SaaS Product Metrics 2024 – Userpilot

What “SaaS product metrics” means in 2024 (and why it’s not the same as “SaaS metrics”)

Let’s clear up the confusion: product metrics describe what users do inside the product (activate, adopt a feature,
come back, hit value). business metrics describe what happens to the business as a result (MRR/ARR, churn, CAC, LTV, NRR).
Both matterbut if your product metrics are fuzzy, your business metrics become a mystery novel with missing chapters.

Product metrics answer “Are people getting value?”

• Activation rate: Are new users reaching the “aha” moment?
• Time-to-value: How long until they get value (and do they quit first)?
• Core feature adoption: Are users actually using the thing you sell?
• Retention: Do they come back after the first month?
• NPS: Do they like you enough to recommend you (or at least not warn their friends)?

Business metrics answer “Is value turning into revenue efficiently?”

• MRR/ARR: Recurring revenue trend (the heartbeat).
• Churn (logo + revenue): How much you’re losing and how fast.
• NRR (net revenue retention): How much existing customers expand or shrink.
• CAC payback: How long it takes to earn back what you spent to acquire customers.
• Burn multiple / efficiency score: Whether you’re buying growth at a reasonable price.

In 2024, the winners didn’t just “grow.” They grew with proof: tighter funnels, faster value, better retention,
and a clearer story about efficiency.

The 6 SaaS product metrics Userpilot benchmarked for 2024 (with what they mean in real life)

Benchmarks are not gradesthey’re context. If you’re above average, great. If you’re below, it’s not a crime; it’s a clue.
The trick is defining metrics the same way every time, segmenting correctly, and using benchmarks to prioritize what to fix.

1) Activation rate: the “did they get it?” metric

Definition: the percentage of new signups who complete a predefined activation event.
Keep it concrete: “created first project,” “connected integration,” “sent first campaign,” etc.

Formula: Activation Rate = Activated Users / New Users

2024 benchmark: average activation rate of 37.5% (median 37%).
Translation: roughly 6 out of 10 people who sign up never reach the moment where your product makes sense.

If this number is low, your growth efforts are basically pouring water into a bucket with a holeimpressive splashing,
but the bucket stays empty.

2) Onboarding checklist completion: helpful… until it becomes homework

Checklists can be great because they reduce ambiguity. They can also be ignored because nobody signed up for a to-do list.
In Userpilot’s benchmark, the overall average completion rate was 19.2%. That’s not a typo.

What it means: if your onboarding depends on checklist completion, you’re betting the company on a behavior
most users don’t do. Use checklists as scaffoldingnot as a moral test.

Formula: Checklist Completion = Completed Checklists / Checklist Views (or per-user completion rate).

3) Time-to-value: your “patience budget” is tiny

Time-to-value (TTV) measures how long it takes a new user to experience meaningful valueoften aligned to your activation event.
Userpilot’s benchmark reported an average TTV of 1 day, 12 hours, 23 minutes (median 1 day, 1 hour, 54 minutes).

Formula: TTV = Timestamp(Activation Event) − Timestamp(Sign-up)

A faster TTV doesn’t always mean “better,” but a slow TTV is almost always risk. If your product requires setup,
integrations, approvals, or training, your job is to create a smaller “first value” moment that can happen sooner.

4) Month-1 retention: the “do they come back?” reality check

Month-1 retention captures how many new users return to use the product after their first monthan early signal of whether
the product fits into their workflow.

Userpilot’s benchmark found an average month-1 retention rate of 46.9% (median 45.25%).
In plain English: about half of new users are gone by the end of month one.

Formula: Cohort-style, for example Month-1 Retention = Users active in month 1 / Users who signed up in month 0.
(Exact definitions varypick one and stick to it.)

5) Core feature adoption: the metric that tells you if the product is actually being used

Core feature adoption measures how many users regularly use your product’s key feature(s) that drive value.
Userpilot’s benchmark: average 24.5%, median 16.5%.

That’s a polite way of saying: even after people sign up, many don’t use the core thing you built.
This is why “more signups” doesn’t automatically mean “more revenue.”

Formula: Core Feature Adoption = Users who meet adoption criteria / Active users
(Where “adoption criteria” is behavior-based, not vibes.)

6) NPS: useful when you treat it like a system, not a scoreboard

Net Promoter Score is a customer sentiment metric based on one question: “How likely are you to recommend us?”
It’s simple, which is both its strength and its weakness.

Userpilot’s 2024 benchmark reported an average SaaS NPS of 35.7 (median 39).

Formula: NPS = %Promoters (9–10) − %Detractors (0–6)

The value of NPS isn’t the number. It’s the patterns in the follow-up: what promoters love, what passives wish existed,
and what detractors keep tripping over.

How product metrics connect to revenue metrics (the “stop measuring in a vacuum” section)

A clean way to think about SaaS performance is a chain reaction:

Activation → Adoption → Retention → Expansion → Net Revenue Retention → ARR

When product metrics improve, business metrics usually get easier. For example:

• Higher activation often improves trial-to-paid conversion (more users reach value before the trial ends).
• Higher core adoption often reduces churn (users build habits around the product’s “must-have” value).
• Better retention increases LTV and makes CAC payback faster (you earn revenue longer).
• Better adoption + retention can lift expansion revenue, improving NRR (especially in usage-based or seat-based models).

A few “must-know” revenue definitions

• MRR/ARR: recurring revenue monthly/annualized.
• Logo churn: % of customers lost.
• Revenue churn: % of recurring revenue lost (more important than logo churn in many B2B models).
• NRR (Net Revenue Retention):
  (Starting RR + Expansion − Contraction − Churn) / Starting RR
• CAC payback: how long it takes gross margin dollars to repay acquisition costs.

If you can’t connect a product metric to one of these business outcomes, it might still be interestingbut it’s not a priority metric.

2024 investor-grade efficiency: burn multiple, efficiency score, and “Rule of X” (without the finance fog)

A big 2024 theme: companies were pushed to prove efficient growth. Two metrics show up constantly in board decks and
operator conversations: burn multiple and the Bessemer Efficiency Score (they’re basically mirror images).

Burn multiple

Formula: Burn Multiple = Net Burn / Net New ARR

Lower is better. It tells you how many dollars you burn to generate each dollar of net new ARR.

Bessemer Efficiency Score

Formula: Bessemer Efficiency Score = Net New ARR / Net Burn

Higher is better. Same story, flipped.

Rule of 40 vs. Rule of X

The classic Rule of 40 is: Growth Rate + Profit Margin ≥ 40%.
In 2024, Bessemer argued for a “Rule of X” framing that weights growth more heavily:
Rule of X = (Growth Rate × Multiplier) + FCF Margin.

Don’t treat these like magic spells. Use them as conversation starters:
Are we balancing growth and profitability appropriately for our stage?

A simple 2024 SaaS dashboard that doesn’t ruin your life

The best dashboards do two things: (1) show trends, and (2) point to actions. Here’s a practical layout for a product-led
or hybrid SaaS team.

North Star + three supporting layers

1. North Star metric: the value your users receive repeatedly (e.g., “weekly active teams completing X”).
2. Activation + TTV: are new users reaching value, quickly?
3. Adoption + retention: are they building habits and using core features?
4. Revenue outcomes: conversion, churn, NRR, CAC payback.

Three rules that prevent dashboard chaos

• Segment first: SMB vs mid-market vs enterprise, PLG vs sales-led, industry vertical, acquisition channel.
• Cohorts over averages: track retention by signup month; don’t just eyeball a single blended number.
• Define events precisely: “Activated” must mean a specific action, not “clicked around a bit.”

How to improve each Userpilot benchmark metric (practical levers that don’t require a total rebuild)

Improve activation rate

• Pick one activation event that correlates with retention (not vanity actions like “visited settings”).
• Reduce steps to value: remove optional fields, postpone non-essential setup.
• Guide the first session: short walkthroughs, tooltips, or contextual promptsonly when needed.
• Personalize by role/use-case: a marketer and an engineer should not get the same onboarding path.

Improve onboarding checklist completion

• Make it tiny: 3–5 steps beats 12 steps almost every time.
• Write benefit-first tasks: “Import contacts” is weaker than “Import contacts to send your first campaign.”
• Time the checklist: show tasks when users are ready, not all at once like a pop quiz.

Improve time-to-value

• Offer templates/defaults: empty states should feel like a starting line, not a blank page.
• Preload sample data when appropriate, so users can “see” value before they build it.
• Instrument friction: track where users stall (setup, integration, permissions, confusing UI).

Improve month-1 retention

• Progressive onboarding: teach features as users need them, not in one exhausting “tour.”
• Habit hooks: reminders, triggers, or recurring workflows that bring users back naturally.
• Fast support: in-app help, searchable docs, and clear next steps can prevent early churn.

Improve core feature adoption

• Define “adoption” as repeated usage (e.g., “used X feature 3 times in 7 days”).
• Nudge contextually: prompts should appear when a feature is relevant, not random.
• Teach the “why,” not just the “how”: show what outcome the feature unlocks.

Improve NPS (without gaming it)

• Ask at the right moment: after a successful outcome, not immediately after signup.
• Always capture the “why” with a follow-up question.
• Close the loop: respond to detractors and communicate what you fixed.

Common traps that make SaaS metrics lie (politely)

• Vanity metrics: sessions, clicks, and pageviews that don’t map to value or revenue.
• Blended averages: mixing free users, trials, and paid customers hides the truth.
• Definition drift: changing what “activation” means mid-quarter breaks comparisons.
• No segmentation: a single number across all personas is basically a horoscope.
• Chasing benchmarks blindly: “average” isn’t your goalyour strategy is.

Mini example: how a better activation rate can compound into real ARR

Suppose you drive 10,000 new signups/month with a trial model.
Using the 2024 benchmark activation rate (37.5%), about 3,750 users activate.
If your activated-to-paid conversion is 8%, that’s 300 new paying customers.

Now imagine you improve activation from 37.5% to 45% by simplifying onboarding and shortening time-to-value.
That’s 4,500 activated users. At the same 8% conversion rate, you now get 360 customers.
That’s +60 customers/monthbefore you spend a single extra dollar on acquisition.

If your average new customer is $400 MRR, that’s $24,000 more MRR per month of signups.
And if month-1 retention also improves, your churn drops, LTV rises, and CAC payback gets better. This is why product metrics
aren’t “nice to have.” They’re leverage.

Experiences related to “SaaS Product Metrics 2024 – Userpilot” (500+ words of real-world patterns teams report)

Across SaaS teams that actively use product metrics benchmarks (including the six highlighted in Userpilot’s 2024 report),
a few experience-based patterns show up again and againnot as theory, but as “we tried it, and this is what happened.”
Think of these as field notes from teams who moved beyond dashboards and into behavior change.

Pattern #1: Activation improves fastest when teams stop arguing about the activation event.
A surprisingly common experience is spending weeks debating what “activation” should mean. One team insists it’s “completed profile,”
another says “connected integration,” and someone in leadership suggests “clicked any button, probably.” The teams that improve
activation the fastest pick a single activation event that clearly signals value (often tied to completing the first meaningful workflow),
ship instrumentation, and revisit the definition only if retention data proves it’s the wrong milestone. Once the activation event is stable,
experimentation becomes real: onboarding tweaks, in-app guidance, and messaging changes can be compared without the baseline shifting underfoot.

Pattern #2: Checklist completion is usually lowand that’s okay if users still reach value.
Teams often feel personally offended by a 19% completion benchmark, as if their checklist is being judged in a talent show.
In practice, teams report that checklist completion becomes a useful metric only when it’s tied to outcomes:
“Do users who complete step 3 retain better?” If the answer is yes, step 3 becomes precious and should be surfaced.
If the answer is no, step 3 is either unnecessary or poorly designed. The experience-based lesson: don’t optimize the checklist;
optimize the path to value. A checklist is just one possible map.

Pattern #3: Time-to-value drops when teams create a “first value” milestone that happens before full setup.
Many SaaS products require configuration, data imports, or approvalsespecially in B2B. Teams report that TTV improves when
they split value into two milestones: a fast “first value” (a small win within the first session or day) and a later “full value”
(the complete workflow). The first value moment can be as simple as running a demo report on sample data, creating a first draft,
or seeing a working output without completing every integration. This reduces early abandonment and gives the user a reason to return.

Pattern #4: Month-1 retention improves when onboarding becomes progressive instead of front-loaded.
A frequent experience is that “tour-style onboarding” creates short-term activity but weak long-term retention. Teams that see better
month-1 retention typically stop teaching everything on day one. Instead, they teach the minimum to reach activation, then use contextual
prompts, lifecycle emails, and in-product cues to introduce the next capability at the moment it becomes relevant. This approach tends to
lift retention because it respects the user’s attention span and aligns education with real needs.

Pattern #5: Core feature adoption often lags because teams assume users understand the “why.”
Teams report that feature adoption increases when messaging shifts from “click here” to “here’s the outcome you’ll get.”
That might mean adding a short explanation at the moment of choice (“Use automation to prevent missed follow-ups”), showing before/after examples,
or creating lightweight success playbooks embedded in the product. In short: adoption improves when the product teaches users how to win.

Pattern #6: NPS becomes more valuable when it’s treated as an operations loop, not a popularity contest.
Teams that get the most out of NPS don’t obsess over the score. They operationalize the feedback: categorize themes, assign owners, and ship fixes.
Over time, this tends to reduce repeated friction points that harm retention and expansion. The experience-based takeaway is simple:
the best NPS strategy is building a product that deserves promotersthen making sure you’re listening when users tell you what’s in the way.

Put together, these experiences point to one big 2024 truth: metrics don’t create growth. Better decisions create growth.
Benchmarks help teams spot where the leaks areand the six product metrics above are a practical place to start patching.