Product Led Growth Flashcards
Product Led Growth Motion
The product activates, engages and retains users eventually funneling them through a payment flow, most commonly without a human touch.
High upfront hurdle. Then exponential benefits afterwards
Product-led experiences best solve frequent, straightforward problems.
Sales Led Growth Motion
Direct and active engagement from sales and customer success help users progress through their journey with the product
SLG is effective in solving complex problems
Growth Hypothesis
The growth motions (SLG or PLG) you want to use for each part of your growth model.
From Acquisition (User sign-up), Activation (User experiences core value prop), and Retention (User establishes habit) to Monetization (User pays or upgrades)
Factors that inform Growth Hypothesis
Willingness to pay - When you know how much your product is worth to your target audience, you will know if you can fund a sales team.
When the three factors are high, PLG fits well, otherwise SLG works well
Motivation
Ability
Permission
Do I have the resources to fund human touch points due to the high willingness to pay?
Do I need human touchpoints to remove roadblocks?
- Low motivation: need sales to demonstrate value
- Low ability: Buyer needs implementation help
- Low permission: need outbound sales to find the right buyer
If the buyer and user are different use cases, do I need multiple growth motions?
PLG vs SLG
Most scaled companies have a mix of both
PLG and SL assisted
SLG and PL assisted
PLG Monetization.
The best PLG monetization strategies are designed with user escalation in mind. From individual to team, then to enterprise.
Conversion Tactics
A company’s strategy for moving users from one tier to another
Choosing the wrong free tactic for conversion might actually hurt your ability to monetize
How to choose conversion tactics
Based on Cost to serve and time to value
Cost to serve, the cost of acquiring and maintaining customers
Time to value, the time between when a user signs up and when they first experience the aha moment
When Cost to serve is high and time to value is low -> Free trail, giving users access to the product for a limited period of time. Generally, companies see 15% trial to paid conversion with minimal dropoff at the start of the trial.
When Cost to serve is low and time to value is low -> Reverse trail, same as trial but at the end of trial, put users back to freemium product which can convert to paid later. Within this freemium group, 25% keep using the product, driving network effects, and indirect monetization.
When Cost to serve is low and time to value is high -> Opt-in trail, Freemium users can sign up for a free trial of the premium product whenever. 5% standard free to paid conversion rate.
When Cost to serve is high and time to value is high -> Sale assisted pilot, needs to be a use case where you can justify sales involvement.
When to take the credit card
80% initial dropoff
20% provide payment information. The conversion of this group can be as high as 70-80%
Cost to serve can guide if and when you take a credit card up front.
High cost -> upfront credit card
When to convert free users?
Monetize users after they have reached an aha moment and started habitually using the product
Product natural use frequency
Value Metrics to scale pricing
Value metrics: Indicate how our product monetizes users
1. Feature differentiation, users buy into a certain set of features and the price scales as they add additional features
2. Usage, How much and how many users are using the app? A usage based value metric that servces as a proxy metric for the outcomes
3. Outcomes, Scaling in line with outcome based value metrics, e,g, stripe per transaction
Choosing your value metric strategy
Consider the customer’s view of value
Consider the cost of revenue
Choose a value metric that doesn’t inhibit your product’s growth loops
Product-qualified accounts vs Product-qualified leads
Ideal Customer Profile (ICP)
Product-qualified accounts: Accounts where certain patterns of usage signal it may be ready for sales intervention; the characteristics of the account match your ICP
Determine which account-level metrics or behaviours indicate that an account is most likely to convert
Product-qualified leads: Buyers or decision makers who match your ICP, and who are also users of your product.
We need PQL to likely close an account
Scoring accounts
- Develop metrics hypothesis, measurable product actions that are predictive of your overall goal (expansion, “monetization”, or engagement)
- Velocity Metrics, the speed with which a user signs up or explores features
- Usage Metrics, the extent to which users are engaging with a wide variety of features
- Volume of usage metrics, the number of users from a single domain
example: Accounts with X number of users, over Y period of time, using ABC features are more likely to monetize. - Analyze account metrics, determine which metrics correlate with monetization
- correlation analysis - Score accounts, Stack rank based on how they meet product-qualified metrics standards.
- Based on the Probability of close
- Expected value calculations
probability of close * contract size = expected value
Building your product qualified playbook
- Revisit sales compensation. You don’t want to toy with people’s livelihoods.
- Should have a different playbook compared to traditional sales
- Tools/systems. Even the existing system might be easy to reuse, but to clearly differentiate it from traditional sales, some simple tools like Google sheet can help. Make sure we always have a feedback loop to help us improve.
