A Product Manager’s Guide to Growth

Contents

  • The mindset for a product manager to adopt in order to drive growth in users and revenue
  • A graphic to explain the relationship between product and growth
  • A 10-point checklist of how a PM can drive growth
  • FAQ: Frequently anticipated questions

10 point how-to checklist on Product-Led-Growth (PLG):

  1. Understand how your product grows: Build a growth model & ID the levers (low input : high output). Find the constraints — what’s blocker in the pipe, that if improved, will have a large flow on effect?
  2. Goal setting and deadlines: Start with specific OKRs (objectives, key results), create a deadline & align a cross-functional team.
  3. Parallel goals: Simultaneously test micro and macro goals. Have a weekly goal that’s easily achievable (80% confidence rate), a monthly goal that’s achievable (50% confidence rate) and annual stretch goals (< 30% confidence). E.g. improve activation rate by 1%/week, improve retention by 5%/month, double monthly recurring revenue within 12 months. Think of it as a pipeline of opportunities of different value, probability and maturity.
  4. Implement a data & visualisation stack: E.g Use Segment (a customer data infrastructure tool)to store data, Amplitude (a product analytics tool) to implement the right event tags and easily visualise behaviour, Tableau to build predictive graphs and a metrics dashboard (or Google Sheets Forecast tool for a free regression analysis).
  5. Improve your feedback loop: Increase the quality, speed and comprehensiveness of relevant user data in order to make faster and better decisions. Automate NPS score reports, Customer success/support metrics, qualitative user research interview takeaways into a live public feed (e.g. via a Slack channel). Make and justify decisions based on data, not highest-earner or loudest voice.
  6. Rigorous scientific experimentation process: Observe user data &make a hypothesis (e.g. We’ve observed that newly acquired are not using the product after initial install. Our hypothesis is that if we create a 1–to-many, self-service knowledge base, this will improve the week 1 retention rate by 10%), ideate and prioritise tests based on expected value and maturity time to value, coordinate the tests with a cross-functional team via public documents (experiment hypothesis and forecast, experiment pipeline and tracker, product requirement document).
  7. Forecast, decision and results analysis: Improve future decision making by conducting a post-mortem on experiment results. E.g Our forecast was off — why was that and how can we improve future forecasting? The results came back better than expected — why was that and how can we allocate resources more efficiently in the future to grow faster? As growth-oriented PMs, we’re deploying capital (financial and human) in order to maximise opportunity with minimum risk. Efficiently allocate capital.
  8. Leverage the concept of product-qualified-leads (PQL): Sales and marketing have the concept of SQL or MQL — i.e this customer is ready to buy or to be messaged. PQLs are ready to experience more value — whether that be converting from freemium to paid, or lite user to power user. Identify the leading indicators of when a user is ready to experience more value, e.g they’ve submitted x number of forms from Typeform, or y number of surveys from Survey Monkey.
  9. Understand value creation to value capture ratio and leverage pricing as a growth tactic: Segment initially charged $1200/year, but realised they were creating $500k+ in value for a certain segment of customers. They pitched their next prospect at $120k/year, were haggled down to $12k/year and finally agreed on $18k/year. The new client perceived that as a bargain.
  10. Communicate cross-functionally: Learn how to communicate with developers (e.g. understand the ramifications on software architecture), marketing (e.g help out with copy and messaging for new features), data scientists (e.g understand statistical significance of experiments and which cohorts to drive), execs (e.g justify decisions based on expected value and communicate quickly and visually), designers (e.g explain a feature with user tasks or a wire-frame) and align a team towards a common goal.

Frequently anticipated questions

So what? Why do I need to know this?

Credit: Openview Partners

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