Dirty dozen of PLG B2B SaaS health metrics with benchmarks

A set of key predictive indicators every B2B PLG should monitor.

I've operated or advised 30+ B2B companies with product-led growth (PLG) motion. With many similarities and even more differences, one constant stands out: quantitative metrics these companies use to evaluate the health of the PLG. And there are 12 of them - let's dig in!

Revenue is not it!

Revenue is not a metric. It's an outcome.

Many leaders obsess with revenue. And rightfully so, because revenue is the outcome of any business. But revenue is a lousy metric to goal the team against because it assesses past performance instead of predicting the future. Tracking revenue as a metric is equivalent to navigating a ship with a map that only shows you where you've been, not where you're going. So let's put revenue in the 'outcome' corner and focus on the metrics that will help us understand the future potential of revenue instead. 

Metrics fall across four growth levers

All metrics fall across four universally applicable growth levers:

Every business needs to answer how to acquire, activate, engage, and monetize its customers to create predictable, sustainable, and competitively defensible growth for the product. 

B2B PLG dirty dozen metrics & benchmarks

Acquisition

1. Prospecting traffic

Prospecting traffic measures the number of new potential customers that visit a website. This metric is calculated by excluding the number of returning visitors and focusing solely on the volume of new users that land on the site.

2. New teams with work intent + % new logos

New teams (or new orgs) measure the number of new sign-ups for a product or service with work intent (intent captured through profiling). It's worth noting that this metric is not measured on an individual user level because B2B sells to teams and accounts rather than individual users. Even a team size of one should count as a new team. The purpose of aggregating individual users into teams is to create network effects that can assist in expanding the enterprise. 

Additionally, "% of new logos" is a secondary metric that ensures a constant influx of new logos that a company is acquiring, which is a great predictor of how many accounts could materialize into the sales pipeline. In b2b acquiring 100 users vs. acquiring 100 new logos are very different measurements. 

Benchmarks

Prospecting traffic to new team conversion rate: 10-15%.

Prospecting traffic to new user conversion rate: 20-40%. 

Activation

3. New team set-up

New team set-up measures the number of teams completing mandatory set-up actions to experience aha! moment. For Miro, the set-up is creating a board, adding an element, and inviting a team member. For Amplitude, the set-up metric is the team connecting to the data sources and creating the first chart. 

Benchmarks

60-80% of new work intent teams should be completing set-up (even for technical products). 

4. New team aha!

New team aha! measures how many teams experience the first actual value exchange with the product. For SurveyMonkey, aha! is receiving five or more responses. For Miro, aha! is collaborating on the whiteboard. 

Benchmarks

50% of your new work intent teams should be getting to aha!. 

5. New team habit 

New team habit measures how many teams reach the first Habit loop. A habit loop is defined by repeating aha! (and potentially more meaningful actions) on the desired frequency of use. So weekly active team entering habit would mean the team completed desired activities in 3 out of the last four weeks. Daily active team entering habit is the team completing desired activities 4 out of 5 work days for the past two weeks. 

Benchmarks

30-40% of new work intent teams should be reaching habit.

Engagement

6. Core engaged teams

Core engaged teams metric is often set as a north star metric for the product. Core engaged teams measure how many teams continue staying within the desired frequency of use defined by habit. This metric is often called daily active team (DAT), weekly active team (WAT), or monthly active team (MAT).

7. Users per team

Users per team measure user growth within the team. A growing number of users in the company using the product is a valuable predictor of monetization opportunities in B2B.

8. PQA: Product Qualified Accounts

Product Qualified Accounts metric is an engagement tie to monetization. PQA is the product auto-qualification for sales intervention. It is measured on the account usage volume, velocity change of use, feature breadth, and/or behavioral factors (which can be an aggregation of multiple teams). PQA'ed accounts are sent to sales for prospecting and pipeline generation activities. 

Monetization

9. New paid teams

New paid teams metric measures the number of new work intent teams converting to a paid plan (either self-serve or with sales). Conversion can be direct to paid (without product use) or free to paid. 

Benchmarks

Great freemium conversion rate: 5%.

With a free trial: 10-15%.

With cc trial: 60%. 

An excellent resource for more detailed benchmarks is written by Ada here.

10. New paid ARPA

Average Revenue Per Account (or Team) for the new paid team. The alternative is to track the package mix. 

11. 1st term churn (monthly & annual)

1st term churn measures the churn rate of the monthly and annual plans on their first renewal. Most of the churn happens on 1st renewal, so it is crucial to track this metric closely as a predictor of the overall churn rate. 

Benchmarks

1st term monthly churn: 25%.

1st term annual churn: 20%. 

12. Hand raisers

Hand raisers measure the number of work intent teams submitting sales forms to talk to sales. Hand Raisers measure organic pull into the sales funnel. Segmenting hand raisers into high and low intent is essential, as this metric is often littered with support questions. 

Benchmarks

High-intent hand raisers usually close at a 60% rate into closed-won contracts. 

Happy analyzing!

Note about benchmarks: my personal benchmarks developed across companies such as SurveyMonkey, Miro, Netlify, Amplitude, MongoDB, and others I’ve advised throughout my career. Benchmarks are highly contextual and should be taken with a grain of salt.