Sales Model

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.

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.

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.

Required pipeline = next-month bookings × the coverage ratioCopy

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.