Your Product Adoption Curve
Not all growth is created equal, as Brian Balfour said. You need to know what is happening quantitatively in your product journey for users before you can design your growth strategy and tactics. Growth doesn't just mean acquisition; if you don't understand your current product adoption curve, then you won't grow just by acquiring users.
It is a mistake that is made time and again: a leaky bucket will always leak, just like a poor product adoption curve will always churn users.
This is a primer on what to track and how, across the funnel, to understand your product blockers and build your SaaS growth tactics.
Product Adoption Curve
Product adoption is a user-centric view of your product. It does not take into account account-based marketing (ABM), wider business-level views such as your marketing and sales motion or your pricing/packaging model - but is a core input into your wider Software-as-a-Service (SaaS) Customer Journey model.
A view of the product starts at the product, not before, but does include onboarding flows and trials - as these on-ramps to product value, though often the domain of marketing, are an inherent part of your product.
Your product adoption curve should be split into the following measures (summarised into the table below):
Stage | Answers the Question | Metric Definition | Why it matters? | Ave SaaS Benchmarks | Best In Class SaaS Benchmarks |
Reach | How many people have used your product recently? | Total number of users who performed any action (monthly) | This metric gives you a snapshot of top line growth and a core conversion metric for GTM teams. | 3% MoM growth | 13% MoM growth |
Activation | What percentage of new users have experienced your product’s value? | A measure of how many users get value from your product within the first week: calculated as % new users (based on user creation date) who performed a key action in the first 7 days. | This gives you a clear view of when users get value and over what time period, leading to conversion. It also dispells internal myths around what is, and isn’t high value. | 17% activation in L7D (last 7 days) | 65% activation in L7D |
Active | Are people performing a key action in a specified period of time? | DAUs - Number of people performing 1 or more key actions per day & WAUs the weekly version | Growth of active users tells you if you really have a sticky product that delivers value. if this is growing for a particular cohort or persona, then hone in there. | 4% growth in WAUs month on month | 72% growth in WAUs month on month |
Engagement | How engaged are your active users? | Total key actions in a week divided by Weekly active users | This is a benchmark of how frequently your users get value from the product (value actions) and if that is growing or declining over time, indicating a growing integration into their workflow, business or processes. | Varies by product - 12 average | Varies by product - 12 average |
Retention | How many of your active users come back? | 7 & 30 day retention curve (users performed actions/total % of users activated from that time point), ideally built by time & segment (experiments, features, industry etc) cohorts. | This enables a view on the risk profile of accounts and users, and gives your customer success function the data required to intervene before churn occurs. | 5% benchmark | No data available |
Reach is a benchmark for your growth - the more this comes from organic and direct channels, the stronger your virality in the market. Analysing your data based on channel, account and cohort is critical for this metric to flow into the rest of the adoption curve. Activation and Active users are the most hotly debated definitions in product teams, given how hard it is to define and how unique it is to each product.
The first step here is to list as a team everything a user can do in your product. Things like “Set up their account” and “add a task”. Then you need to break this list into two categories - actions that enable a user to get value, and actions that actually deliver value.
Actions that enable value are blockers to real activation. Installing a tracking code blocks value creation for a web analytics tool, but doing the action doesn’t itself deliver the product’s utility. The next is to rank the value creating actions. If you use the Kano method then simply map these values over, or start with an equal weighting for your model. Finally, you should combine them into flows that, if completed define a core aha moment for a user - these can be split into personas or buyer types or segments, depending on your product.
Once you have those then the benchmark for SaaS is 12 Actions define activated, but, if you have the data, simply retrofit this into your model for your own use as you might find a weighing system for each user segment defines a more refined activation model you can use to fuel your PLG motion.
Building your view of this product adoption curve is the first step to building a strong PLG motion, as you can quickly identity where users fall down, and how you are performing in order to build your SaaS growth engine to directly address the core areas where your product is letting you, and your users, down. It also directly feeds insights into your Product, Marketing, Sales, Success and Development team, so getting it right is essential for your success.