Choosing a Marketing Mix Modeling (MMM) solution in retail starts with a practical question: can it measure your full business and help your teams decide where to spend next?
For an omnichannel retailer, that means understanding how advertising affects both store and ecommerce sales, how promotions influence the results, and how performance differs between product groups. The solution also needs to support the decisions your teams make, from annual media planning to campaign and ad set optimization.
This guide presents all 69 evaluation criteria across ten categories from our comparison of MMM solutions for retail brands. The ten categories cover retail outcomes, promotions and other non-media drivers, media coverage, modeling fundamentals, experiment calibration, scenario planning, reporting, data integration, enterprise governance, and implementation and support.
Table of contents
- What is a retail MMM solution?
- Define what you need the solution to do
- How the evaluation criteria were developed
- The 69 evaluation criteria across ten categories
- How to use this framework in your evaluation
- Frequently asked questions
- Further reading
What is a retail MMM solution?
A retail MMM solution uses historical sales, marketing, and business data to estimate the incremental impact of marketing: the additional sales that would not have occurred without a marketing activity.
The model needs to account for other factors that influence sales, including promotions and seasonal demand. Its results can then inform decisions about marketing budgets and expected returns. The reliability of those estimates depends on the data, modeling assumptions, and validation.
For retail teams, useful questions include:
- How much store revenue does our digital advertising generate?
- Which product groups benefit most from a campaign?
- How much of a promotional sales increase comes from the discount and how much from advertising?
- Where should we allocate additional budget before the next trading period?
- Which campaigns should receive more spend, and what changes should media buyers make?
Use these questions to define the scope of your evaluation before looking at individual features.
Define what you need the solution to do
Start with the decisions and people the MMM must support.
If your main use case is annual or quarterly budget allocation, prioritize coverage of the business, model quality, and scenario planning. If performance marketers will also use the results to manage campaigns, add requirements for campaign and ad set measurement, frequent updates, and specific spend or bidding recommendations.
Then agree how much work your organization will own. Ask each provider to separate what the software delivers, what its consultants deliver, and what your team must build or maintain. An internal implementation also needs named owners for data preparation, model validation, reporting, and ongoing support.
Write down your required sales channels, markets, product groups, and reporting cadence. This gives every provider the same scope to respond to and makes costs easier to compare.
How the evaluation criteria were developed
We created the evaluation criteria for retail MMM solutions following Sellforte's Solution Research Objectives and Guiding principles. To develop the criteria, we conducted primary research:
- Analysis of 1,100 customer and prospect discussions with marketers, marketing analytics professionals, and data scientists.
- Review of our experience in more than 100 retail RFPs and tender processes
- Desk research and LLM-assisted investigation to identify gaps and check the framework against public information.
The result is 69 criteria across ten categories.
The 69 evaluation criteria across ten categories
Categories 1–3 define the business outcomes and media activity in scope. Categories 4 and 5 assess modeling methods and calibration. Categories 6 and 7 address planning and use of the results. Categories 8–10 cover the data, governance, and delivery needed to operate the solution.
Category 1: Modeling store sales & other retail outcomes
Retailers need to see how advertising affects stores and ecommerce separately. A campaign can generate purchases in both, and the sales mix can differ by product category or customer group. This category also checks whether measurement can use net revenue, margin and customer lifetime value with clear definitions.
| ID | Criterion | What it means |
|---|---|---|
| 1.1 | Separately measures media impact on store and e-commerce sales | Estimates media contribution to physical-store and e-commerce sales, with distinct results for each sales channel. Uses actual retailer sales data for physical stores, obtained from POS systems, transaction feeds or data warehouses. |
| 1.2 | Measures how media drives each product-category | Estimates how each media channel drives each product group separately, using retailer-defined product categories |
| 1.3 | Measures cross-channel sales effects (digital media to offline sales, and vice versa) | Quantifies the sales impact of digital media separately for ecommerce sales and store sales. Quantifies the sales impact of offline media separately for ecommerce sales and store sales. |
| 1.4 | Support both gross and net revenue (after returns and cancellations) | Supports both gross (or demand) and net-sales outcomes with documented treatment of returns, refunds or cancellations and consistent revenue definitions across inputs and results. |
| 1.5 | Measures incremental profit or contribution margin | Calculates marketing impact and return using retailer-supplied margin or cost data, with the profit numerator and media-cost treatment defined. |
| 1.6 | Measures acquisition and retention by customer group | Reports incremental marketing outcomes separately for new and existing customers. Supports customer groups defined by the retailer. |
| 1.7 | Connects marketing impact to customer lifetime value | Combines incremental customer acquisitions with customer lifetime-value inputs to estimate marketing impact over a stated value horizon. |
Ask the provider to show one media channel’s contribution to store sales and ecommerce, then break the results down by product category and customer group. Confirm how returns, margins, and lifetime-value assumptions change the reported outcomes.
Category 2: Promotions and other non-media drivers
Promotions, stock availability and changes to the store network can move retail sales at the same time as advertising. The criteria assess whether the model distinguishes these effects and accounts for the retailer's trading calendar and weather. Promotion halo and cannibalization address how an offer changes sales of other products.
| ID | Criterion | What it means |
|---|---|---|
| 2.1 | Separates promotional uplift from media effects | Estimates promotional uplift separately from baseline and media |
| 2.2 | Separates effects of different promotion types | Estimates distinct effects for different promotional mechanisms, such as price discounts and customer-specific offers |
| 2.3 | Measures promotion halo and cannibalization | Estimates how promoted items or categories affect sales of other items or categories, including both positive halo and substitution losses. |
| 2.4 | Accounts for availability and assortment changes | Uses inventory, out-of-stock or assortment-change data to distinguish supply constraints and range changes from media performance. |
| 2.5 | Accounts for store-network changes | Models openings, closures, refurbishments or store coverage so changes in selling capacity are distinguished from marketing-driven growth. |
| 2.6 | Models retail seasonality and trading events | Accounts for recurring seasonality and retailer-relevant holidays or events, with support for local trading patterns and event timing. |
| 2.7 | Accounts for weather-driven demand | Uses relevant weather variables in MMM to separate weather effects from marketing response |
Request a walkthrough of a promotional trading period. Ask how the model handles overlapping advertising, discounts, stock-outs, and store changes, and where the data cannot reliably separate their effects.
Category 3: Media coverage & analytical granularity
This category covers digital, traditional and owned media, along with the level of detail available for decisions. It distinguishes channel-level iROAS from campaign and ad-set estimates, and asks whether results cover seasonal campaigns, geographic areas and multiple businesses. Marginal returns describe the expected return on additional spend.
| ID | Criterion | What it means |
|---|---|---|
| 3.1 | Measures incremental ROAS across digital channel types | Reports incremental ROAS separately for paid search, paid social, and display or online video |
| 3.2 | Measures incremental ROAS by digital campaign and ad set | Reports incremental ROAS for individual campaigns and ad sets or equivalent ad groups within Google Ads, Meta Ads and TikTok Ads. |
| 3.3 | Measures Incremental ROAS of traditional and offline media | Reports incremental ROAS for each offline media, including TV, Radio, Print, Out-of-home |
| 3.4 | Measures owned and CRM media | Estimates the incremental contribution of owned activity, with examples spanning CRM contact channels, email, SMS, organic social and catalogs. |
| 3.5 | Measures seasonal campaigns (e.g. back to school) | Estimates incremental ROAS for each seasonal campaign. |
| 3.6 | Provides geographic estimates below national level | Estimates marketing effects for regions, states, DMAs or store catchments and states the geographic modeling level and data conditions. |
| 3.7 | Supports multiple brands, markets and business units | Maintains differentiated MMM results across brands and markets |
| 3.8 | Provides marginal returns | Presents both incremental return on existing spend and marginal return on additional spend |
Provide your actual media plan and campaign structure. Ask which results the solution can estimate at each level and what evidence supports its geographic, seasonal campaign, and ad set breakdowns.
Category 4: Modeling fundamentals
An MMM needs assumptions about delayed effects, diminishing returns and the relationship between advertising and existing demand. This category checks public evidence for those methods, model transparency, validation, diagnostics and uncertainty. A predictive fit statistic alone cannot establish that a media-effect estimate is causal.
| ID | Criterion | What it means |
|---|---|---|
| 4.1 | Models delayed and carryover media effects | Modeling methodology includes channel-specific lag or adstock treatment |
| 4.2 | Models saturation and diminishing returns | Modeling methodology includes nonlinear response curves that estimate how additional spend changes incremental outcomes as a channel saturates. |
| 4.3 | Exposes model assumptions and parameters | Provides customers with model specifications, relevant coefficients or response parameters, and the basis for priors or constraints so analysts can review the model. |
| 4.4 | Tests predictive performance on held-out data | Modeling methodology includes time-based holdout testing or backtesting with data excluded from fitting, and reports the horizon and validation metrics. |
| 4.5 | Provides model health diagnostics | Provides a customer-facing diagnostic report or view covering fit and residuals, plus method-appropriate checks such as convergence or parameter plausibility. |
| 4.6 | Addresses confounding and demand capture | Explains how pre-existing demand and media targeting can bias estimates, including paid-search demand capture, and documents controls or another identification strategy. |
| 4.7 | Handles sparse and correlated marketing activity | Documents pooling, regularization, aggregation or another approach for small datasets and overlapping campaigns, with limits on granularity or identifiability. |
| 4.8 | Reports uncertainty in incremental effects | Provides confidence or credible intervals for incremental contribution or ROI, with the interval level and interpretation stated. |
Have your analytics team review a sample model specification and diagnostic report. Ask the provider to explain its treatment of paid-search demand capture, overlapping campaigns, and uncertainty in the estimates.
Category 5: Model calibration & Experiments
Experiments can inform an MMM when their outcomes, channels and time windows are relevant to the model. These criteria assess calibration from retailer experiments and platform lift studies, other calibration inputs, handling of uncertainty, reconciliation between methods and prioritization of further tests. Expert-reviewed manual calibration qualifies alongside automated workflows.
| ID | Criterion | What it means |
|---|---|---|
| 5.1 | Calibrates MMM using geo or first-party experiments | Uses results from geo holdouts, matched markets or retailer-run A/B tests to update MMM parameters, priors, constraints or response estimates when relevant new evidence becomes available. Expert-reviewed manual and automated workflows both qualify; a self-service interface is not required. |
| 5.2 | Calibrates MMM using platform lift studies | Ingests conversion lift tests from ad platforms, and uses ad-platform conversion-lift results in MMM calibration |
| 5.3 | Calibrates MMM using incrementality factor benchmarks and attribution data | As additional calibration data, provides calibration inputs based on incrementality factors and attribution data. |
| 5.4 | Checks calibration relevance and uncertainty | Aligns experiment and MMM outcomes, channels, markets and time windows, and explains how experimental uncertainty and evidence age influence calibration. |
| 5.5 | Investigates disagreements between measurement methods | Provides a documented process to reconcile MMM, experiment and attribution results using aligned definitions, while retaining and explaining discrepancies. |
| 5.6 | Uses MMM uncertainty to prioritize further tests | Identifies channels or decisions where experiments would most improve MMM, using uncertainty and business impact to support a testing agenda. |
Use an experiment from your own business to walk through calibration. Check how the provider aligns the test with the model, treats uncertainty, and investigates a result that disagrees with the existing MMM.
Category 6: Scenario planning & budget optimization
Planning requires translating model estimates into feasible spending choices. The criteria cover budget scenarios, allocation under a fixed budget, spending needed for a target, practical constraints, timing across the trading calendar and allocation across businesses. They also assess joint marketing and commercial scenarios and the limits shown around recommendations.
| ID | Criterion | What it means |
|---|---|---|
| 6.1 | Simulates changes to marketing budgets | Lets users change spend levels or channel mixes and forecast the resulting incremental and total business outcomes over a stated period. |
| 6.2 | Optimizes channel allocation for a set budget | Recommends a channel spending mix that maximizes a chosen business outcome using modeled response curves while keeping total expenditure at the specified amount. |
| 6.3 | Estimates budget required to reach a business target | Solves for spend required to meet a specified outcome or efficiency target and identifies infeasible targets or model limits. |
| 6.4 | Respects practical planning constraints | Allows users to set channel minimums, maximums or fixed commitments and incorporates those constraints into the recommended plan. |
| 6.5 | Optimizes spending across the trading calendar | Allocates spend across time periods using seasonal response, campaign timing and carryover, supporting tactical and longer-term planning. |
| 6.6 | Optimizes budgets across brands or business units | Recommends how a common marketing budget should be distributed across business units or markets using their respective response estimates and allocation constraints. |
| 6.7 | Combines marketing and commercial decisions in scenarios | Lets users assess changes in media spending together with a controllable business decision, such as pricing, and explains the relationships and assumptions used. |
| 6.8 | Shows uncertainty and limits in recommendations | Shows uncertainty or sensitivity around scenario outcomes and flags recommendations outside supported spend or data ranges. |
Ask the provider to demonstrate both a fixed-budget allocation and the budget needed to reach a target. Include a committed media booking, a seasonal promotion, and a limit on how much spend can move between markets.
Category 7: Reporting, speed & decision workflows
This category assesses how marketers use results between planning meetings: reviewing MMM in an interface, receiving updated results, understanding performance changes, acting on campaign recommendations, exporting data and asking questions in natural language. The refresh criterion accepts a documented cadence suited to the decision, including daily, weekly or monthly cycles.
| ID | Criterion | What it means |
|---|---|---|
| 7.1 | Provides self-service MMM exploration UI | Shows a marketer-facing interface for reviewing MMM results and drilling into relevant periods, channels and business dimensions without writing code. |
| 7.2 | Refreshes MMM to match decision cycles | Documents recurring model refreshes or result updates using newly available data, with cadence tied to the intended planning or optimization decisions. Daily, weekly, monthly or other stated cycles qualify when suitable for those decisions; input refresh alone does not establish updated MMM results. |
| 7.3 | Explains changes in business performance | Provides period-to-period explanations of sales changes using media, promotions and baseline/context effects, distinguishing modeled explanations from causal claims about controls. |
| 7.4 | Recommends optimal spend and bidding parameters by campaign & ad set | Provides optimal spend and bidding parameters on the campaign & ad set level |
| 7.5 | Exports structured results for downstream analysis | Provides documented exports of granular MMM outputs and planning results in usable tables, plus an API or warehouse route for recurring downstream use. |
| 7.6 | Supports natural-language analysis of MMM results | Provides a conversational AI interface that answers questions using the customer’s MMM results and identifies the underlying data, period or scenario. |
Have a marketer use the interface to investigate a sales change and find a campaign recommendation. Ask what data and model version support the answer, when results were last updated, and how the team can export them.
Category 8: Data integration & quality
Retail MMM depends on recurring feeds of sales, media and business data. The criteria assess advertising connectors, retailer-system connections, offline and custom feeds, retailer-defined taxonomies, validation, data specifications and preparation tools. Accepting a data file and maintaining a recurring production integration are different requirements.
| ID | Criterion | What it means |
|---|---|---|
| 8.1 | Automates digital media ingestion | Documents working, named advertising-platform connectors that retrieve MMM inputs on a recurring schedule, including spend and campaign metadata. |
| 8.2 | Connects to retailer sales and customer data | Documents recurring ingestion from retail commercial systems or data warehouses for store and e-commerce sales, with customer or transaction detail where needed. |
| 8.3 | Accepts offline media and custom business feeds | Supports agency data and custom retailer inputs through documented file templates, APIs, warehouse feeds or secure file transfer, with input fields explained. |
| 8.4 | Enables custom data taxonomy for sales and media data | Supports retailer-defined channel, campaign, objective, product group and geo taxonomies |
| 8.5 | Provides tools for data validation | Has tools for validating data, such visualization and automated checks |
| 8.6 | Provides data specifications | Publishes data specifications for Marketing Mix Modeling |
| 8.7 | Provides data cleaning, harmonization and aggregation tools | Documents tools for harmonizing, aggregating and cleaning data, such managing monthly offline data for an MMM with daily frequency |
Walk through your actual source systems and reporting calendar. Ask who owns each feed, how retailer-specific categories are mapped, and what happens when sales data arrives late or an input fails validation.
Category 9: Enterprise security & governance
The five criteria assess public documentation of encryption, independent security assurance, single sign-on, access controls and residency choices. They require evidence about the relevant customer service or data flows. A general company statement may provide only partial evidence for a specific MMM product.
| ID | Criterion | What it means |
|---|---|---|
| 9.1 | Encrypts stored and transmitted customer data | Public security documentation explains the encryption used for customer data during storage and transfer, including which services or data flows it covers. |
| 9.2 | Provides independent security assurance | Publicly identifies a relevant independent security assessment or certification, such as SOC 2 Type II or ISO 27001, and the covered organization or service. |
| 9.3 | Supports enterprise single sign-on | Publicly documents customer single sign-on through a named enterprise identity standard or supported identity provider. |
| 9.4 | Controls access by role and business scope | Documents role-based access with restrictions for relevant teams, agencies, brands or markets, including distinctions between viewing and editing. |
| 9.5 | Provides data residency choices | Supports storage and processing options at least between EU and US. |
Ask your IT team to verify the scope of the security evidence and the proposed hosting arrangement. For an open-source implementation, assess the organization or partner operating the solution against these requirements.
Category 10: Retail experience, implementation & support
This category checks evidence for named retail MMM deployments, documented decisions and outcomes, implementation responsibilities, support, training, expert interpretation and commercial scope. Five qualifying named retail MMM cases are required for full credit on the deployment-count criterion; logos alone do not qualify. Pricing can be documented through a proposal process without public list prices.
| ID | Criterion | What it means |
|---|---|---|
| 10.1 | Demonstrates 5+ named retail MMM deployments | Publishes named retail case studies describing actual MMM use and the retailer’s sales-channel or business context. Five or more qualifying cases score 1; one to four score 0.5. Customer logos alone do not count as qualifying cases. |
| 10.2 | Documents a retail decision and measured outcome | A retail case links an MMM insight to an implemented decision and a quantified outcome, stating the metric and comparison period or baseline. |
| 10.3 | Defines onboarding responsibilities and milestones | Publishes implementation stages, customer and vendor responsibilities, and indicative timing to first usable insights, including key data dependencies. |
| 10.4 | Provides ongoing support with clear coverage | Describes technical or user-support routes, service coverage and response or escalation arrangements relevant to the customer’s operating region. |
| 10.5 | Trains teams and supports adoption | Provides documented training, onboarding resources or role-specific enablement for marketers and analysts, including continued use after initial setup. |
| 10.6 | Provides expert interpretation and planning support | Describes access to MMM or retail analytics specialists for model review, business interpretation and recurring planning discussions. |
| 10.7 | Defines pricing basis and agreed service scope | Documents how buyers receive the charging basis and software, implementation, support and optional-module inclusions through standard terms or a custom proposal. Public list prices are not required; vendor-published evidence of commercial terms or proposal contents qualifies. Service-scope evidence without the charging basis supports partial credit. |
Request reference conversations with comparable retailers and an implementation proposal. Confirm the work your team will own, access to specialists after launch, training, support coverage, and the full charging basis.
How to use this framework in your evaluation
1. Choose your mandatory requirements
Mark each criterion as required, preferred, or outside your current scope. An omnichannel retailer might require separate store and ecommerce measurement. A promotion-heavy business should closely examine Category 2. A performance marketing team needs the campaign measurement and recommendation criteria in Categories 3 and 7, supported by the model quality requirements in Categories 4 and 5.
Apply your company's security and procurement requirements before investing in detailed demonstrations.
2. Ask every provider to demonstrate the same scenarios
Prepare a short brief with your business structure and representative decisions. Useful scenarios include explaining a promotional sales spike, estimating digital media's contribution to stores, and reallocating a budget with fixed commitments.
For each scenario, ask to see the inputs, results, assumptions, and recommended action. Record whether delivery is available in the product, requires a service engagement, or depends on future development.
3. Record evidence alongside each score
Use the framework’s 0, 0.5, and 1 scale to record how well the evidence supports each requirement. For your own buying process, keep unresolved items explicitly unverified:
| Score | Meaning |
|---|---|
| 0 | The requirement is not met. |
| 0.5 | It is partially met, with material limitations or additional work. |
| 1 | It is met and demonstrated for the agreed use case. |
| Unverified | Evidence is still needed before assigning a score. |
The published comparison gives every criterion equal weight. In your own selection, weight criteria according to their importance to your business. Keep mandatory requirements as separate pass/fail decisions so a high overall score cannot hide a missing essential capability.
4. Validate the proposed scope and ongoing workload
Before committing, agree how the provider will demonstrate that your data supports the proposed model detail and decisions. Review data completeness, assumptions, uncertainty, and the connection between model results and optimizer recommendations.
Document what your team will own after launch. Include recurring data work, model reviews, training, and the process for adding a market or product group. Compare the total cost for that agreed scope.
Frequently asked questions
What should retailers prioritize when choosing an MMM solution?
Start with coverage of your sales channels, media, product groups, and promotions. Then evaluate whether the measurement detail, optimization tools, and update frequency support the decisions your teams need to make. Require evidence for each mandatory capability.
Does every retailer need campaign and ad set-level MMM?
The requirement depends on who will use it. Channel-level results can support strategic budget planning. If media buyers are expected to use the solution for campaign decisions, evaluate granular measurement and recommendations, including the evidence needed to support that detail.
How much data do we need?
Ask providers to assess your historical coverage, data quality, variation in media activity, and proposed model scope. Require them to explain what can be estimated with the data you have and what additional history or experiments would improve the analysis. Agree this before committing to specific reporting detail.
How can we assess model accuracy?
Review predictive checks together with modeling assumptions, uncertainty, sensitivity to different choices, and relevant experimental evidence. A close fit to historical sales is one diagnostic; it does not establish that the model has correctly separated each channel's causal contribution.
Should we evaluate AI capabilities separately?
Yes. Test whether the AI can answer your team's questions using traceable data and model outputs. If it recommends a budget change, ask it to show the planning assumptions and the calculation supporting that recommendation. Assess execution permissions separately if the AI can make changes to campaigns.
Further reading
- Best MMM solutions for retail brands: compare providers for your shortlist.
- How to choose an AI tool for MMM and incrementality testing: evaluate conversational AI and execution capabilities in more detail.
- How to choose an incrementality testing tool: assess experiment analysis, management, and integration with MMM.
Author
Lauri Potka is the Chief Operating Officer at Sellforte, with over 15 years of experience in Marketing Mix Modeling, marketing measurement, and media spend optimization. Before joining Sellforte, he worked as a management consultant at the Boston Consulting Group, advising some of the world’s largest advertisers on data-driven marketing optimization. Follow Lauri on LinkedIn.
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