worth solving is severe, widespread, and currently solved badly. We frame it, then score it on the desirability / feasibility / viability lens before committing a semester to it.

Problem statement (framed, not solution-first)

Problem

First-generation secondary students in under-resourced Madrid public schools fall behind in mathematics because they cannot access affordable, consistent out-of-school support — private tutoring costs €25–40/hour and families in the target neighbourhoods cannot pay it. The result is a widening attainment gap that compounds each year and narrows university options.

Note the framing rule from Sessions 9–10 (Are You Solving the Right Problems?): we describe a need, not a product. "An after-school app" would be a premature solution; "students cannot access affordable, consistent support" is a problem we can re-frame and explore.

Evidence the problem is real (desk research, pre-discovery)

Opportunity scoring (Session 8 lens)

We score the opportunity on five dimensions, 0–10, then read a weighted verdict. Weights emphasise problem severity and desirability, the two things hardest to fix later.

DimensionScore (0–10)WeightContribution
Problem severity (pain)90.302.70
Market size / reach70.151.05
Desirability (do they want it)80.252.00
Feasibility (can we build it)70.151.05
Viability (sustainable motor)50.150.75
Weighted score1.007.55 / 10
Verdict: GO to validation (≥7.0). Viability is the weakest leg — flagged as the thing to prove.

2Riskiest assumption + validation experiment

Before building anything, test the one assumption that would kill the venture if it were false. Plot each assumption by importance (damage if wrong) × uncertainty (how little evidence we have); the top-right corner is the Riskiest Assumption Test — the RAT, your real MVP (Sessions 13–14).

AssumptionImportanceUncertaintyStatus
A1. Schools will let us run sessions on-site & refer students98← RAT
A2. University students will tutor reliably for a small stipend85queued
A3. A funder (foundation/CSR) will pay per student-place96queued
A4. 12 weeks of tutoring measurably lifts maths grades75later
A1 has the highest importance × uncertainty product — without school access there is no venture and no channel.

Riskiest assumption (A1)

"Public secondary schools in our district will grant on-site access and actively refer students to a free, external tutoring programme." If false, our entire distribution channel and beneficiary pipeline collapse — so we test this first, before recruiting tutors or designing curriculum.

The experiment (cheapest test that could disprove it)

ElementDesign
HypothesisIf we pitch a free numeracy pilot to school leadership, ≥30% will sign a letter of intent within 3 weeks.
TestCold-outreach + a one-page concept to all 14 district schools; book meetings; ask for a signed letter of intent (a costly signal, not a "nice idea").
Sample14 schools contacted, target ≥10 meetings.
Success metric≥ 4 signed letters of intent (≈30%) committing a room + a referral list for a 12-week pilot.
Kill metric≤1 letter of intent → channel is broken; pivot to community centres / NGOs instead of schools.
Cost / time~€0 cash, ~3 weeks of two founders' time.

Result (illustrative): 14 contacted → 9 meetings → 5 letters of intent (36%). RAT passed; A1 retired. The build-measure-learn loop then moves to A2 (tutor supply) and A3 (funding).

3Customer discovery → value proposition → MVP

With the channel proven, we ran customer discovery (Sessions 21–22), synthesised the insights, filled a Value Proposition Canvas (Session 20), and defined the smallest MVP that tests the value.

Customer discovery synthesis

We requested 60 interviews across three "customer" groups — students, parents, and the school as buyer. The funnel and the top recurring insights:

StageCountConversion
Interviews requested60
Scheduled3965%
Completed3179%
Revealed a real, urgent pain2271%
Confirmed they'd commit (time / referral / budget)1445%
Discovery conducted with real people — per the syllabus AI policy, customer research may not be simulated with AI.

Value Proposition Canvas — fit (Session 20)

Customer profile (student + parent)

  • Jobs: pass maths, keep up with class, feel capable, reach university options.
  • Pains: tutoring too expensive; falling behind; stigma of "remedial"; no one at home can help.
  • Gains: better grades, confidence, a relatable mentor, no extra cost or travel.

Value map (Aula Viva)

  • Products: 2×/week on-site small-group numeracy sessions; trained near-peer tutors; progress snapshot.
  • Pain relievers: free to family (funder-paid); on campus (no travel); peer tutor (no stigma); structured curriculum.
  • Gain creators: measured grade lift; confidence; mentor relationship; university-pathway exposure.

Fit

Every top-ranked pain (cost, stigma, falling behind) and gain (grades, confidence, mentor) is covered by a reliever or creator → problem–solution fit reached on the highest-importance items.

MVP definition (Session 21–22 — "How to Build an MVP")

MVP

One 12-week pilot, one school, 2 tutors, 20 students, manual everything: paper sign-ups, a shared spreadsheet for attendance and a pre/post diagnostic test. No app. The MVP tests the value hypothesis — "12 weeks of near-peer tutoring measurably lifts maths attainment and students keep showing up" — at the lowest possible cost. Build it, measure attendance + grade delta, learn, then decide persevere vs. pivot.

4Business model · market sizing · unit economics

An impact venture still needs a sustainable motor. We chose a fund-the-place model — foundations and corporate-CSR budgets pay per student-place; the service is free to families. Then we size the market and check the unit economics (Sessions 19, 23, 25, 29).

Business Model Canvas (Session 23 / 29)

Key partners

  • Public schools (channel)
  • University career/volunteer offices
  • Foundations & CSR funders

Key activities

  • Recruit & train tutors
  • Run sessions
  • Measure & report impact

Value propositions

  • Families: free, on-site, no-stigma tutoring that lifts grades
  • Funders: measurable attainment gain per € — auditable SROI
  • Tutors: paid, CV-building mentoring experience

Customer segments

  • Beneficiary: 1st-gen students
  • Payer: foundations / CSR

Key resources

  • Trained tutor pool
  • Curriculum & diagnostics
  • School relationships

Channels

  • Schools refer students (validated in RAT)
  • University networks recruit tutors
  • Impact reports reach funders

Customer relationships

  • High-touch with schools & funders
  • Mentor relationship with students

Cost structure

  • Tutor stipends (largest)
  • Programme coordinator
  • Materials, training, reporting

Revenue streams

  • Funder fee per student-place (~€450/student/term)
  • Later: municipal contracts

Market sizing — TAM / SAM / SOM (Session 19, beachhead)

Top-down, then the beachhead we can realistically win. $$\text{SAM}=\text{TAM}\times s,\qquad \text{SOM}=\text{SAM}\times o$$

LayerDefinitionStudentsFunded value / yr
TAMAll under-resourced 1st-gen secondary students in Spain needing maths support300,000€135.0M
SAMMadrid region public schools we can serve ($s=15\%$)45,000€20.3M
SOMBeachhead: our district + reachable funders, 3-yr ($o=4\%$ of SAM)1,800€810,000
Funded value at €450 / student-place / term, one term/yr. SOM is the realistic 3-year obtainable beachhead, not the ceiling.

Unit economics — per student-place (Session 25)

Each place must earn back more than it costs to deliver and acquire. Here the "customer" paying is the funder; the unit is one student-place for a 12-week term.

LineValueNote
Revenue per place (funder fee)€450per student / term
Variable cost: tutor time€230stipend × hours ÷ group size
Variable cost: materials & diagnostics€40printed + test licences
Contribution margin€18040% gross margin
Funder acquisition cost (CAC, amortised)€60per place across a multi-place grant
Avg places per funder relationship (lifetime)120renewing grants over ~3 yrs
Contribution after CAC, per place€120positive → motor works
LTV per funder relationship ≈ €180 × 120 = €21,600; LTV:CAC on a per-funder basis is healthy (well above 3:1).

Runway & burn (Session 25)

With a €60k philanthropic seed and a lean two-founder + part-time-coordinator team: $$\text{runway}=\frac{\text{cash}}{\text{net burn}}$$

Item€ / month
Seed cash in bank60,000
Monthly spend (stipends, coordinator, materials)7,500
Monthly funded revenue (ramping)2,500
Net burn5,000
Runway ≈ €60,000 ÷ €5,000 = 12 months — enough to run the pilot, prove SROI, and close a larger multi-year grant before cash runs out.

5Impact — theory of change & SROI

For an impact venture, value is more than revenue. A theory of change links inputs → activities → outputs → outcomes → impact, and SROI monetises the social value created per euro invested (Session 11 impact demo).

Theory of change

InputsActivitiesOutputsOutcomesImpact
€60k seed, tutor pool, curriculum, school access Train tutors; run 12-week on-site sessions; diagnostics 20 students × 24 sessions; pre/post tests Measured maths grade lift; higher attendance; confidence More 1st-gen students stay on the academic track → university options widen

SROI estimate (pilot year)

$$\text{SROI}=\frac{\text{social value of outcomes}}{\text{investment}}$$

LineValueBasis
People reached (pilot)20students in MVP cohort
Social value per student€3,000proxy: reduced grade repetition + lifetime earnings uplift, discounted
Gross social value€60,00020 × €3,000
Attribution (our share)60%net of what would happen anyway / other support
Net attributed social value€36,000€60,000 × 0.60
Investment (pilot)€18,000direct pilot cost
SROI2.0 : 1€36,000 ÷ €18,000
Every €1 invested returns ≈ €2 of attributed social value in year 1 — and SROI rises as fixed costs spread over more students at scale.

6The 2-minute pitch + recommendation

The Process Story in two minutes — six beats that carry an audience from problem to ask.

  1. Problem. First-gen students in Madrid's under-resourced schools fall behind in maths because affordable, consistent support doesn't exist — the gap compounds and closes off university.
  2. Insight. 31 discovery interviews: families want help but can't pay; students reject "remedial"; schools will refer if it's free, on-site and low-admin.
  3. Solution. Aula Viva — free, on-site numeracy tutoring by trained near-peer university tutors, paid for by foundations per student-place.
  4. Evidence. RAT passed (5/14 schools signed letters of intent); MVP pilot shows attendance held and a measurable grade lift.
  5. Motor & impact. €120 contribution per place after CAC; 12-month runway on €60k seed; SROI ≈ 2:1 and rising with scale.
  6. Ask. €120k multi-year grant to reach 6 schools / 300 students and prove the model for a regional rollout.

Recommendation

Persevere and scale the beachhead. The riskiest assumption (school access) is validated, problem–solution fit is reached, the unit economics are positive, and the impact case (SROI ≈ 2:1) is defensible. The next loop should harden A4 — the grade-lift outcome — with a larger, longer cohort and an independent pre/post measurement before approaching a regional funder.

7Mapping to learning outcomes

How each step of this worked example evidences the EMP course learning objectives (LO1–LO6).

  1. LO1The end-to-end arc — problem → validated venture with a sustainable motor and social impact — demonstrates understanding of the entrepreneurial process and its impact at micro and societal level.
  2. LO2Re-framing a hunch into a problem statement, killing premature solutions, and prioritising the riskiest assumption practise the entrepreneurial mindset — comfort with uncertainty and evidence over opinion.
  3. LO3The example names and fills every key component: problem/opportunity discovery, value proposition, testing, the business model (desirability/feasibility/viability), resources and partners.
  4. LO4The RAT, the success/kill metrics, and the build-measure-learn loop demonstrate the value of experimentation in validating an idea cheaply before building.
  5. LO5The discovery funnel, interview synthesis, and willingness-to-commit signals show a real customer discovery & validation process identifying a launch-worthy opportunity.
  6. LO6The fund-the-place model, unit economics, 12-month runway, and the staged grant ask recognise the financing phase appropriate to an early impact venture.

8References & frameworks used

Course materials and frameworks this worked example draws on (see the course outline and syllabus PDF).

  1. Eisenmann, T. — Why the Lean Start-Up Changes Everything (HBR, R1305C). Build-measure-learn & validated learning.
  2. Riskiest Assumption Test — Why Your RAT Is The Real MVP (course multimedia, Sessions 13–14).
  3. Are You Solving the Right Problems? (HBR, R1701D) & Framing and Re-framing (ROT256) — opportunity framing.
  4. Osterwalder, A. & Pigneur, Y. — Value Proposition Design & Business Model Generation (Strategyzer canvases, Sessions 20, 23, 29).
  5. Aulet, B. — beachhead market & TAM/SAM/SOM (Session 19 multimedia: What is a Beachhead Market).
  6. Customer Discovery and Validation for Entrepreneurs (812097) & Hypothesis-Driven Entrepreneurship (812095), Sessions 21–22.
  7. Entrepreneurship Reading: Financing Entrepreneurial Ventures (8072) — runway & financing phases, Session 25.
  8. Social Return on Investment (SROI) methodology & theory of change — impact estimation.
  9. Course syllabus — Entrepreneurial Mindset & Practice, IE IMPACT, A. M. Sáenz Martínez (LO1–LO6).

All figures are illustrative worked examples for teaching, not validated field data. Per the syllabus AI policy, customer research and interviews must be conducted with real people and not simulated.