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Are you spending next year's budget based on last year's results?

A Marketing Budget Allocation Playbook models forecasted pipeline against every reasonable budget distribution and recommends the allocation that maximizes projected return, with scenario modelling for any cut or addition you propose.

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The Challenge

Budgets reflect organizational power, not predicted return

  • Annual planning is political

    Marketing budget allocation is one of the most political conversations in the planning calendar. Every team defends its share. Cuts trigger resistance. Increases trigger lobbying. The resulting allocation reflects internal influence more than projected return.

  • No model connects budget to pipeline

    Most teams cannot quantitatively answer "if we move $300K from events to ABM, what happens to pipeline?". Without a forecasting model, the conversation defaults to vendor commitments, sunk costs, and what worked last year. Forward-looking decisions get made on backward-looking arguments.

  • Program-level decisions are made in isolation

    A single underperforming conference might consume $340K with no measurable pipeline. Without surfacing program-level ROI alongside the channel-level view, those expenditures keep flowing because nobody is forced to compare them to the next-best use of the same dollars.

How eyko Solves It

Forecast pipeline by allocation, model the alternatives

A Marketing Budget Allocation Playbook reads channel and program-level spend, attribution data, and pipeline outcomes. It produces a forecasted pipeline curve for the current allocation and any alternative split, identifies underperforming programs, and recommends the reallocation that maximizes projected return.

Channel Allocation: Current vs Predicted Optimal | What
Executive Summary

Current allocation is 35% events, 28% digital, 22% content, 15% ABM. Predicted optimal is 20% events, 32% digital, 23% content, 25% ABM. The recommended reallocation projects $2.1M in additional pipeline at the same total budget. The events budget includes $340K on conferences with a negative ROI history that the channel-level view does not surface.

Pipeline Forecast by Allocation Scenario
Predicted optimal mix
$6.3M
Recommended phased mix
$5.7M
Current allocation
$4.2M
Events-heavy alternative
$3.6M
All-digital alternative
$3.1M
MetricCurrentBenchmarkStatus
Primary indicatorFlaggedTargetAction needed
Secondary indicatorMonitoringWithin rangeOn track
Trend directionDecliningStableReview required
Recommendations
1Current allocation is 35% events, 28% digital, 22% content, 15% ABM.
2Full analysis available across all connected data sources.

FAQ

Frequently asked questions

Everything you need to know about Channel Allocation: Current vs Predicted Optimal.

Marketing Budget Allocation is an AI-driven analysis that models forecasted pipeline against every reasonable split of your marketing budget across channels and programs. The Playbook recommends the allocation that maximizes projected return, surfaces underperforming programs, and supports scenario modelling for any reallocation under consideration.

The Playbook reads from your finance system (channel and program spend, vendor commitments), marketing automation and ad platforms (campaign and audience-level performance), CRM (attribution and closed-won outcomes), and historical budget records. The richer the program-level granularity, the more precisely the Playbook can attribute ROI to individual line items.

The Playbook fits a forecasting model that combines historical channel response curves with program-level outcomes. For any scenario you propose (shift X dollars from channel A to channel B, cut program C, add program D), it returns the projected pipeline impact and the confidence band. CMOs can compare scenarios side by side before committing to a budget shift.

Yes. The Playbook supports program-level scenarios: cut a specific conference, drop a sponsorship, end a content syndication contract. Each scenario returns the projected pipeline loss, the freed budget, and the recommended reallocation of that budget to the channels and programs with the strongest marginal return. The output is a structured argument for or against the cut, not a binary recommendation.

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