If you're a founder spending serious money on marketing, the price tag on Marketing Mix Modelling can look frightening. £150,000 from one of the big firms. £80,000 from a boutique. £25,000 from a productised offer. Free if you've got someone in-house who can build it. That's a wide range, and the reasons aren't obvious from the outside.
TL;DR
Traditional MMM costs £100k to £300k and takes around six months. AI-accelerated builds now sit between £25k and £80k and run in 8 to 12 weeks. The gap is almost entirely about how data preparation gets done. For a brand spending £1m+ a year on marketing, an MMM that finds even a 15% improvement pays for itself many times over in year one.
This guide breaks down what you're paying for, where the price sits across the four main vendor categories, what makes a quote balloon or shrink, and the question worth asking before any of that: when does the investment pay back, and at what scale does it stop making sense?
The short answer on cost
A single MMM build, in 2026, will cost you somewhere between £15,000 and £300,000. That's not a useful range, so here's a more honest picture by vendor type.
| Vendor type | Typical price (one build) | Timeline | Best fit |
|---|---|---|---|
| Large analytics firm | £150,000 to £300,000 | 6 to 12 months | Enterprise, multi-brand portfolios |
| Mid-market consultancy | £60,000 to £120,000 | 4 to 6 months | Single-brand mid-market |
| Productised AI-accelerated | £25,000 to £80,000 | 8 to 12 weeks | Scale-ups and growth-stage brands |
| In-house with open-source | £0 software, £80k+ talent / year | Ongoing | Companies with existing data science teams |
The price isn't a proxy for quality. A £200,000 engagement and a £40,000 engagement can use the same statistical methods, the same model class, and produce a comparable answer. What you're paying for is people, time, and the layer of process built around the model.
The four vendor categories
Large analytics firms
Ekimetrics, Analytic Partners, Nielsen, Gain Theory, IPG/Kantar. Established names with long client lists and deep benches of analysts. You get a relationship-led engagement, a polished deliverable, multi-stakeholder workshops, and a brand on the invoice that helps internally if you're the person defending the spend. The price reflects the overhead: senior partners, junior analysts, account managers, big offices. Six months minimum, often a year.
If you're a multi-brand global business and the cost of getting the answer wrong is in the millions, this tier exists for good reason. Forrester's most recent wave on MMM vendors notes that the larger players still dominate enterprise deals because procurement processes at that scale demand them. For most other businesses, the overhead doesn't translate into a better answer.
Mid-market consultancies
Smaller, more focused firms, often spun out of the bigger players. Less brand recognition, typically more partner-level attention. £60,000 to £120,000, four to six months. Quality varies more than at the top end, so reference checks matter. A good mid-market consultancy can give you 80% of what a large firm delivers at half the price. A bad one will give you a generic deliverable with the partner's logo on the front page.
Productised AI-accelerated offers
A newer category. The pitch is that AI handles the data prep work that traditionally swallows 60 to 80% of project time, which collapses both cost and timeline. £25,000 to £80,000 for a build, weeks rather than months. The trade-off is less bespoke account management and a tighter scope. If you want a clear answer to "how should I split my marketing budget?" without paying for a year-long programme, this is where the market is heading.
In-house with open-source tools
The cheapest on paper. Meta's Robyn and Google's Meridian are open-source and free. The catch: you need a data scientist who knows MMM (rare), 40 to 80 hours of analyst time per build, and the political capital to defend a model the marketing team didn't pay an external party to bless. Realistic annual cost: £80k+ in fully loaded salary, plus the opportunity cost of that person not doing other things. Nielsen's research on MMM adoption shows in-house programmes typically take 12 to 18 months to reach a usable first model when starting from scratch.
What drives the price up or down
Three things explain almost every quote difference you'll see.
Data state
This is the big one. If your marketing data is clean, complete, in one place, and tagged consistently, prep is fast. If it's scattered across agencies, ad platforms, and finance exports with inconsistent naming, prep balloons. The single biggest cost driver in any MMM project is the state of your data when work begins. Vendors who quote without seeing your data are guessing.
This is also why the same vendor will give two different brands wildly different quotes for the same scope. A DTC business with everything in a single warehouse can get a build done in a fraction of the time a multi-agency, multi-platform conglomerate can.
Number of brands and markets
One brand in one market is the cheapest unit. Each additional brand or country roughly doubles the work, because you need separate models. A multi-brand FMCG running across five countries is a fundamentally different project from a UK DTC brand with one product line. Bundle pricing exists, but the underlying work scales with the number of models being built.
Refresh frequency
A one-off build is one price. A model that gets refreshed quarterly to feed planning is a different commercial model. Most enterprise programmes run on annual retainers (£200k to £500k a year) with quarterly refreshes built in. Productised offers either include refreshes in the annual fee or charge per refresh (£25k to £30k each).
Other factors that move the number: how many marketing channels you run, weekly or daily granularity, whether you need scenario planning tools or just a deliverable, and the depth of strategic recommendation you want at the end.
Hidden costs nobody mentions
The headline price is rarely the full bill. Things that often turn up later:
- Internal time. Your marketing, finance, and analytics teams will spend 20 to 60 hours feeding the project. That's billable time you're paying for in salaries.
- Data engineering. If your data isn't already piped into a warehouse, you may need to invest in basic infrastructure to make MMM repeatable. One-off cost, but real.
- Refresh cadence. A one-off MMM goes stale within 12 months. If you don't budget for refreshes, you'll either pay full price again next year or quietly lose confidence in the original output.
- Activation. The model produces recommendations. Acting on them sometimes means renegotiating media contracts, building new measurement processes, or training planners. Worth budgeting for separately.
Most of these get glossed over in initial conversations. They aren't dealbreakers, but they're worth raising upfront so the vendor can scope honestly and you don't get a surprise six weeks in.
Is it worth it? The payback question
The question that matters. The honest answer: it depends on your spend and your willingness to act on the recommendations.
The maths is straightforward. Industry benchmarks from the Marketing Accountability Standards Board (MASB) and several published vendor case studies put the typical ROI improvement from a first MMM at 15 to 25%. For a brand spending £3m a year on marketing at a 3:1 return, that's £450,000 to £750,000 of incremental revenue from reallocation alone. Against a £25k to £100k build cost, payback sits well inside the first year, often within the first quarter.
A useful rule of thumb: if you're spending £1m+ a year on marketing, MMM almost always pays back. Below that, the maths gets thinner. At £500k of annual spend, a 20% lift is £100k of revenue, so a £40k build still works but the margin is tighter. Below £250k of spend, MMM is hard to justify on payback alone unless you have specific strategic reasons (entering a new channel, building a case for brand spend to a board, validating a major budget shift).
A second test worth applying: would you act on the answer? If your channel mix is already locked in by long-dated media contracts, or your team can't move budget in-quarter, even a brilliant MMM is going to struggle to pay back. The model finds the opportunity. You have to take it.
When each tier makes sense
Large firms suit multi-brand enterprises spending £20m+ on marketing across multiple geographies. Stakeholder consensus across many internal teams matters. The brand on the deliverable helps with internal buy-in. Budget isn't the constraint.
Mid-market consultancies suit single-brand businesses spending £3m to £15m on marketing. You want a partner relationship with one point of accountability. You can wait four to six months and have £80k+ to invest.
Productised AI-accelerated suits brands spending £1m to £10m. You want speed (weeks not months), price predictability, and you're focused on the answer rather than the process. You're comfortable with a tighter scope.
In-house works if you already employ data scientists, have a 12+ month horizon to build the capability, and see MMM as a permanent function rather than a one-off project. The hardest part is getting the first usable model out of the door. Once you have it, refreshes get cheap.
Where Hanya fits
A note on context, since you're reading this on hanya.co. Hanya is the productised AI-accelerated category described above, with three pricing tiers:
- Early Adopter Rate — £25,000 (was £75,000). Two places. Includes the full first build, plus a refund-on-case-study clause.
- Partnership Plan — annual programme. £50,000 onboarding, plus unlimited insight refreshes for 12 months.
- Flexi Plan — from £80,000 for the first build, £30,000 per insight refresh thereafter.
All three carry the 20:12 Guarantee: a 20% improvement in marketing return inside 12 weeks, or your money back. The reason we can quote at this level is an AI data layer that compresses the data prep work that would otherwise take three to four months. The trade-off versus a £150,000+ traditional engagement: same statistical method, same rigour on the modelling side, much less time spent on the part that doesn't differentiate the answer.
If you want to dig deeper into the underlying methodology, the essay Beyond Attribution walks through how MMM works and why it tends to outperform attribution as a strategic measurement tool. What is Marketing Mix Modelling? is the shorter primer.