# Sales Model A 12-month top-down forecast: annual revenue target spread by quarterly seasonality, deals required at average ACV, leads / MQLs / SQLs / opportunities back-solved from wins, and an open-pipeline rolling balance with forward coverage ratio per month. - Canonical: https://finamodel.com/templates/sales-model - Excel download: https://finamodel.com/templates/sales-model.xlsx - Category: Corporate Finance - Model type: Operating model - Difficulty: Beginner - Audiences: CFOs & FP&A, Founders & operators, CROs, Sales operations, Founders - Tags: sales, forecast, pipeline, funnel, coverage ## Overview A sales model is a top-down forecast that takes an annual revenue target, an average deal size, and a win rate and back-solves the monthly bookings plan, the funnel volume required at every stage, and the open-pipeline coverage needed to land it. The workbook is built around an Assumptions sheet that holds every driver as a named range, a Targets sheet that owns the bookings split, a Funnel sheet that walks the conversion stages from wins back up to leads, a Pipeline sheet that tracks coverage as a rolling balance, and a Summary sheet that condenses the year into a one-page rollup. The Targets sheet uses four editable quarterly weights summing to 100% to spread the annual target across 12 months: each period weight = quarter weight / 3, bookings target = annual target × period weight, and deals required = bookings target / average deal size. The Funnel sheet starts with wins required (= deals required) and divides up the stack: opportunities required = wins / win rate, SQLs = opportunities / SQL-to-Opp, MQLs = SQLs / MQL-to-SQL, and leads = MQLs / Lead-to-MQL. The full lead-to-win ratio is the inverse product of all four conversion rates. The Pipeline sheet pulls bookings target from Targets, computes required pipeline (target × coverage ratio) and pipeline burned (target / win rate), then runs a rolling balance: opening (M1 from Assumptions, then prior closing), added per month at the average monthly burn rate (annual target / win rate / 12) so closing returns to opening over the full year, and a forward coverage ratio (closing / next-month target) that flexes with seasonality. The Summary sheet rolls the workbook into a one-page view: annual bookings target, deals required, leads required, average and M12 forward coverage ratio, M12 closing pipeline, leads-per-win for the full funnel, and the M12 monthly run-rate. CROs, CFOs, and sales operations use the template for annual planning, GTM-finance reviews, and pressure-testing the realism of an annual quota against the marketing engine that has to feed it. ## What's included - Annual revenue target back-solved into a 12-month bookings plan with quarterly seasonality - Funnel back-solve from wins to opportunities, SQLs, MQLs, and leads required per month - Pipeline coverage analysis with required pipeline (target × coverage), monthly added, burned - Pipeline rolling balance: opening + added − burned = closing per month - Forward coverage ratio (closing pipeline / next-month bookings target) - Annual rollup: bookings, deals, leads, average coverage, M12 pipeline, year-end run-rate - 12-month bookings target driven by annual target and quarterly seasonality weights - Deals required = bookings target / average deal size, with cumulative line - Funnel back-solve from wins to opportunities to SQLs to MQLs to leads required per month - Pipeline coverage analysis: required pipeline, monthly added, burned, opening / closing balance - Forward coverage ratio per month (closing pipeline divided by next-month bookings target) ## Sales Model Template: How the Forecast and Capacity Planning Work This sales model template provides a 12-month top-down B2B forecast, translating an annual revenue target into monthly bookings, a segment-aware funnel, and capacity checks. It is designed for a Head of Sales or CFO to align sales plans with resource needs. The public download is a values-only preview, not a live calculator. ### Assumptions and scenario-driven drivers The model's behavior is steered by a single scenario toggle that selects between Base, Bull, and Bear cases. This choice simultaneously adjusts the annual revenue target, average deal size, win rate, pipeline coverage ratio, and marketing cost per lead, ensuring all downstream calculations stay consistent with the chosen scenario. - More than 60 named ranges feed the model, covering seasonality weights, capacity parameters, unit economics, and benchmark thresholds. Segment-level inputs are configured separately on the Segments sheet, including share of plan, average contract value, win rate, sales cycle length, and conversion rates from leads to MQLs, SQLs, and opportunities for Enterprise, Mid-Market, and SMB. - Users can also choose between demand-driven and average pipeline addition modes, and whether to use quarterly or monthly seasonality weights, giving control over the shape of the plan. ### From revenue target to bookings and segment deals The annual revenue target is first converted into a monthly bookings plan using seasonality weights. If monthly weights are active, each month's share is used directly; otherwise, quarterly weights are spread evenly across their months. - The total bookings target for each month is then split across the three segments based on their share of plan. For each segment, the number of deals required is calculated by dividing segment bookings by the segment's average contract value. - This ensures that deals are sized appropriately for each customer type rather than using a blended average. The total deals target is simply the sum of the three segment deal counts, preserving the distinct deal economics of each segment. This approach makes the plan sensitive to changes in segment mix and pricing. ### Funnel back-solve and pipeline coverage mechanics Each segment's deals are converted into a full funnel using its own conversion rates and sales cycle. Wins targeted in a given month are based on the bookings target from a future month offset by the segment's sales cycle, so leads created now drive wins later. - Opportunities are back-solved from wins using the segment win rate, then SQLs, MQLs, and leads are derived using the respective conversion ratios. The Pipeline sheet tracks the open pipeline needed to support future bookings. - Required pipeline equals next-month bookings multiplied by the coverage ratio. Pipeline burned is bookings divided by the dollar win rate, and pipeline added is either demand-driven or set to a flat average. The closing balance rolls forward and the achieved forward coverage ratio shows whether enough pipeline exists for upcoming targets, with December using a next-year target for the forward look. Calculation summary: ```text Required pipeline = next-month bookings × the coverage ratio ``` ### Capacity check and unit economics outputs The model includes a capacity assessment to compare sales headcount against the plan. It calculates the number of account executives each month, starting from an opening count and adding hires in specific months. - Each cohort of hires ramps according to a tenure-based schedule, and ramped headcount is multiplied by annual quota divided by twelve to estimate productive capacity. The gap between the bookings target and productive capacity highlights under- or over-capacity, and the additional AEs needed shows the shortfall in headcount. - On the unit economics side, the model computes customer acquisition cost from commissions and marketing spend, then derives CAC payback, the Magic Number, and the LTV to CAC ratio. A bookings-to-revenue bridge shows total contract value, new ARR, and recognized first-year revenue, offering a view of how bookings translate into revenue. ## Built for annual sales planning A revenue target is the easy part - landing it depends on funnel volume and pipeline coverage that hold up under seasonality. This template makes both visible side-by-side from a single set of inputs. ## Designed for sales-finance alignment Every input is a single named-range cell, so a CFO and a CRO can flex win rate, deal size, or coverage in front of each other and watch the leads required and pipeline coverage update in lockstep. ## Audit-friendly mechanics Every formula is one or two operations, every Assumptions row is referenced downstream, and the workbook passes static-value, self-reference, dead-assumption, and unused-named-range scans. ## Built for annual sales planning A revenue target is the easy part - landing it depends on funnel volume and pipeline coverage that hold up under seasonality. This template makes both visible side-by-side from a single set of inputs. ## Designed for sales-finance alignment Every input is a single named-range cell, so a CFO and a CRO can flex win rate, deal size, or coverage in front of each other and watch the leads required and pipeline coverage update in lockstep. ## Audit-friendly mechanics Every formula is one or two operations, every Assumptions row is referenced downstream, and the workbook passes static-value, self-reference, dead-assumption, and unused-named-range scans. ## Workbook structure ### Cover Workbook overview, sheet legend, and tab-colour key for navigation. - Title and scope framing - Sheet-by-sheet purpose summary - Tab-colour legend ### Assumptions Every driver in one sheet: annual plan, pipeline coverage, funnel conversion, quarterly seasonality. - Annual revenue target, average deal size, win rate - Coverage ratio target, opening pipeline, sales cycle - Lead-to-MQL, MQL-to-SQL, SQL-to-opportunity conversion - Q1 / Q2 / Q3 / Q4 seasonality weights summing to 100% ### Targets Monthly bookings target, deals required, and cumulative lines. - Period and quarter labels driven by formula - Period weight = quarter weight ÷ 3 - Bookings target = annual × period weight - Deals required = bookings target ÷ avg deal size - Cumulative bookings and cumulative deals ### Funnel Top-of-funnel volume back-solved from wins required. - Wins required per month (= deals required from Targets) - Opportunities = wins ÷ win rate - SQLs = opportunities ÷ SQL-to-Opp - MQLs = SQLs ÷ MQL-to-SQL - Leads = MQLs ÷ Lead-to-MQL ### Pipeline Coverage analysis with rolling balance and forward coverage ratio. - Required pipeline = bookings target × coverage ratio - Pipeline burned = bookings target ÷ win rate - Pipeline added = annual target ÷ win rate ÷ 12 - Opening + added − burned = closing per month - Forward coverage = closing ÷ next-month target ### Summary Annual rollups: bookings, deals, leads, average coverage, year-end run-rate. - Annual bookings target, deals required, leads required - Average and M12 forward coverage ratio - M12 closing pipeline carried into next year - Leads-per-win for the full funnel - M12 monthly run-rate and a seasonality-weights check row ### Cover Workbook overview, sheet legend, and tab-colour key for navigation. - Title and scope framing - Sheet-by-sheet purpose summary - Tab-colour legend ### Assumptions Every driver in one sheet: annual plan, pipeline coverage, funnel conversion, quarterly seasonality. - Annual revenue target, average deal size, win rate - Coverage ratio target, opening pipeline, sales cycle - Lead-to-MQL, MQL-to-SQL, SQL-to-opportunity conversion - Q1 / Q2 / Q3 / Q4 seasonality weights summing to 100% ### Targets Monthly bookings target, deals required, and cumulative lines. - Period and quarter labels driven by formula - Period weight = quarter weight ÷ 3 - Bookings target = annual × period weight - Deals required = bookings target ÷ avg deal size - Cumulative bookings and cumulative deals ### Funnel Top-of-funnel volume back-solved from wins required. - Wins required per month (= deals required from Targets) - Opportunities = wins ÷ win rate - SQLs = opportunities ÷ SQL-to-Opp - MQLs = SQLs ÷ MQL-to-SQL - Leads = MQLs ÷ Lead-to-MQL ### Pipeline Coverage analysis with rolling balance and forward coverage ratio. - Required pipeline = bookings target × coverage ratio - Pipeline burned = bookings target ÷ win rate - Pipeline added = annual target ÷ win rate ÷ 12 - Opening + added − burned = closing per month - Forward coverage = closing ÷ next-month target ### Summary Annual rollups: bookings, deals, leads, average coverage, year-end run-rate. - Annual bookings target, deals required, leads required - Average and M12 forward coverage ratio - M12 closing pipeline carried into next year - Leads-per-win for the full funnel - M12 monthly run-rate and a seasonality-weights check row ## Features - **Top-down with funnel back-solve:** An annual target and four conversion rates are all that's needed to land monthly bookings, deals, and the lead volume marketing must deliver to feed the plan. - **Quarterly seasonality:** Four editable quarter weights summing to 100% control how the annual target spreads across the year, so a Q4-heavy enterprise plan or a flatter consumption plan are both one parameter change away. - **Pipeline coverage as a rolling balance:** Pipeline opening + added − burned = closing each month, with achieved coverage tracked against the next-period target so the forward coverage line tells the real story instead of a single static ratio. ## Use cases - **Annual sales planning:** Pressure-test an annual revenue target by flexing win rate, deal size, and conversion rates to see if the implied lead volume is achievable for the marketing budget on hand. - **Sales operations review:** Use the Pipeline sheet's coverage ratio line to check whether opening pipeline and the steady-state added rate keep coverage above target through the seasonally heavy Q4 close. - **CFO go-to-market check:** Bring the Summary sheet to a finance + GTM joint review: does the funnel ratio, average coverage, and year-end run-rate triangulate against the headcount plan in the hiring model? ## Frequently asked questions ### What is a sales model? A sales model is a top-down forecast that takes an annual revenue target and back-solves the monthly bookings plan, the funnel volume needed at each stage (lead, MQL, SQL, opportunity, win), and the open-pipeline coverage required to land it. It is the planning view sales operations and FP&A use before committing to an annual quota. ### How is pipeline coverage calculated? Required pipeline per month = bookings target × coverage ratio (3.0x by default). Pipeline burned per month = bookings target ÷ win rate (the dollar value of opportunities that reach a decision). Pipeline added per month = annual target ÷ win rate ÷ 12 (the average monthly burn rate, so over the full year added equals burned and pipeline returns to its opening balance). ### Why does the achieved coverage ratio fluctuate through the year? Because pipeline added is held at the average monthly burn rate while bookings demand follows the seasonality weights. In light quarters the pipeline builds up and coverage rises; in the heavy Q4 quarter pipeline drains faster than it is added, and coverage tightens going into year-end. ### What is the difference between win rate and the funnel conversion rates? Win rate is the opportunity-to-closed-won conversion (typical SaaS range: 20–30%). The funnel conversion rates are upstream: lead-to-MQL, MQL-to-SQL, and SQL-to-opportunity. Total funnel conversion (lead-to-win) is the product of all four, which the Summary sheet exposes as leads-per-win. ### How does this differ from a sales-rep-forecast model? This is a top-down model: it starts with an annual revenue target and works down to leads. A sales-rep-forecast is bottoms-up: it starts with a rep ramp curve and per-rep quota and works up to total bookings capacity. Use this template for the annual plan; pair it with sales-rep-forecast for the headcount and capacity check. ## Related templates - [Hiring Model](https://finamodel.com/templates/hiring-model) - [SaaS MRR/ARR Forecast Model](https://finamodel.com/templates/saas-mrr-arr-model) - [Budget vs Actuals Tracker](https://finamodel.com/templates/budget-vs-actuals)