---
skill: metrics-framework
title: Metrics Framework
input: "Define the metrics framework for a B2B analytics product: the north star, input metrics across acquisition/activation/retention/revenue, and guardrails. Stage: early growth, ~500 paying accounts."
source: generated by claude-sonnet-4-6
---

# Metrics Framework: B2B Analytics Product

**Context:** Early growth stage, ~500 paying accounts, B2B SaaS analytics product.
**Framework chosen:** North Star + Metric Tree
**Why:** At 500 paying accounts you have enough signal to measure real customer value, but not enough scale to optimise channels independently. A North Star + Metric Tree keeps the whole company pointed at one outcome while giving each team (sales, product, CS) clear leading indicators to own. AARRR is too funnel-centric for a product where retention and expansion are the primary growth engine; OKRs alone lack the causal structure you need to diagnose what's breaking.

---

## 1. North Star Metric

**Weekly Active Accounts Reaching an Insight Action**

**Definition:** The number of paying accounts (not individual users) where at least one user, in a given week, completes an *insight action* — defined as any of: sharing a dashboard externally, exporting a report to a downstream tool, creating a saved alert, or presenting a chart in a meeting (tracked via our embed/share event). These are moments where the product has produced something the customer acts on, not just views.

**Why this is the right North Star:**
An analytics product's core promise is not data access — it is decisions made faster or better. Tracking accounts that reach an insight action captures the moment the product delivers that promise: someone took the output and did something with it. This is harder to game than DAU or logins (which reward habit without value), and it correlates strongly with renewal because customers who regularly export, share, or alert on data have built the product into a workflow they won't easily abandon. At 500 accounts, growing this number means both activating dormant accounts and deepening usage in active ones.

**Formula:**
```
Weekly Active Accounts (Insight Actions) =
  COUNT DISTINCT(account_id WHERE insight_action_event >= 1 in rolling 7 days)
```
**Current baseline:** [ADD BASELINE]
**Target:** [ADD TARGET]

---

## 2. Metric Tree

```
Weekly Active Accounts Reaching an Insight Action
├── ACQUISITION: New Pipeline Into the Funnel
│   ├── Qualified new accounts entering trial / week
│   └── Trial-to-paid conversion rate (%)
│
├── ACTIVATION: Getting New Accounts to First Value
│   ├── % of new accounts reaching first insight action within 14 days
│   ├── Time to first insight action (median days, cohorted by signup week)
│   └── Onboarding completion rate (% completing setup checklist)
│
├── RETENTION: Keeping Active Accounts Active
│   ├── Month-1 account retention rate (% of activated accounts still active 30 days later)
│   ├── 90-day account churn rate (rolling)
│   └── Insight action frequency per account (median weekly actions, active accounts only)
│
└── REVENUE: Monetising and Expanding Value
    ├── Net Revenue Retention (NRR) — rolling 12-month
    ├── Expansion MRR from seat / tier upgrades ($/month)
    └── Average Contract Value (ACV) by acquisition cohort
```

---

### L2 Metric Definitions

#### ACQUISITION

**Qualified new accounts entering trial / week**
- **Definition:** Count of new accounts starting a free trial or pilot that meet your ICP criteria (e.g. company size, industry, data source used) — not raw signups, which include noise.
- **Why it matters:** Feeds the top of the funnel. Low-quality signups inflate conversion work without adding to the North Star; tracking *qualified* accounts ties acquisition to accounts likely to reach an insight action.
- **Leading or lagging?** Leading — predicts future activated and paying accounts.
- **How to measure:** CRM (HubSpot/Salesforce) trial start events filtered by ICP qualification score or SDR-confirmed fit tag.

---

**Trial-to-paid conversion rate (%)**
- **Definition:** `Accounts converting to paid / Accounts starting trial` in a given cohort month. Measure at 30 and 60 days post-trial start.
- **Why it matters:** Tells you whether the product is convincing people to pay. A rising North Star with a falling conversion rate means you're working harder to fill a leaking bucket.
- **Leading or lagging?** Lagging (outcome of acquisition + activation quality), but leads revenue.
- **How to measure:** Billing system (Stripe/Chargebee) paid event joined to trial start date in your data warehouse.

---

#### ACTIVATION

**% of new accounts reaching first insight action within 14 days**
- **Definition:** `Accounts with ≥1 insight action event in days 0–14 / All accounts that started onboarding in the same cohort`. 14 days is the activation window because analytics products typically require data connection, dashboard build, and a first "aha" — this rarely compresses below a week but should not take three.
- **Why it matters:** This is the single strongest predictor of whether an account will still be active at day 90. If an account doesn't reach an insight action in the first two weeks, churn probability spikes sharply. Fix this metric and you fix retention downstream.
- **Leading or lagging?** Leading — strongly predictive of 90-day retention and NRR.
- **How to measure:** Product event log: `insight_action` event with `account_id`, joined to account `created_at`. Compute cohort weekly.

---

**Time to first insight action — median days (cohorted by signup week)**
- **Definition:** Median number of calendar days between account `created_at` and first `insight_action` event, measured per signup cohort. Track P50 and P90 to catch tail distributions.
- **Why it matters:** Even if 60% of accounts activate, a median time of 11 days vs. 4 days reflects a materially different onboarding experience. Faster time-to-value compresses the sales cycle's "prove it" phase and reduces early churn.
- **Leading or lagging?** Leading.
- **How to measure:** Same event log as above. `DATE_DIFF(first_insight_action_at, account_created_at)`. Track weekly cohort medians in a time-series chart.

---

**Onboarding completion rate (%)**
- **Definition:** `Accounts completing all steps of the onboarding checklist / Accounts that started onboarding`. Steps should include: data source connected, first dashboard created, first team member invited, first alert set. Define the checklist explicitly in-product — this metric is only meaningful if the checklist reflects the actual path to value.
- **Why it matters:** Diagnostic metric. When time-to-first-insight deteriorates, check here first. A drop in checklist completion means an onboarding step is breaking; stagnant checklist completion with slow insight time means the checklist itself isn't driving the right behaviour.
- **Leading or lagging?** Leading.
- **How to measure:** In-product step-completion events, tracked per account. Build a funnel view in your analytics tool (yes, eat your own cooking).

---

#### RETENTION

**Month-1 account retention rate (%)**
- **Definition:** `Accounts with ≥1 insight action in days 22–35 / Accounts with ≥1 insight action in days 1–14`. This is *activity-based* retention, not just "not cancelled" — an account that stops using the product before churning is already lost.
- **Why it matters:** At 500 accounts, losing 10% in Month 1 is a structural problem that compounding makes catastrophic. This metric is the earliest possible read on whether activation led to a habit, not just curiosity.
- **Leading or lagging?** Leading (predicts 90-day churn and NRR).
- **How to measure:** Product event log with rolling cohort windows. Measure by account, not user.

---

**90-day account churn rate (rolling)**
- **Definition:** `Accounts that cancelled or went inactive (zero insight actions for 30+ days) in the trailing 90 days / Average paying accounts in that period`. Track both hard cancellations and soft churn (paid but dormant — these accounts are at high risk and represent recoverable revenue).
- **Why it matters:** This is your core retention health signal. At early growth stage, a churn rate above ~2–3% monthly compresses your growth ceiling to the point where acquisition can't outrun it. Separate hard from soft churn — they have different interventions.
- **Leading or lagging?** Lagging — it records what already happened.
- **How to measure:** Billing system cancellation events + product event log for soft churn detection. Report monthly, trend quarterly.

---

**Insight action frequency per account — median weekly actions (active accounts only)**
- **Definition:** Among accounts that had ≥1 insight action in the past 30 days, the median number of insight actions per week. Exclude newly onboarded accounts (< 30 days old) to avoid distortion.
- **Why it matters:** Frequency signals depth of workflow integration. An account doing one insight action per month is at risk; one doing five per week has built the product into a recurring process. This metric moves before NRR does and identifies expansion candidates.
- **Leading or lagging?** Leading (predicts expansion and renewal).
- **How to measure:** Product event log, aggregated to account-week level. Compute rolling median over active account cohort.

---

#### REVENUE

**Net Revenue Retention — NRR (rolling 12-month)**
- **Definition:** `(Starting MRR + Expansion MRR - Contraction MRR - Churned MRR) / Starting MRR` for the same account cohort over 12 months. Expressed as a percentage. A B2B analytics product at growth stage should be targeting ≥110% NRR.
- **Why it matters:** NRR is the compounding engine of B2B SaaS. Above 100% means your existing base grows even with zero new sales. Below 100% means you're on a treadmill. At 500 accounts, NRR is more important than new logo growth because expansion from proven accounts costs a fraction of acquiring new ones.
- **Leading or lagging?** Lagging — it is the revenue outcome of retention and expansion behaviour.
- **How to measure:** Billing system (Stripe/Chargebee MRR movements by account, tagged as expansion/contraction/churn). Compute monthly, trend as a rolling 12-month cohort.

---

**Expansion MRR from seat / tier upgrades ($/month)**
- **Definition:** Monthly recurring revenue added from existing paying accounts upgrading their plan, adding seats, or unlocking add-ons. Excludes new logo revenue.
- **Why it matters:** At early growth stage with 500 accounts, expansion MRR is both more capital-efficient than new acquisition and a signal that accounts are getting more value over time (they wouldn't pay more otherwise). Track separately from new logo MRR to understand the true growth mix.
- **Leading or lagging?** Lagging (records the upgrade event), but leads NRR.
- **How to measure:** Billing system, upgrade events tagged by account. `SUM(new_plan_mrr - old_plan_mrr WHERE upgrade_event IN month)`.

---

**Average Contract Value — ACV by acquisition cohort**
- **Definition:** Total annual contract value divided by number of accounts, segmented by the quarter the account was acquired. Track how ACV trends across cohorts.
- **Why it matters:** Rising ACV across cohorts means you're moving upmarket or improving sales qualification. Falling ACV means you're backfilling churn with smaller, riskier accounts. At 500 accounts this is easy to miss in aggregate but visible by cohort.
- **Leading or lagging?** Lagging.
- **How to measure:** CRM contract value fields, grouped by account `closed_won_date` cohort quarter.

---

## 3. Counter-Metrics (Guardrails)

These three metrics exist to ensure you don't optimise the North Star in ways that quietly damage the business.

**1. Support ticket volume per active account (weekly)**
If you optimise for insight action frequency by making exports or alerts easier to trigger, you may inadvertently create confusion (users firing alerts they don't understand, or exporting malformed data). A rising support ticket rate alongside a rising North Star is a warning sign that product quality is being sacrificed for engagement. Target: flat or declining as the account base grows.

**2. Trial-to-paid conversion rate vs. activation rate gap**
Track whether your 14-day activation rate and your trial-to-paid conversion rate move together. If activation rises but conversion stays flat, your sales process or pricing is misaligned with the value customers are experiencing — they're getting value but not paying for it. If conversion rises but activation falls, you may be using sales pressure to close accounts that aren't genuinely ready, loading your churn pipeline.

**3. Percentage of NRR from more than three accounts**
At 500 accounts, NRR can look healthy while being dangerously concentrated. If three accounts represent >30% of expansion MRR, your NRR metric is masking fragility. Track concentration monthly and flag any single account >10% of expansion MRR for executive review. This is especially critical before a Series A, where investors will stress-test customer concentration.

---

## 4. Dashboard Recommendation

### Exec View — Weekly (5 metrics)
| Metric | Why here |
|---|---|
| Weekly Active Accounts (Insight Actions) | The North Star — top of every review |
| 90-day rolling account churn rate | Retention health at a glance |
| Net Revenue Retention (trailing 12-month) | Business compounding signal |
| New qualified trials entered this week | Leading acquisition signal |
| Expansion MRR this month vs. last | Revenue momentum |

### Team View — Daily (9 metrics)
*For product, CS, and growth teams:*
| Metric | Owner |
|---|---|
| % new accounts reaching first insight action (14-day window, current cohort) | Product / Onboarding |
| Time to first insight action — P50 and P90 | Product |
| Onboarding checklist completion rate | Product / CS |
| Month-1 account retention (most recent completed cohort) | CS |
| Insight action frequency per account (active accounts) | Product |
| Qualified trials started this week | Growth / Sales |
| Trial-to-paid conversion rate (30-day cohort) | Sales / Growth |
| Support tickets per active account | CS / Product |
| Soft churn accounts (active 30+ days ago, zero actions last 14 days) | CS |

### Diagnostic View — On Demand
*Pull these when a top-level metric moves unexpectedly:*
- Insight action breakdown by action type (export vs. share vs. alert vs. embed) — tells you *which* value moment is driving or declining
- Activation funnel by onboarding step (where do accounts drop out of the checklist?)
- Churn cohort analysis by acquisition channel and ICP segment
- Feature adoption depth map (which dashboard features correlate with retained accounts?)
- Account health score distribution (a composite of recency, frequency, and depth of insight actions — build this before you need it)

---

## 5. Weekly Metrics Review — Health Check Questions

Use these five questions to turn numbers into decisions in your weekly review:

**1. Is our 14-day activation rate moving in the same direction as time-to-first-insight?**
If activation rate rises but time-to-first-insight also rises, you may be counting a lower-quality action as an insight event — or onboarding is slower but more accounts are eventually getting there. Investigate the *type* of insight action driving the change.

**2. Are accounts that activated via a specific onboarding step retaining at a higher rate?**
Compare Month-1 retention for accounts that completed the full checklist vs. partial completions. If "first alert set" correlates with dramatically higher retention, that step is your real activation moment — consider forcing it earlier in onboarding.

**3. Is soft churn rising even while hard churn holds flat?**
Soft churn (paid but dormant) is the canary. If you see accounts going quiet 60–90 days before cancellation, you have a CS intervention window you're not using. A rising soft churn count is a leading indicator of a churn spike in the next quarter.

**4. Is expansion MRR coming from accounts with high insight action frequency, or from accounts that are being upsold regardless of usage?**
Expansion driven by high-frequency accounts is durable — those customers are paying more because they're getting more value. Expansion from low-frequency accounts is fragile — it often reverses at renewal. Segment expansion MRR by account activity level monthly.

**5. Is our NRR concentration increasing or decreasing?**
Track what share of total NRR (retention + expansion) comes from your top 10 accounts. At 500 accounts this should be gradually diversifying. If it's concentrating, your CS team may be over-investing in whales while mid-market accounts quietly churn — which also distorts every other metric in this framework.

---

## Quality Check

- [x] North Star reflects customer value delivered (decisions acted on), not activity or revenue
- [x] Metric tree covers 4 distinct drivers: Acquisition, Activation,
