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Case Study Project: Goalhanger’s Subscriber Growth for Economics Students

UUnknown

2026-02-27

9 min read

Turn Goalhanger’s 250k subscribers into a hands-on economics case: modelling revenue, churn, pricing tests and classroom assignments.

Turn Goalhanger’s 250,000 Subscribers into a Live Economics Case Study — Fast

Hook: Students and instructors struggle to bridge textbook models and messy, real-world data. Use Goalhanger’s 2026 milestone — 250,000 paying subscribers generating roughly £15m a year — as a turnkey classroom case: quantifiable, current, and rich with applied economics problems from pricing and elasticity to lifetime value and scenario planning.

Why this case matters in 2026

In late 2025 and early 2026, the global media-business landscape accelerated its shift to subscription-led revenue, first-party data strategies and AI-driven personalization. Podcast publishers and production companies like Goalhanger now sit at the cross‑roads of creator-economy growth and platform competition. Examining a concrete example gives students practice in:
revenue modelling, pricing strategy, cohort analysis, policy trade-offs, and sensitivity testing — all essential skills for economics, business, and data analysis courses.

Case Snapshot (use this as your dataset seed)

Press coverage reports Goalhanger exceeded 250,000 paying subscribers across its network, with an average subscriber paying £60 per year (split roughly 50/50 between monthly and annual payments). That equates to around £15m annual subscriber income. Membership benefits include ad-free listening, early content, newsletters and community features.

Source (summary): Goalhanger exceeds 250,000 paying subscribers, estimated £15m annual subscriber income — Press Gazette, early 2026.

Learning Objectives (what students will practice)

Build a subscription revenue model and forecast (monthly and annualized).
Estimate ARPU, churn, LTV and break-even CAC given assumptions.
Run pricing sensitivity and elasticity experiments.
Perform cohort and retention analysis using synthetic data.
Translate model outputs into strategic recommendations and investor-style slides.

Step-by-step modelling guide (classroom-ready)

1) Recreate the headline revenue

Start with the basic formula and verify the reported figure.

Total revenue = Subscribers × Average annual revenue per user (ARPU).
Given: Subscribers = 250,000; ARPU = £60 ⇒ Revenue = 250,000 × £60 = £15,000,000.

2) Build a monthly model (use Excel or Google Sheets)

Break the user base into monthly and annual cohorts. Assume the 50/50 split applies to count, not revenue share.

Monthly cohort price (implied): Let monthly price p_m satisfy annualized value for monthly payers. If annual payers pay £X annually and monthly payers pay p_m per month, we only need the average ARPU = £60. For classroom clarity, set annual price £60, then monthly equivalent = £5/month (since 12 × £5 = £60). Adjust in exercises.
Sheet columns: month, new_monthly_subs, new_annual_subs, total_monthly_subs, total_annual_subs, churn_monthly, churn_annual, revenue_monthly, revenue_annual, total_revenue.

3) Simple churn & LTV assumptions

Use conservative base assumptions (students will change these):

Monthly churn = 3% (0.03) per month.
Annual churn = 10% (0.10) per year (or convert to monthly = 0.10/12 ≈ 0.0083 for modelling purposes).
ARPU: monthly = £5, annual = £60.

Basic LTV formulas (simplified):

LTV_monthly ≈ ARPU_monthly / churn_monthly = £5 / 0.03 ≈ £166.7.
LTV_annual ≈ ARPU_annual / annual_churn = £60 / 0.10 = £600.
Note: These simplified LTVs ignore discounting and gross margin; include a discount rate and margin for advanced tasks.

4) Scenario & sensitivity analysis

Teach students to run at least three scenarios: Conservative, Base, and Upside. Key levers:

Growth rate of new subs (monthly acquisition).
Churn changes from retention initiatives or price hikes.
Price changes and migration between monthly/annual mixes.

Example sensitivity: increase monthly price 10% (from £5 to £5.50). If elasticity implies 5% higher churn among monthly payers, compute revenue change and net effect on ARPU and LTV.

Data-driven classroom questions

Use these as in-class prompts or homework:

Verify the headline: Show the arithmetic that yields £15m. Then disaggregate revenue into monthly vs annual payments and compute each stream's share of revenue.
Assume a blended ARPU of £60 and 250,000 users. If the company reduces annual price by 10% to boost conversions, what change in sign-up rate would be required to keep revenue flat? (Solve for required % increase in subscribers.)
Estimate how a 1 percentage point reduction in monthly churn affects annual revenue after 12 months. Use the monthly model and show incremental revenue and cumulative NPV (assume discount rate of 8%).
Design an A/B test to estimate price elasticity for monthly users. What sample size and test duration are needed for 95% power to detect a 5% change in conversion? What metrics do you track?
Using an assumed Customer Acquisition Cost (CAC), compute payback period and build a profitability table for 12, 24 and 36 months.

Assignment prompts — scaffolded activities

Assignment A: Replicate & extend (individual, 1 week)

Deliverables:

Excel/Sheets file that reproduces the £15m result and includes a monthly cohort model for 24 months.
One-page memo summarising three sensitivity runs and recommended priority for management.

Assignment B: Price elasticity experiment (group, 2 weeks)

Tasks:

Design a randomized experiment to test price points for new monthly subscribers.
Simulate the experiment with synthetic data and estimate elasticity.
Provide recommendation and expected revenue change with confidence intervals.

Assignment C: Strategic memo — growth vs profitability (capstone, 3 weeks)

Prompt:

Assume Goalhanger is considering subsidizing subscriptions to grow audience ahead of a planned advertising relaunch. Model two 3-year strategies: Subscription-first growth (lower CAC, higher churn) vs Hybrid (higher ad revenue, slower subscription growth).
Deliver a slide deck for a hypothetical board meeting outlining the models, risks, and recommended path.

Sample dataset schema (synthetic)

Provide students with a CSV template including these columns for each monthly row:

month (YYYY-MM), new_monthly_signups, new_annual_signups
active_monthly, active_annual, churn_monthly_rate, churn_annual_rate
price_monthly, price_annual, revenue_monthly, revenue_annual
cac_per_signup, marketing_spend, gross_margin_pct

Grading rubrics (practical & transparent)

Model correctness & reproducibility — 40%: formulas documented, workbook tidy, assumptions explicit.
Analysis & insights — 30%: scenario reasoning, sensitivity, LTV/CAC logic, and policy recommendations.
Presentation & communication — 20%: clarity of memo/slide, implications for real business decisions.
Creativity & data hygiene — 10%: novel experiments, realistic synthetic data, error-checking.

Advanced extensions for senior courses

Challenge students with these 2026-relevant problems:

First-party data strategy: Model the value uplift from using listening behaviour to reduce churn by 1–3% using personalization and predictive retention AI.
Dynamic pricing: Simulate a machine-learning pricing engine that offers personalized monthly discounts to high-risk churn cohorts. Compare revenue and fairness implications.
Regulatory & policy analysis: Evaluate how stricter consumer subscription regulations (e.g., simplified cancellation or clearer auto-renewal disclosures introduced in 2025–26) change churn and acquisition costs.
Platform risk: Model distribution changes if a major platform (Spotify/Apple) introduces a competing paid tier or improves discovery for competitors.

Instructor tips & classroom management

Start with the headline calculation in a 10–15 minute live demo to set expectations.
Give students the synthetic CSV to remove dataset-cleaning time and focus on economics.
Use breakout groups for the price experiment assignment; have each group present one slide with a 90-second pitch.
Encourage code submissions (R/Python) or spreadsheet models; both skill-sets are valuable.

Illustrative worked example (short)

Suppose Goalhanger splits 250k evenly: 125k monthly payers at £5/month and 125k annual payers at £60/year. Compute first-year revenue:

Revenue_monthly (annualized) = 125,000 × £5 × 12 = £7,500,000.
Revenue_annual = 125,000 × £60 = £7,500,000.
Total = £15,000,000 (matches headline).

Now, if monthly churn falls from 3% to 2% due to a retention initiative, steady-state monthly LTV increases from ~£166.7 to £250. With a CAC of £50 per monthly signup, payback periods and unit economics improve significantly — an excellent springboard for ROI calculations in class.

Linking to 2026 industry trends (why your students should care)

Key trends through early 2026 that make this case timely:

Subscription saturation & bundling: Consumers manage multiple subscriptions; bundling and cross-offers have become standard tools to reduce churn and increase ARPU.
AI-driven retention: Predictive models and personalization are now mainstream in media, enabling targeted offers and content recommendations that materially affect LTV.
First-party data importance: With third-party cookies largely deprecated by 2025, publishers must rely on logged-in behavioural data for targeting and attribution.
Regulatory attention: Governments and platforms tightened disclosure rules on auto-renewals and free-trial conversions in late 2024–2025, impacting acquisition funnels.

Discussion prompts for seminar

Is revenue growth from subscriptions inherently more valuable than ad revenue? Under what assumptions?
How should Goalhanger weigh short-term ARPU growth against long-term retention? Provide a model-based answer.
What ethical concerns arise when using personalization and dynamic pricing in media subscriptions?

Actionable takeaways for instructors and students

Start simple: Reproduce the £15m headline in one line of arithmetic, then iterate complexity.
Make assumptions explicit: Churn, CAC, margin and discount rate drive all valuations.
Test levers: Price, retention and acquisition volume; show which lever most affects NPV.
Bring trends in: Ask students to incorporate AI personalization or regulatory changes into at least one scenario.

Synthetic CSV with 24-month columns (schema above).
Starter Excel workbook: revenue sheet, churn sheet, scenario tab.
Grading rubric and slide template for 5-slide board memo.

Final classroom prompt (turnkey)

Using the supplied dataset and the assumptions above, produce a 2,000-word report and a 5-slide summary recommending whether Goalhanger should (A) increase monthly price by 10% with targeted retention offers, (B) push more users to annual plans via discounts, or (C) prioritize growth via paid acquisition. Include model outputs, sensitivity analysis and a one-paragraph risk assessment.

Closing — why this exercise pays off

Goalhanger’s milestone is more than a headline: it’s a compact, modern microcosm of subscription economics. Students who can build clear models, test pricing, and justify recommendations on this case will be ready for internships and roles in media, product, and strategy in 2026. The assignment teaches technical fluency (cohort models, LTV, elasticity) and the strategic judgement employers demand.

Call to action: Download the instructor toolkit and starter spreadsheet, adapt the assignments for your syllabus, and run this live in your next module. Want the template or a turnkey slide deck? Contact us for the free classroom pack and a 30-minute walkthrough.

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