Blog · Territory, capacity and quota planning
Prescriptive marketing compares actions against an agreed objective and real business constraints. Work through a marketing allocation example, test its response assumptions, and check the contribution and capacity arithmetic before choosing a budget.
Prescriptive marketing compares possible actions using an agreed objective, response assumptions and the limits the business must respect. The answer is a conditional recommendation: choose this allocation if these assumptions hold and the company can deliver the resulting business. A larger projected return is useful only when its underlying costs and capacity requirements reconcile.
Write the decision before collecting more measures. For example: how should a US industrial supplier divide its next campaign budget between paid search and partner webinars? Specify the budget period, the customer outcome and the contribution horizon.
The broader prescriptive analytics framework applies across business functions. Here the focus is marketing allocation: comparing a few practical actions, identifying what makes one preferable, and agreeing who can approve it.
Do not assume that past channel averages will hold at a higher spending level. Demand, competition and market saturation belong in the review; those are among the questions in the SBA's market research guidance. A campaign can have attractive historical economics and still lack enough reachable buyers to absorb additional spending.
Use campaign expense records, an acquisition register with one row per customer, and customer revenue and variable-cost exports. Join them through approved campaign and customer identifiers. Record acquisition dates so every historical customer has the same observation window.
For this fictional supplier, finance approves the following planning inputs. Contribution covers each acquired customer's first six months after variable fulfillment costs, including onboarding labor, but before campaign spending and corporate overhead. Onboarding hours are also tracked as a capacity requirement; their cost is already included and must not be deducted again.
| Planning input | Paid search | Partner webinars |
|---|---|---|
| Assumed campaign cost per acquired customer (USD) | 1,000 | 500 |
| Six-month contribution per acquired customer before campaign cost (USD) | 2,000 | 1,800 |
| Onboarding hours per acquired customer | 2 | 4 |
| Shared constraint | Available amount |
|---|---|
| Campaign budget (USD) | 60,000 |
| Onboarding hours | 300 |
These are assumptions supplied for the decision, not industry benchmarks. The campaign cost excludes shared sales overhead, so it is narrower than fully loaded customer acquisition cost. Label that difference instead of presenting unlike cost measures as comparable.
Apply the same calculations to every channel and option:
Expected acquired customers = Campaign spending / Assumed campaign cost per acquired customer
Required onboarding hours = Expected acquired customers × Hours per customer
Contribution after campaign spending = Expected acquired customers × Contribution per customer before campaign cost − Campaign spending
The team proposes three alternatives. The table shows the complete calculation for each channel. Dollar amounts are USD; counts and hours are exact under the stated assumptions.
| Allocation | Channel | Spend (USD) | Customers | Hours | Contribution before campaign cost (USD) | Contribution after campaign cost (USD) |
|---|---|---|---|---|---|---|
| Current | Search | 40,000 | 40 | 80 | 80,000 | 40,000 |
| Current | Webinars | 20,000 | 40 | 160 | 72,000 | 52,000 |
| More webinars | Search | 20,000 | 20 | 40 | 40,000 | 20,000 |
| More webinars | Webinars | 40,000 | 80 | 320 | 144,000 | 104,000 |
| Capacity fit | Search | 30,000 | 30 | 60 | 60,000 | 30,000 |
| Capacity fit | Webinars | 30,000 | 60 | 240 | 108,000 | 78,000 |
For the capacity-fit webinar row, 30,000 / 500 = 60 customers; 60 × 4 = 240 hours; and 60 × 1,800 − 30,000 = $78,000 after campaign spending.
Sum the channel rows to compare the alternatives:
| Allocation | Spend (USD) | Customers | Hours | Contribution after campaign cost (USD) | Within capacity? |
|---|---|---|---|---|---|
| Current | 60,000 | 80 | 240 | 92,000 | Yes |
| More webinars | 60,000 | 100 | 360 | 124,000 | No |
| Capacity fit | 60,000 | 90 | 300 | 108,000 | Yes |
The highest projected contribution fails the capacity check. Among these candidates, the capacity-fit allocation is preferable under the original assumptions. This is a comparison of supplied alternatives; it does not establish the best possible allocation across every spending level.
The capacity-fit contribution must reconcile both ways: channel results of $30,000 + $78,000 equal $108,000, and total contribution before campaign cost of $60,000 + $108,000, less $60,000 spending, also equals $108,000. Its 60 + 240 onboarding hours equal the available 300 hours.
Now change only the webinar campaign cost per acquired customer from $500 to $600. Keep spending, customer contribution and onboarding requirements unchanged.
| Capacity-fit sensitivity | Search | Webinars | Total |
|---|---|---|---|
| Spend (USD) | 30,000 | 30,000 | 60,000 |
| Customers | 30 | 50 | 80 |
| Hours | 60 | 200 | 260 |
| Contribution before campaign cost (USD) | 60,000 | 90,000 | 150,000 |
| Contribution after campaign cost (USD) | 30,000 | 60,000 | 90,000 |
The revised webinar calculation is 30,000 / 600 = 50 customers. The proposed allocation now produces $90,000 after campaign spending. Recalculate every competing allocation with the same revised assumption before choosing again; the original current-case result is no longer a fair comparator.
Marketing owns the candidate allocations and campaign assumptions. Finance owns the cost definition and contribution reconciliation. Operations confirms available onboarding capacity, including work already promised to existing customers.
Attributed customer counts do not prove that spending caused those acquisitions. Keep the marketing attribution method fixed when comparing historical evidence, and distinguish attribution from incremental response. NIST describes comparative experiments as a way to gather comparable evidence when choosing between alternatives.
Agree on a limited test and a review date before expanding spending. Report contribution alongside revenue; the marketing ROI and ROAS distinction explains why a revenue return alone cannot settle this decision.
Bring campaign spending, customer outcomes, contribution records and a file of agreed scenario assumptions to Covirage. Covirage maps the columns; its tools compute and check the comparisons, and the external AI model explains the results with every figure traced to its rows. Create a free Covirage account, which takes about a minute, then set up your business and bring a sample of your own data.
See sales insights for the supporting business context.
Predictive work estimates an outcome, such as expected customer response. Prescriptive work compares actions using those estimates and business constraints. A response estimate alone cannot choose a budget because delivery capacity, contribution and management priorities also affect the decision.
No. A team can compare a short list of budget options using explicit assumptions and ordinary arithmetic. Automatic optimization is a separate capability. The essential requirement is that each recommended action has a stated objective, feasible constraints and a checkable calculation.
Start with campaign spending, customers acquired by mature acquisition cohort, customer revenue and variable delivery costs. Add the available service or sales capacity. Keep observed results separate from future response assumptions so the decision does not present an estimate as a recorded outcome.
Reconcile historical costs and outcomes, vary the assumptions that drive the ranking, and test uncertain responses with a controlled comparison where practical. Review the result with finance and the team responsible for delivering the additional business before changing spending.