What does a sales analyst do?

The stages a prospect passes through from first contact to closed deal — lead, qualified, opportunity, proposal, negotiation, won — with fewer surviving each step.

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The share of opportunities that move from one stage to the next. Multiplied along the funnel, the stage rates give the overall lead-to-close rate.

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Deals won as a share of deals closed (won plus lost). The single most-used measure of how well the sales motion converts.

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The average number of days from an opportunity's creation to its close, won deals only. How long the motion takes.

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Keeping the CRM honest: close dates in the future, stages current, dead deals closed-lost, amounts real. Everything downstream — forecast, coverage, velocity — depends on it.

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Open pipeline for a period divided by the quota or target for that period. How many dollars of opportunity sit behind each dollar of target.

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Each open opportunity's value multiplied by its stage's historical win probability, summed. An expected value of the pipeline rather than its face value.

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The share of leads that become qualified opportunities — the handoff from marketing to sales, and the first place funnel math breaks.

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A marketing-qualified lead has shown enough interest or fit by marketing's definition; a sales-qualified lead is one sales has accepted as worth pursuing. The two definitions are where marketing and sales argue.

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Deals whose close date moved out of the period they were forecast in. The share of a forecast that was real but late.

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How long each open deal has sat in its current stage compared with the typical time at that stage. Old deals are stuck deals.

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How fast the pipeline turns into revenue: opportunities × average deal value × win rate, divided by sales cycle length. Dollars per day the motion produces.

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Studying the deals that were lost — to whom, at what stage, for what stated reason — to find the fixable patterns.

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Ranking leads by fit (industry, size, role) and intent (visits, downloads, demo requests) so sales works the likeliest first.

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Labels reps and managers put on open deals — commit, best case, pipeline, omitted — that say how confident they are the deal closes this period.

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Regular within-year patterns in bookings — a Q4 surge, a summer slowdown, month-end spikes — that repeat and should be planned for, not explained away.

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How close the forecast called at a point in the quarter came to what actually closed. Measured week by week so the process can improve.

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The weekly process that aggregates rep-level calls through managers to the company number, with a finance or ops view beside it.

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Projecting the period from what has closed so far and the historical pattern of when deals land — the share usually closed by week six, and so on.

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Bookings are signed contracts; revenue is recognised as delivered. The bookings forecast is what sales calls; the revenue forecast is what finance reports. They differ by timing.

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A snapshot counts what is in the pipeline today; a cohort follows the deals created in a period until they resolve. Only cohorts give true conversion rates and cycle times.

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The total value of contracts signed in a period. The sales team's output measure, before any revenue is recognised.

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Bookings divided by the number of deals won. One of the four velocity levers, and the one most affected by mix.

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Annual contract value is a contract's value normalised to one year; total contract value is everything over the full term, including one-time fees.

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Splitting bookings by source: new logos, expansion of existing customers (upsell, cross-sell), and renewals of existing contracts. Each has different economics and usually different owners.

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How signed contracts change annual recurring revenue: new ACV adds, expansion adds, churn and downgrades subtract; one-time fees never count.

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Revenue is recorded as the service is delivered, not when signed or paid. A subscription is spread over its term; an implementation is recognised when done.

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What is invoiced in a period. It sits between bookings (signed) and revenue (recognised), and drives cash.

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Contracts whose value steps up over the term — a discounted first year, seats added on a schedule. Bookings, ACV, and ARR each treat the ramp differently.

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A rep's bookings as a share of their quota. The distribution across the team says as much as the average.

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A rep's total pay at exactly 100% of quota: base salary plus target variable. The pay mix is the split between the two.

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The share of each booked dollar paid to the rep: target variable divided by quota gives the base rate.

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Assigning each rep a bookings target from the company plan, territory potential, ramp, and history, so the sum of quotas covers the plan with a margin for misses.

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Higher commission rates above quota (accelerators) and lower rates below a threshold (decelerators), so overachievement pays disproportionately.

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How long a new rep takes to reach full productivity, and the reduced quota (a ramped quota) they carry in the meantime.

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Short-term sales incentives — a bonus for selling a product, closing before a date, or hitting an activity target — layered on top of the comp plan.

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Recovering commission already paid when the deal it was paid on churns, cancels, or never pays, within a defined window.

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The full set of rules — mix, rate, accelerators, thresholds, SPIFs, clawbacks, payment timing — that decides what reps are paid for and therefore what they do.

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Splitting the market and the sales motion by size or type — SMB, mid-market, enterprise — because each has a different cycle, deal size, and cost to serve.

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The description of the accounts that buy fastest, pay most, and stay longest — industry, size, tooling, trigger events — derived from the customers you already have.

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Dividing accounts among reps — by geography, industry, size, or named list — so potential is balanced and coverage has no gaps or overlaps.

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Total addressable market is everyone who could buy; serviceable addressable market is the part you can reach; serviceable obtainable market is what you can realistically win.

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Ranking accounts by fit and potential so territories are balanced and reps prioritise, using firmographics, install base, and intent.

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Mapping which products each existing customer has and has not bought, to find expansion opportunity inside the base.

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Which motion covers which accounts — field, inside, partner, self-serve — matched to deal size and cost of sale.

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Measuring the gap between list price and what deals actually close at, by rep, segment, and quarter, and what the discount bought.

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The average realised price per unit or seat, after discounts. Falling ASP with rising volume is a mix shift or a discounting problem — find out which.

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A discount comes straight off the margin: 10% off a 40%-margin sale removes a quarter of the profit and needs about 33% more volume to compensate.

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Net price achieved as a share of list. The mirror of discount, aggregated: how much of the price book the team actually collects.

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Splitting a change in bookings into more units, higher prices, and a different blend of products or segments.

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Annual up front, quarterly, monthly, net 30, net 60 — the billing and payment terms in a contract, which decide when the cash arrives and how much working capital the deal consumes.

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A review point for non-standard deals — discounts above a threshold, unusual terms, custom contracts — so pricing discipline holds without slowing every deal.

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Contracts longer than a year, usually with a discount for the commitment. They lock in revenue, reduce churn risk, and complicate ACV, quota credit, and cash.

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Total sales and marketing cost in a period divided by new customers won in it. What it costs to buy a customer, fully loaded.

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The share of customers or revenue lost in a period. Logo churn counts customers; revenue churn counts dollars; gross revenue retention excludes expansion.

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The gross profit a customer generates over their whole relationship: revenue per period, times gross margin, divided by churn.

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Lifetime value divided by acquisition cost — the return on each dollar spent winning a customer. Three is the usual healthy line.

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Months of a customer's gross profit needed to earn back the cost of acquiring them. The cash question the LTV ratio ignores.

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Revenue from last year's customers as a share of what they paid then, after churn, downgrades, and expansion. Above 100% means the base grows without new logos.

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Following the customers won in each period and measuring how many, and how much revenue, remain over time — the only honest view of whether customers stay.

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Additional revenue from existing customers — more seats, higher tiers, added products — measured as a share of starting ARR.

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How much of revenue comes from the largest customers. One at 20% or more of revenue is a risk to the whole business, not just a big account.

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Calls, emails, meetings, demos, and proposals per rep per period — the inputs that precede pipeline.

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Bookings per fully ramped rep per period — the yield of the sales machine per unit of capacity.

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The number of deals a rep closes in a period. With average deal size, it explains productivity; with pipeline, it explains coverage.

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The share of reps who leave in a year, voluntary and not. Each departure costs the ramp time of the replacement plus the pipeline that goes with them.

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How many sales development reps support each account executive with qualified meetings. The ratio sets how much pipeline is sourced versus self-generated.

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Working out how many reps, hired when, at what ramp and attrition, produce the bookings plan. The bridge from the revenue target to the hiring plan.

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When to hire each rep so that, after ramp, capacity meets the plan — with the cost landing months before the bookings do.

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Whether the fields the reports depend on — stage, amount, close date, source, owner — are complete, current, and mean the same thing to everyone.

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The standing view for a sales leader: bookings versus target, pipeline coverage, conversion by stage, forecast versus actual, and rep attainment.

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The gap between planned and actual bookings, explained by its drivers — deal count, deal size, win rate, slippage — rather than just reported.

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Conversion, cycle, deal size, and win rate broken out by where the lead came from — inbound, outbound, partner, event.

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The handful of metrics that actually drive bookings — coverage, conversion, cycle, deal size, attainment — each with an owner and a target.

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A per-rep view of attainment, pipeline, conversion, activity, and forecast accuracy, used for coaching and for spotting who needs help before the quarter is lost.

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Assigning credit for a deal to the marketing and sales touches that produced it — first touch, last touch, or multi-touch.

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Tracking whether the customers a rep sold stay, so the comp plan rewards deals that last rather than deals that sign.

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One function owning the systems, data, process, and planning across marketing, sales, and customer success, so the funnel is measured end to end by one set of definitions.

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The weekly meeting where the sales leader reviews the number with managers: what is committed, what slipped, what is at risk, and what has to happen this week.

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Working backward from the bookings target through win rate and coverage to the pipeline that must be created each month, and where it comes from.

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Shared definitions, a shared funnel, and shared targets between the team that generates demand and the team that closes it.

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CRM, engagement, enablement, forecasting, and CPQ tools — and the integrations between them — that the sales motion runs on.

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Turning the revenue target into segments, territories, quotas, hiring, and comp — with the capacity model as the spine that connects them.

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The few numbers a board needs on sales: bookings versus plan, pipeline coverage for next quarter, win rate and cycle trends, rep productivity, and the capacity plan.

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How much new ARR a dollar of sales and marketing spend produces — the magic number and its cousins. Whether growth spend earns its keep.

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