Exercises and Simulations

To solidify these concepts, here are some hands-on exercises and simulations:

Exercise 1: Map Your Growth Levers – Take a real or hypothetical SaaS product (it could be your company, a startup idea, or a well-known product for analysis). Using the Growth Lever Mapping Worksheet above, identify at least 2–3 potential levers in each stage: Acquisition, Activation, Retention, Referral, Monetization. Be specific (e.g. "improve onboarding completion" not just "activation"). Then, based on current data (or reasonable assumptions), mark which levers seem high priority. Discussion: Which lever would you focus on first and why? (This helps practice identifying high-impact areas.)

Exercise 2: Competitor Gap Analysis Simulation – You are the head of growth for a SaaS project management tool. A key competitor just released a new integration with Slack. Conduct a mini gap analysis: list the competitor's strengths and your product's strengths. Identify one area where customer needs might be underserved by all current competitors (a gap). Propose a growth lever for your product to exploit that gap. For instance, you might discover no one has a good migration tool from spreadsheets – building one could be an acquisition lever. Share your reasoning: why would this lever drive growth? (The goal is to simulate strategic thinking using competitor insight.)

Exercise 3: Funnel Diagnosis – You're given a mock funnel dataset for a SaaS app for the past month: 10,000 website visitors -> 500 sign-ups -> 100 active after one week -> 50 paying customers after one month -> $5k MRR. Calculate the conversion rates at each stage (visitor->sign-up 5%, sign-up->active 20%, active->paid 50%). Identify the largest drop-off. Then brainstorm two ideas to improve that specific drop-off. For example, if visitor->sign-up is low, ideas could be "redesign homepage for clarity" or "offer an incentive to sign up." If active->paid is the problem, ideas might be "shorten trial length to create urgency" or "sales call to trial users". This exercise tests your ability to interpret funnel data and propose optimizations.

Exercise 4: Prioritization and ICE Scoring – Given a list of 5 potential experiments (the instructor or case study provides these, or use the ideas from Exercise 3), perform ICE scoring for each. The ideas could be: A) Launch referral program, B) Increase ad budget on best-performing channel, C) Add tutorial videos for new users, D) Change pricing model to annual plans, E) Invest in SEO content. Score each 1-10 on Impact, Confidence, Ease. Calculate ICE scores and rank them. Which experiment would you run first? Now, consider if using RICE (adding Reach and Effort) would change the priority. Discuss how scoring might differ for an early-stage startup vs. a later-stage company (e.g. impact of something might be larger when you have more users to reach).

Simulation 1: The PLG vs SLG Decision – Split into two teams (or two approaches in your mind). You have a SaaS product that so far has grown with a sales-led approach (outbound sales to enterprise). The CEO is considering launching a self-serve freemium version to accelerate growth (product-led). One team outline: what are the potential growth lever benefits of adding a PLG motion (think acquisition via free users, viral loops, etc.)? The other team outline: what could be the risks or downsides (e.g. channel conflict with sales, support costs)? Now formulate a strategy that uses both: how might you integrate a freemium tier without hurting the enterprise sales pipeline? This simulates strategic planning and recognizing how different levers (free product vs. sales outreach) can complement each other.

Simulation 2: Metrics Mystery – You are presented with a scenario: "Our activation rate went up 15% this quarter, but our paid conversion rate fell by 5%, and churn increased slightly." In groups, debate what could be happening. Perhaps the activation lever you pulled (e.g. easier sign-up) brought in more casual users who don't convert or retain as well. Or maybe a pricing change made some marginal users convert less. Each group come up with a hypothesis for the metric movement and propose a follow-up experiment to validate it. The point is to interpret metric signals and adjust your lever focus accordingly (maybe focus back on quality of acquisition if conversion fell, etc.).

These exercises encourage applying the frameworks: mapping levers, using data to find bottlenecks, prioritizing actions, and thinking holistically about strategy. They can be done as written assignments or live workshops.