What is the right marketing mix in Denver versus Austin versus Tampa?
Continuous, per-market marketing mix modeling that pulls from your real attribution, foot-traffic, and call data — not a $700,000 quarterly engagement.
The problem
Your 60-location urgent-care brand spends $4.2M a year on marketing — $1.8M on Google Ads, $900K on Meta, $600K on direct mail, $400K on radio, $300K on out-of-home, $200K on Klaviyo. The CFO and the CMO want to know what the optimal mix looks like in Denver versus Austin versus Tampa, and they want an updated answer continuously, not once a year. The MMM platforms (Rockerbox, Northbeam, Triple Whale, Nielsen MMM, Analytic Partners, Marketing Evolution, Recast, LiftLab, Mass Analytics) cost $25,000 to $500,000+ a year and handle brand-wide or single-banner work, not per-market for a multi-location operator. The enterprise MMM consultancies (Bain Consumer Insights, McKinsey QuantumBlack, Ipsos MMA, Kantar) run $100,000 to $1,500,000 per engagement on a quarterly cadence — by the time the report lands, the data is three months stale. The open-source Bayesian frameworks (Meta Robyn, PyMC Marketing) require a data scientist who can run Stan and Bayesian regression. The in-house version takes a senior MMM data scientist at $130,000 to $240,000 a year plus six to sixteen weeks per cycle. Excel regressions fall apart past five channels or any non-linear adstock decay.
What success looks like
Every market gets a continuously updated marketing mix model, with the per-channel contributions and the recommended allocation for the next planning period. Denver's optimal mix is separate from Austin's and Tampa's. The model pulls from your actual data — call-tracking attribution, foot-traffic capture rate, identity-resolved customer journeys, POS receipts, attribution roll-ups — instead of stitching together a one-off CSV every quarter. Multi-banner operators see per-market MMM across banners with each banner's spend and outcomes kept distinct. Every run, every coefficient, and every scenario is preserved with the methodology, so when a board, investor, private-equity sponsor, or state attorney general asks how a number was produced, the answer is on file.
How most operators solve this today
Six categories touch MMM. None of them do continuous per-market work without a data-science team or a consultancy invoice.
MMM platforms (Rockerbox, Northbeam, Triple Whale, Nielsen MMM, Analytic Partners, Marketing Evolution, Recast, LiftLab, Mass Analytics, Hawke Atlas)
$100 per month to $500,000+ per year
Brand-wide or single-banner MMM. Not per-market for multi-location operators.
Enterprise MMM consultancies (Bain Consumer Insights, McKinsey QuantumBlack, Ipsos MMA, Kantar)
$100,000 to $1,500,000 per engagement
Quarterly cadence. By the time the report lands, the data is three months old.
Financial-planning suites (Anaplan, Pigment, Vena, Mosaic, Cube)
$15,000 to $500,000+ per year
MMM features bundled inside finance-team-led planning. Not marketing-ops-led per-market.
Marketing planning platforms (Allocadia, Plannuh, Hive9, Aprimo)
$25,000 to $500,000+ per year
Planning features with some MMM. Not full per-market MMM.
In-house MMM data science (custom Python plus Stan, PyMC, or Meta Robyn)
$130,000 to $240,000 per year per data scientist, plus six to sixteen weeks per cycle
Requires a data scientist. Six to sixteen weeks per cycle.
Build it in-house
Excel and Solver
Falls apart past five channels or any non-linear adstock decay.
What changes when this is an agent skill
Every market gets a continuously updated mix model. Denver, Austin, Tampa, and every other market see their own per-channel contribution and recommended allocation, refreshed continuously instead of every quarter. The model pulls from data you already have — call-tracking attribution, foot-traffic capture rate, identity-resolved customer journeys, POS receipts, attribution roll-ups, the drivers analysis for each market — so the analyst is not stitching together a one-off CSV every cycle. Adstock decay, diminishing returns, and seasonality are handled inside the model. Scenarios run against the current data: what if Texas grows 12%, what if a new competitor enters Phoenix, what if you reallocate $200,000 from out-of-home to local SEM. The output ties into the rest of the rollup reporting so the executive summary, the board deck, the LP letter, and the forward-looking recommendations all draw from the same source of truth. Multi-banner operators see per-market MMM across banners with each banner's spend and outcomes kept distinct. Compliance rules apply per vertical and jurisdiction (HIPAA, FDA, GDPR, California consumer-data). Every run, coefficient, and scenario is preserved with the methodology — so when an audit, regulator, or PE sponsor asks how a number was produced, the answer is on file. Rockerbox, Northbeam, and Nielsen MMM remain useful for brand-wide or single-banner work. Bain and McKinsey remain a reasonable choice for one-off enterprise engagements. Meta Robyn and PyMC remain useful if you have a data-science team. This is the continuous per-market layer none of them provide.
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.
Offline Attribution Intelligence Agent
Surface-as-foundation hybrid — answers which marketing dollar generated which revenue dollar at which store.
FAQ
- What does the output actually tell me?
- For each market, the current contribution of each channel (paid search, paid social, direct mail, radio, out-of-home, email, organic, calls, foot traffic) and the recommended allocation for the next period — with a confidence level and downside risk attached.
- How is this different from Rockerbox or Northbeam?
- Rockerbox and Northbeam handle brand-wide or single-banner MMM well. They are not built for per-market MMM across a 60-location urgent-care brand or a multi-banner portfolio.
- How is this different from Bain, McKinsey QuantumBlack, or Kantar?
- Those run on a quarterly engagement at $100,000 to $1,500,000+ each time. By the time the report lands, the data is three months old. This is continuous.
- How is this different from Meta Robyn or PyMC?
- Those are excellent open-source frameworks if you have a data scientist who can run Bayesian regression. This is built for marketing ops and runs continuously without that overhead.
- What data does it use?
- Per-market spend by channel, per-market outcomes (revenue, foot traffic, calls, customer journeys), the drivers analysis for each market, your cohort KPIs, and your attribution roll-up. All from data you already have.
- Can I run scenarios?
- Yes. What if Texas grows 12%. What if a new competitor enters Phoenix. What if you reallocate $200,000 from out-of-home to local SEM. The output moves against your actual data.
- Does it work for multi-banner operators?
- Yes. Per-market MMM across banners with each banner's spend and outcomes kept distinct.
- How does this hold up under board, investor, PE, or state-AG scrutiny?
- Every run, every coefficient, and every scenario is preserved with the methodology and the data it was based on. When someone asks how a number was produced, the answer is on file.