A demand-gen model is the forward-looking plan that connects every marketing dollar to a pipeline number and a bookings number. Most teams either skip it and spend on vibes, or build a 40-tab spreadsheet nobody reopens. This guide walks you through a tight build: define the revenue target, back into pipeline, break pipeline into channels, assign conversion rates and costs, pressure-test the plan with sales, and ship it with a quarterly review cadence so the model keeps up with reality.
Time: 1-2 weeks
A demand-gen model starts at the end. Pull the bookings target for the planning window from finance and split it by segment, product line, or motion. Divide the marketing-sourced share of bookings by the historical win rate on marketing-sourced opportunities to get the sourced-pipeline target. Then multiply by the pipeline coverage ratio your sales leader signs off on, usually three to four times for mid-market and four to six times for enterprise. That final number is the pipeline target your model has to deliver. Write it at the top of the plan and reference it on every tab. If the pipeline target does not reconcile to the bookings number through win rate and coverage, the rest of the model is fiction.
Once the pipeline target is locked, work backward through the funnel using your trailing conversion rates. Divide the opportunity target by SQL-to-opp conversion to get the SQL target. Divide SQLs by MQL-to-SQL to get the MQL target. Divide MQLs by lead-to-MQL to get the lead target. Divide leads by visitor-to-lead to get the traffic target. Do this per segment, not in aggregate, because enterprise and SMB funnels convert at very different rates and mixing them hides the real demand gap. Lay the whole thing out as a stage-by-stage table with target volumes by month so your channel plan has concrete numbers to hit, not vague growth language.
Now split the lead and MQL targets across the channels that will produce them. For each channel, start from last quarter's actual sourced-lead and MQL volume, then set a realistic growth or decline rate per channel based on budget, saturation, and team capacity. Paid search can scale 20-40 percent with budget. Organic content scales on a lag of two to three quarters. Events produce bursty pipeline. Partner and co-marketing scale with partner activity, not your spend. Document the assumption behind every channel number in a column next to it. Nobody should be able to open the model six months later and wonder where a number came from.
A demand-gen model that only shows volume gets killed in the first finance review. Add a cost column to every channel line: planned spend for the quarter, planned headcount cost if the channel is people-heavy, and planned agency or vendor fees. Divide spend by lead volume for cost per lead, by MQL volume for cost per MQL, by SQL volume for cost per SQL, and by opportunity volume for cost per opp. Compare the cost per opp against average deal size times gross margin. If a channel's cost per opp is more than 20-30 percent of the gross profit on the deal it is likely to close, it is a candidate for cuts, not growth. Show the math inline so finance can audit it.
Group the channel lines into four buckets so the model tells a story a leadership team can argue about: paid acquisition (search, social, display, retargeting), content and SEO (editorial, SEO, product-led content, lifecycle), events and field (owned events, trade shows, dinners, webinars), and partner and ecosystem (co-marketing, marketplace, integration partners, referrals). For each bucket, show total planned spend, total sourced pipeline, blended cost per opportunity, and share of the total pipeline target. Mature B2B teams usually land somewhere in the 30/30/20/20 range across these four buckets, with heavier content for product-led motions and heavier events for enterprise. Use the mix view to argue tradeoffs, not the line items.
A demand-gen model you built alone is a draft. Walk the model through sales leadership first, line by line. They need to agree the MQL volume is accepted, the SQL definition still holds, and the pipeline target matches the capacity of the rep team. If reps cannot physically work the SQL volume you are planning to deliver, the plan has to shrink or sales has to hire. Then walk the model through finance. They need to accept the spend envelope, the cost per opportunity math, and the implied gross margin. Record every concession in the plan document so you can show the chain of trade-offs when someone questions a number in month two.
A demand-gen model that only lives in a spreadsheet drifts the moment the quarter starts. Wire the plan numbers into the systems that produce the actuals. Push the monthly targets per channel into the CRM as campaign goals so sourced-pipeline reports can show planned versus actual. Build a dashboard that shows cost per outcome by channel using live spend from the ad platforms and live pipeline from the CRM. Set alerts for any channel that drops below 70 percent or runs above 130 percent of its monthly target. Strkr AI can watch the drift between planned and actual pipeline and surface the two or three channels that need attention first so the team is not reading seventeen charts to find a problem.
A demand-gen model is a living document, not a one-time plan. Every quarter, pull actuals against plan at the channel and stage level, update conversion rates with the last 90 days of data, and rebuild the forward look. Kill channels that missed the cost per opp target twice in a row unless there is a documented strategic reason. Expand channels that beat plan if the capacity exists to scale them. Lock in a 60-minute review meeting with sales and finance for every quarter so the reforecast is a shared decision, not a marketing memo. Treat the model like a budget, not a dashboard, and the whole revenue team starts speaking the same language about demand.
A demand-gen model is a forward-looking plan that maps marketing spend across channels to pipeline and bookings outcomes. It defines channel mix, volume assumptions, conversion rates between funnel stages, and cost per outcome, and it is reviewed quarterly against actuals.
A budget is a spend envelope. A demand-gen model is the plan that connects that spend to a pipeline target through conversion rates and cost assumptions. A budget tells you what you can spend. A model tells you what that spend is supposed to produce and by when.
Use your own trailing six-month conversion rates per segment: lead to MQL, MQL to SQL, SQL to opportunity, and opportunity to closed-won. If you lack history, start with industry benchmarks for your segment and sales motion, then replace them with your own rates as soon as you have 90 days of data.
Review the model quarterly at minimum, with a formal 60-minute working session that includes sales and finance. Update conversion rates, reforecast the next two quarters, and decide which channels to kill, expand, or leave flat. Monthly pulse checks on channel-level pacing fill the gap between reviews.
Most mature B2B teams land near a 30 percent paid, 30 percent content and SEO, 20 percent events and field, and 20 percent partner and ecosystem split. Shift heavier toward content for product-led motions and heavier toward events and partner for enterprise. Treat the mix as a benchmark to argue against, not a formula to copy.
The head of demand gen or marketing operations owns the model. The CMO sponsors it. The sales leader signs off on pipeline coverage and SQL volume. Finance signs off on the spend envelope and cost per opportunity math. Shared ownership of inputs is what keeps the model honest.
Strkr connects channel spend, pipeline, and bookings in one place so your demand plan stays reconciled to the number the CFO cares about.
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