Where should you spend next quarter — by market — and how confident is the recommendation?
A continuous view of where the next dollar should go, by market, grounded in your actual revenue drivers, attribution, competitor density, and trade-area data.
The problem
Your CMO needs to recommend Q3 marketing investments across 40 units. Denver underperformed in Q2 but has the highest cannibalization risk if you go heavier. Austin overperformed but the trade area is saturating. Tampa has three new competitors that opened since Q1. Every market has a different story and the data lives in eight different tools. The marketing budget-planning platforms (Allocadia, Plannuh, Hive9, Aprimo, Workfront, BrandMaker) run $25,000 to $500,000 a year and are built to manage the budget itself, not to recommend where the next dollar should go by market. The financial-planning suites (Anaplan, Pigment, Vena, Mosaic) are built for finance, not for marketing ops. The predictive-analytics platforms (DataRobot, H2O.ai, Dataiku, Alteryx) require a data-science team to operate. The attribution and MMM tools recommend on the marketing-mix slice only. The in-house version takes two to four weeks of manual scenario work per market per quarter, and falls apart past ten locations or five scenarios.
What success looks like
Every market continuously surfaces a recommendation for where the next dollar should go, grounded in the actual revenue drivers for that market, the attribution you have on each channel, the competitor density, the cannibalization risk, the demographic profile, and the foot-traffic capture rate. Each recommendation carries a confidence level and a downside risk estimate. You can run scenarios (what if Texas grows 12%? what if a new competitor enters Phoenix?) and see the recommendation update against your actual data. Multi-banner operators get per-market and across-banner recommendations from one view. Every recommendation is preserved with the data it was based on, so the board, an investor, or a private-equity sponsor can ask how a recommendation was produced and get an answer.
How most operators solve this today
Six categories touch this. None of them combine the multi-location reality with the marketing-mix detail.
Marketing budget-planning platforms (Allocadia, Plannuh, Hive9, Aprimo, Workfront, Welcome, BrandMaker, Smartsheet)
$25,000 to $500,000+ per year
Built to manage the budget. They do not recommend where the next dollar should go based on what each market actually needs.
Financial-planning suites (Anaplan, Pigment, Vena, Mosaic, Cube, Causal, Datarails, Workday Adaptive Planning, OneStream, Planful)
$45 per month to $500,000+ per year
Built for finance. Marketing ops rarely has access. Not grounded in marketing-mix detail.
Predictive-analytics platforms (DataRobot, H2O.ai, Dataiku, Alteryx, RapidMiner)
$5,000 to $200,000+ per year
Real capability. Requires a data-science team to operate.
Attribution and MMM tools (Rockerbox, Northbeam, Triple Whale, Nielsen MMM, Analytic Partners, Marketing Evolution)
$100 per month to $500,000+ per year
Focused on the marketing-mix slice. Not full per-market scenario coverage.
In-house marketing and finance analysts
$90,000 to $180,000 per year per analyst, plus two to four weeks per market per quarter
Manual scenario sessions. Falls apart past ten locations or five scenarios.
Build it in-house
Excel and Google Sheets
Works for the first quarter. Breaks down by the second.
What changes when this is an agent skill
Each market gets a continuous recommendation for where the next dollar should go, grounded in the data that actually drives outcomes in that market — the revenue drivers, the attribution by channel, the competitor density, the cannibalization risk, the demographic profile, the foot-traffic capture rate, the recent KPI roll-ups. Recommendations come with a confidence level and a downside risk estimate, so the team is not chasing a number that the data does not support. You can run scenarios — what if Texas grows 12%, what if a new competitor opens in Phoenix, what if you cut paid by 20% — and see the recommendation move against your actual data, with the new confidence and downside. Multi-banner operators see per-market and across-banner recommendations from one view, with each banner's reality respected. Every recommendation is preserved with a timestamp, the scope, the drivers and scenarios it used, the confidence level, and the reviewer if one was involved — so when a board, investor, or private-equity sponsor asks how a recommendation was produced, the answer is on file. Allocadia and Plannuh remain useful for budget management. Anaplan and Pigment remain useful for enterprise finance planning. DataRobot remains useful if you have a data-science team. This is the recommendation layer that grounds the spend conversation in market reality.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Per-Location Rollup + Executive Reporting Agent
CFO / board / PE-sponsor / franchisee-council reporting surface — cohort-framed rollups, drivers analysis, forward-looking recommendations.
FAQ
- What does a recommendation actually look like?
- A specific spend recommendation for a specific market — for example, 'shift $40,000 from Meta to local SEM in Denver next quarter' — with a confidence level, a downside risk estimate, and the data the recommendation is based on.
- How is this different from Allocadia, Plannuh, or Aprimo?
- Those manage the budget once you have decided how to allocate it. This recommends how to allocate it, based on what each market actually needs.
- How is this different from Anaplan, Pigment, or Mosaic?
- Those are enterprise finance-planning suites for finance teams. This is for the marketing-ops team and is grounded in the marketing-mix detail finance tools usually skip.
- How is this different from DataRobot, H2O.ai, or Dataiku?
- Those are predictive-analytics platforms that require a data-science team. This is built for marketing ops and runs continuously without that overhead.
- How is this different from Rockerbox or Nielsen MMM?
- Those focus on the marketing-mix slice. This covers the full picture — drivers, attribution, competitor density, cannibalization risk, demographics, foot traffic — per market.
- Can I run scenarios?
- Yes. What if Texas grows 12 percent. What if a new competitor opens in Phoenix. What if you cut paid by 20 percent. The recommendations and confidence move against your actual data.
- Does it work for multi-banner operators?
- Yes. Per-market and across-banner recommendations show up in one view, with each banner's reality respected.
- How does this hold up under board, investor, or PE scrutiny?
- Every recommendation is preserved with the data it was based on, the scenarios it considered, the confidence level, and the reviewer. When someone asks how a recommendation was produced, the answer is on file.