Your metrics shape your commercial model. They determine where your team focuses, what gets prioritised, and what you learn. At early stages, measurement is less about reporting and more about navigating. The goal is to find proof points, diagnose friction, and identify leverage early.
The best teams focus less on volume, more on outcomes. Not just how many demos, but what moved the deal forward. Not just pipeline coverage, but where and why deals stall.
This module introduces a three-layer KPI architecture and embeds it in a practical question-led framework that helps early stage teams design KPI systems that work. Each question is paired with examples and case applications to support implementation.
The Purpose of Sales Measurement
Most teams at early stages almost always fall into one of two traps,
- Too much tracking. Creating dashboards full of vanity metrics, investor metrics, or irrelevant numbers (website traffic, MRR spikes, social impressions) that do not reflect value delivered.
- Too little tracking . Tacking activity without understanding the inputs that actually move deals: qualification quality, POC velocity, integration blockers, or time-to-value.
For data and AI companies there are additional traps,
- Fragmented tooling: CRM, product analytics, support, and spreadsheets contain contradictory data.
- Misaligned definitions: “pilot”, “qualified”, “activation” mean different things to engineering and sales.
- Inconsistent pipeline stages: deals sit in POC with no success criteria.
- Long, multi-stakeholder cycles: not captured by traditional SaaS funnels.
- No real-time learning loops teams cannot diagnose friction early enough
A well-designed KPI system is not a dashboard but a decision engine. It aligns the commercial team, forces focus, reveals what is working and done correctly, it becomes one of the highest-leverage GTM sources a founder can build.
Measurement is not about reporting. It is about making better decisions, faster.
Lead vs. Lag Indications
What predicts traction vs proves it
Lead Indicators show momentum
These are the controllable inputs that predict forward progress.
- Technically qualified opportunities
- POC defined with success criteria
- Conversion rates by stage
- Inbound signal quality
- Time to first demo
Lead indicators move before revenue moves — crucial at pre-seed and seed.
Lag indicators show outcome.
These validate business and product value
- Revenue (ARR, ORR, expansion
- Time-to-value
- Production adoption
- POC to production conversion
- Retention and NRR
- Implementation time
Lag indicators matter at Series A — earlier than that, they are often misleading.
KPI Architecture
Layer 1 – North Star Metric
The single metric that captures real value delivered.
Layer 2 – Input Metrics
These are the drivers of the North star controlled by the commercial team.
Layer 3 – Activity Metrics
The baseline operational actions that influence inputs.
What is our true North Star metric?
Your North Star metric is the one number metric reflecting the moment product value is delivered and captured in the hands of the customer. It aligns the team and guides early GTM focus.
While revenue is the ultimate goal at the early stages, revenue often lags behind value creation. Measuring relevant proxies becomes critical e.g. number of users on the platform, number of repeat customers, number of pipeline coverage. Depending on your model consider measuring,
- ARR (if usage directly maps to revenue/value)
- Pilots signed (enterprise-led, proof equals progress)
- GMV (marketplace, liquidity equals growth)
- Activated users (if product-led)
Example: an AI legal assistant for mid-size law firms does not track MRR in the first six months. Instead, it tracks the numbers of high-intent pilots signed and % of users completing three workflows — early proof of product stickiness and long-term retention.
Further Reading: Startup Metrics by Visible VC, 16 Startup Metrics by by Jeff Jordan, Anu Hariharan, Frank Chen, and Preethi Kasireddy at 16z.
What input drives the North Star metric?
It is not enough to measure outcomes, you need to understand what inputs drive it. Break your North Star down into actions your team controls – calls, demos, proposals, and loops that lead to value by measuring,
- Cold outreach: emails sent, calls made
- Sales engagement: demos booked, trials started, follow-ups completed
- Conversation: proposal acceptance right, contract signed
- Signups: onboarding completion → Retention at 30 days
Example: a data infrastructure company realises most closed deals come from warm intros via CTOs, not outbound. They shift weekly sales activity to structured partner referrals and referral-to-demo as key metrics, improving demo conversion by 2x in six weeks.
What cadence makes sense for decisions?
Track too frequently, you get noise. Too infrequently, you miss learning. Measure what matters and match the rhythm of your review to the pace of change in your GTM. For example,
Weekly (fast-moving signals)
- Activity metrics
- Demo conversion
- Developer activation
- Top-of-funnel health
Monthly (structural insights)
- Funnel conversion
- Pipeline velocity
- Revenue trend
- ICP drift
- POC progression
Quarterly (strategic & board-level)
- Retention
- CAC vs. LTV
- Market expansion
- Pricing impact
Example: a devtool company sees weekly CAC spikes. With few deals, they shift CAC review to quarterly, while reviewing developer activation weekly to catch usability friction faster.
Are we focused on top-line, gross profit, or net profit margin?
Early growth (ARR/MRR) can be misleading. If revenue scales but margins degrade, you may be building a leaky machine. Measure not just what you earn, but what you keep – margins matter more as you scale. For example,
- Gross Margin
- CAC: LTV ratio
- Payback period
- % of revenue requiring high-touch onboarding
Example: a B2B AI platform with $30K ACV realises it is spending $20K onboarding each new customer. They deprioritise expansion, invest in implementation automation tracked via hours per new customer and reduce onboarding cost per customer by 40%
What data do we currently collect - and where are the gaps?
Measurement is only as strong as the data infrastructure behind it. Many early teams rely on CRM data alone, missing context from product analytics, customer feedback, financial reports, or manual processes. What to measure,
- CRM: deal stage, sales velocity
- Product analytics: activation rate, usage
- Support: ticket volume by feature
- Manual: customer feedback logs, pilot satisfaction
Example: A marketplace sees seller churn but cannot explain why. They start tagging support tickets with themes and embed NPS at key milestones. This leads to a new onboarding flow, tracked weekly with churn dropping 15%.
Do we have the right tools to track KPIs effectively?
The point of measurement is action. If your data is hard to access, inconsistent, or fragmented — it will not inform decisions. While your sales stack does not need to be perfect, it needs to be robust enough to ensure accurate and automated tracking. What to use,
- CRM: HubSpot, Pipedrive, Attio, Salesforce (only once you are moving firmly up-market or building a complex enterprise motion)
- Product & User Analytics: Mixpanel, Amplitude, PostHog
- Reporting & Dashboards: Google Sheets, Airtable, Looker Studio, Metabase, Tableau (only when data and reporting needs to get complex)
- Integrations & Data Movement: Zapier, Make, Census, Segment, n8n
Example: a seed-stage company runs sales in Notion and Google Sheets but deals are falling through cracks. By moving to Pipedrive and building a weekly reporting sync, they shorten cycle time and improve pipeline visibility.
Reporting through excel is ok (for a period of time) but aim to systematically report as you grow.
Further Reading: The Top 10 Data Discovery Tools That Get Results by Eyal Katz.
How do our customers define success?
Selling into enterprise means aligning with how your buyers report value internally. If your KPIs do not reflect how your customers, peers, or industry think about success your sales motion will miss the mark. Anchor your metrics in their language by measuring for example,
- Time to Value
- Workflow efficiency
- Cost savings vs legacy process
- % error reduction
Example: an AI data labeling platform sells to compliance teams. Customers care about annotation speed and audit-readiness. The team shifts demo storytelling and metric reporting to focus on time-to-compliance and labeling accuracy, increasing win rate by 30%
Are we measuring what we can control?
Founders often obsess over macro trends or pipeline volume without tracking quality or the actions your team directly influences. Avoid KPIs driven by externalities, and choose metrics tied to internal execution for example,
- Demo conversion
- Time-in-stage per rep
- Customer engagement score
- Deal velocity
Example: a founder tracks “number of inbound leads” weekly. Most are unqualified. By replacing this with a qualified lead conversation rate and adding a stage-specific health check in CRM to diagnose drop-offs, the win rate improves 18%.
Who needs to see what - and when?
Not all metrics are for everyone. You always want metrics that are actionable and understood in context. Sales ops need tactical detail. Founders need narrative. Investors need clarity. Build and tailor layered visibility to serve different stakeholders.
- Sales: weekly dashboards, deal velocity, win/loss
- Founders: monthly board-level metrics, funnel health
- Investors: quarterly performance, key narrative shifts
Example: an AI orchestration platform builds a Notion dashboard for internal KPIs, but overlooks the investor context. They begin sending a simple 5-metric update monthly, tied to key hypotheses which becomes a core asset in the Series A process.
What do we do when metrics are off?
The value of a KPI is in the decision it enables. Predefine actions or hypotheses tied to each metric. If it moves, the team should know what to explore, test, or change - without guessing.
- Low conversion rate - Review qualification
- CAC spike → Audit acquisition mix
- Stalled deals → Review sales scripts or pricing feedback
Example: a sales team sees demo-to-close drop from 40% to 22% in two months. Instead of reacting emotionally, they run win/loss interviews, surface a new competitor objection, and sharpen their positioning. Close rate rebounds to 37% within one quarter.
Final Thought: Metrics Are a Means to Learn, Not to Impress
Do not build a dashboard for your board; build it for your team. Metrics should increase clarity, reduce wasted motion, and compound execution quality. That is how your commercial operations become a strategic advantage.
Further Reading: Will It Make the Boat Go Faster by Ben Hunt-Davis and Harriet Beveridge, Glossary of Key SaaS Terms by Oxx
