AEO Managed Services Pricing: What 15 Providers Actually Charge in 2026
Robyn, LightweightMMM, Recast, Mass Analytics, and Pecan AI have made marketing mix modeling cheap enough for mid-market operators. The next discipline is treating AI search as a first-class channel input alongside paid, organic, and affiliate — and validating the coefficients with geo holdouts before the CFO does it for you.
By Grace Mwangi, Impact & ESG · May 26, 2026
AEO marketing mix model guide: Robyn, LightweightMMM, Recast, Mass Analytics, Pecan AI compared, with geo-experiment validation for AI search attribution.
Frequently Asked Questions
What is a marketing mix model and why does it matter for AEO attribution?
A marketing mix model, or MMM, is a top-down statistical regression that decomposes revenue into contributions from each marketing input — paid media, organic search, affiliate, email, brand TV, and now AI search — using aggregated weekly or daily time-series data rather than user-level tracking. It matters for AEO because answer engines like ChatGPT, Perplexity, and Claude do not pass clean referrer data, set cookies, or surface UTM parameters reliably, which means cookie-based multi-touch attribution undercounts AI search contribution by a factor that ranges from two to ten across the campaigns we have measured. MMM sidesteps that limitation by working with aggregated outputs against aggregated inputs. The 2026 generation of open-source tooling has made MMM cheap and fast enough that mid-market operators can run it in-house quarterly rather than paying agencies six figures annually.
Which MMM tool is best for measuring AI search contribution in 2026?
No single tool dominates. Meta's Robyn is the strongest open-source default for teams with an R-capable analyst — it offers adstock, saturation, and ridge-regression options with built-in hyperparameter tuning. Google's LightweightMMM, built on JAX and NumPyro, suits Python-native teams and integrates cleanly with notebook workflows but has been in slower-paced development since Google released Meridian as its strategic successor. Recast, from Aurelius Marketing Sciences, is the leading hosted Bayesian MMM platform and the strongest choice for teams that want continuous re-fitting rather than quarterly batch runs. Mass Analytics offers enterprise consulting plus the MassTer platform and tends to win regulated-industry RFPs. Pecan AI is a predictive-analytics-first vendor whose MMM module trades methodological depth for time-to-first-model. Match the tool to the analyst skill and the cadence required, not to the marketing on the homepage.
How do you add AI search as a channel input to a marketing mix model?
You add AI search as a channel input by constructing a daily or weekly time-series that captures aggregate AI search exposure, treating it the same way you treat impressions for paid media. The most common inputs are share-of-voice in answer engines from tools like Profound, Otterly, or Peec, weighted by query volume; referral sessions tagged as AI-source in server logs; and where measurable, a synthetic impression count derived from query-level citation tracking. Apply adstock — a decay function — to model the lagged effect of AI citations on conversions, since a user who first sees your brand in a ChatGPT answer often converts days later via direct, organic, or paid. Then let the model estimate the channel coefficient and validate the result with a geo-experiment holdout before publishing the number to the board.
Why do geo experiments matter for validating MMM coefficients?
Geo experiments matter because MMM is a correlational technique, not a causal one. The model estimates which inputs co-vary with revenue, but co-variance is not causation, and a coefficient that looks reasonable can still be wrong. A geo experiment — holding out a designated market area or set of zip codes from AI search optimization while continuing it elsewhere — produces a clean causal estimate of incrementality. You then compare the observed lift in the test geos against the MMM's predicted lift for those geos. If they match within the model's credible interval, the MMM coefficient is trustworthy. If they diverge meaningfully, the model has correlation confounds and the coefficient needs adjustment. Meta's Robyn ships geo-experiment calibration as a first-class workflow, and Google's Causal Impact package supports the difference-in-differences math behind it.
How often should a marketing mix model be re-run to track AEO contribution?
Traditional MMMs were re-run annually or quarterly because the consulting engagement cost made faster cadences impractical, but the answer engine channel changes faster than that. ChatGPT model releases, Google AI Overview expansions, and Perplexity ranking shifts can move citation rates by 30 to 50 percent in a single week. Operators measuring AEO contribution should re-fit at least monthly, and the hosted Bayesian tools — Recast in particular — are designed for continuous re-fitting as new data arrives. Open-source Robyn and LightweightMMM workflows can be scheduled as monthly Airflow or GitHub Actions jobs. The key discipline is to lock the model specification, the channel definitions, and the validation geos in advance, so a re-fit produces comparable coefficients rather than a freshly tuned model each cycle.
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Topics: AEO, Marketing Mix Modeling, Attribution, Robyn, LightweightMMM, Measurement
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