Marketing Mix Modeling (MMM) RFP for Retail: A Procurement Guide

A retail Marketing Mix Modeling (MMM) RFP should give every supplier the same business questions, data context and delivery expectations. It should also explain how the retailer will verify the answers. That is what makes proposals comparable and gives marketing, analytics, finance and procurement a shared basis for selecting a partner.

The detail matters. Two proposals can both promise store and ecommerce measurement, monthly updates and budget optimization while covering different outcomes, requiring different amounts of internal work and arriving at very different prices.

This guide explains how to prepare an MMM request for proposal, structure the selection process, evaluate suppliers and agree what happens after the contract is signed. It includes an RFP outline, 69 supplier questions, a scoring method and guidance for demonstrations and pilots.

The evaluation questions follow the framework in How to choose a Marketing Mix Modeling solution in retail and Best MMM solutions for retail brands. The procurement guidance also draws on Sellforte's experience participating in more than 100 retail MMM buying processes as a vendor.

What is an MMM RFP in retail?

An MMM RFP is a structured invitation for suppliers to explain how they would measure marketing effectiveness for a retailer, deliver the results and support their use. It asks for a proposed solution, implementation approach, evidence of capability and a commercial offer against a defined scope.

In retail, that scope can include physical stores and ecommerce, several brands or countries, product categories, customer groups and different measures of business performance. Promotions, changes in availability and the trading calendar also need consideration when estimating marketing's contribution.

Three procurement stages serve different purposes:

A complex retailer may use all three. A smaller, well-defined purchase can combine the proposal and quotation stages. Keep exploratory questions in the RFI where possible, so suppliers can price the RFP against a stable brief.

Prepare the business scope before launching the RFP

Start with decisions and their owners

Write down the decisions the solution must support and when they occur. For example, a marketing director may need to allocate the next annual budget across countries, while a media buyer needs campaign recommendations during a seasonal promotion. Those decisions place different demands on granularity, updates and support.

For each priority use case, identify the person who will act, the output they need and the deadline for receiving it. A useful requirement might read:

Before the quarterly planning meeting, the media team must be able to compare a fixed-budget plan with a reduced-budget plan across stores and ecommerce, including committed media bookings and the planned promotional calendar. Finance must be able to review the revenue and margin assumptions.

Appoint one business owner for the overall selection. Give the other functions clear responsibilities:

Include the people who will use the results in demonstrations. A successful presentation to the selection committee does not establish that a country team can maintain its data feed or that a media buyer can use the recommendations.

Define what the supplier is pricing

Create a scope schedule covering brands, countries, sales channels, outcomes, product groups and required geographic detail. Separate the initial deployment from later expansion.

Avoid using “number of models” as a pricing metric. One supplier may price one brand in one country as a model, while another counts a separate model for every outcome. The number of models alone will not make those offers comparable. For illustration, three brands in two countries create six brand-country combinations. Requiring revenue and new-customer outcomes creates twelve business outcome combinations. That arithmetic describes coverage; it does not prescribe twelve independent statistical models.

Instead of the number of models, describe the specifications of the implemented solution, for example:

• Modeled KPIs (e.g., media impact on net sales after returns or CLV)
• Sales channels in scope (e.g., stores, ecommerce, app)
• Customer segments (e.g., new & returning, retailers' loyalty segments)
• Product groups in scope
• Brands and countries in scope, including media spend in each
• Digital media in scope
• Offline media in scope
• Owned media in scope

This enables the measurement vendor to understand the complexity of the solution and give accurate pricing. If procurement wants options, it can give this detail for each country or even each country-brand combination.

Ask suppliers to map their proposed architecture and charging units to the same coverage schedule.

Prepare a data readiness brief

Start the data inventory while procurement prepares the tender. A useful brief tells suppliers what exists, who owns it and what may be difficult to deliver.

For every feed, name an owner and record historical coverage, update frequency and known gaps. Identify planned migrations, such as a change in ecommerce or CRM platform, that could interrupt a series.

Ask suppliers to assess what the available data can support. A universal minimum number of months does not settle whether a particular campaign, category or regional effect can be estimated. History, variation, missing inputs and the requested detail all matter.

For shortlisted suppliers, share a representative sample through the agreed confidential process. Include a difficult period, such as a promotion with overlapping media activity. A sample that excludes the retailer's main measurement challenge provides limited evidence of fit.

Structure the procurement process around a usable result

Work backwards from the first decision that needs MMM. Allow separate time for selection, contracting, data preparation, model validation and training. An award date is not the date the business will have usable insights.

The following is an illustrative twelve-week selection schedule for a retailer with a reasonably clear scope. It excludes a substantive pilot and the subsequent implementation. Add time for those activities where needed.

More complex sourcing can use separate information and quotation rounds. A pilot can add weeks or months depending on whether it tests integration, model feasibility or the outcome of a live marketing decision. Plan that work explicitly.

Publish the question deadline, response deadline, presentation dates, decision stages and submission route. Give suppliers a shared answer log, removing bidder-identifying details where appropriate. If a clarification changes the scope, update the main schedule and pricing template as well. Otherwise, the final offers may still reflect different versions of the requirement.

What to include in the retail MMM RFP

Use the following outline as the contents of the tender pack:

1. Business background and reasons for the purchase, including the problems with the current measurement approach.
2. Priority decisions, intended users and the first planning deadline the solution must support.
3. Initial scope and optional expansion, with agreed definitions of markets, outcomes and delivery units.
4. Data inventory, representative schemas, known gaps and proposed responsibilities.
5. The requirements questionnaire, using the 69 criterion IDs below.
6. Demonstration brief and any proposed pilot, including the evidence and outputs expected.
7. Implementation and operating plan, covering milestones, retailer effort, training and support.
8. Security and governance requirements and the contract documents suppliers need to review.
9. Pricing schedule covering setup, recurring services, optional work and expansion.
10. Procurement instructions, response format, evaluation approach and timetable.

For each requirement, ask suppliers to provide the capability status, explanation, supporting evidence, data dependencies, delivery method, additional cost and any exception. Require a reference to the specific product view, document section or demonstration that supports the answer.

Use distinct capability statuses: available now; available through an included service; available through additional paid work; planned; or unavailable. Record retailer responsibilities separately. A feature on a roadmap and a feature demonstrated in production should remain distinguishable throughout the evaluation.

The 69 questions to ask MMM suppliers

The questions below translate the ten categories in the retail MMM selection framework into RFP language. The IDs preserve the connection to the original criteria. Use the linked definitions when deciding whether an answer meets a requirement, and mark each item required, preferred or outside the agreed scope.

1. Modeling store sales and other retail outcomes

Define the business coverage before comparing dashboards. Request sample outputs using the same sales and customer definitions that the retailer will use.

• 1.1. Can you estimate media contribution separately for physical stores and ecommerce, using actual retailer sales data for stores?
• 1.2. Can you estimate each media channel's contribution to our own product categories?
• 1.3. Can you quantify digital media's impact on both stores and ecommerce, and offline media's impact on both sales channels?
• 1.4. Can you support gross and net revenue, with documented treatment of returns, refunds and cancellations?
• 1.5. Can you measure incremental profit or contribution margin using our cost data, with the profit numerator and media-cost treatment defined?
• 1.6. Can you report incremental outcomes separately for new and existing customers and other retailer-defined customer groups?
• 1.7. Can you connect incremental customer acquisition to lifetime value, with a stated horizon and transparent assumptions?

2. Promotions and other non-media drivers

Use a promotional trading period to examine these answers. Ask the supplier to explain where the data supports separation of effects and where overlapping activity limits the analysis.

• 2.1. How do you separate promotional uplift from media contribution and baseline sales?
• 2.2. Can you distinguish different promotion mechanisms, such as price discounts and customer-specific offers?
• 2.3. Can you estimate promotion halo and cannibalization across products or categories?
• 2.4. How do you account for stock-outs, inventory availability and assortment changes?
• 2.5. How do you account for store openings, closures, refurbishments and changes in selling coverage?
• 2.6. How do you model seasonality, local holidays and retailer-specific trading events?
• 2.7. How do you use relevant weather variables to distinguish weather-driven demand from marketing response?

3. Media coverage and analytical granularity

Provide the actual channel and campaign structure. Ask suppliers to distinguish the detail at which effects are estimated from detail added through allocation or presentation in the interface.

• 3.1. Can you report incremental return on ad spend, or iROAS, separately for paid search, paid social, and display or online video?
• 3.2. Can you report iROAS for individual campaigns and ad sets or ad groups in Google Ads, Meta Ads and TikTok Ads?
• 3.3. Can you report iROAS separately for offline media, including TV, radio, print and out-of-home?
• 3.4. Can you estimate incremental contribution from owned and CRM activity, including email, SMS, organic social and catalogs?
• 3.5. Can you estimate iROAS for individual seasonal campaigns, such as back to school?
• 3.6. Can you estimate effects below national level, and what geographic structure and data conditions does that require?
• 3.7. Can you maintain differentiated results across our brands, markets and business units?
• 3.8. Can you show both returns on existing spend and marginal returns on additional spend?

4. Modeling fundamentals

Have the analytics team review the model specification and diagnostic examples. A close fit to historical sales does not, by itself, establish correct estimates of marketing's causal contribution.

• 4.1. How do you model channel-specific delays and carryover, including adstock?
• 4.2. How do you estimate saturation and diminishing returns through nonlinear response curves?
• 4.3. Which model specifications, coefficients or response parameters, priors and constraints can our analysts inspect?
• 4.4. How do you test predictive performance on time periods excluded from fitting, and report the validation horizon and metrics?
• 4.5. Which customer-accessible diagnostics cover fit, residuals and other statistical checks?
• 4.6. How do you address confounding, pre-existing demand and targeting bias, including paid-search demand capture?
• 4.7. How do you handle sparse data and correlated campaigns, and what limits do these place on estimation detail?
• 4.8. Can you provide confidence or credible intervals for incremental contribution and ROI, with their level and interpretation stated?

5. Model calibration and experiments

Ask for a worked example showing how evidence changes the MMM. Expert-reviewed manual calibration can meet the framework's requirements; record the workflow and effort alongside the answer.

• 5.1. How do geo holdouts, matched markets or retailer-run A/B tests update MMM parameters, priors, constraints or response estimates?
• 5.2. How do you ingest and use ad-platform conversion-lift studies for calibration?
• 5.3. How can incrementality-factor benchmarks and attribution data provide additional calibration inputs?
• 5.4. How do you work with multiple incrementality tests for the same channel?
• 5.5. What process investigates disagreements between MMM, experiments and attribution while retaining and explaining unresolved differences?
• 5.6. How do MMM uncertainty and the importance of a business decision guide the next experiments?

Ask the supplier to identify the evidence type behind each calibration input. Benchmarks, attribution and retailer experiments can enter the process in different ways; their assumptions should remain visible.

6. Scenario planning and budget optimization

Test the planning workflow using real constraints. For example, include a committed TV booking, a promotional period and a limit on changes to country budgets.

• 6.1. Can users change budgets or channel mixes and forecast incremental and total outcomes over a stated period?
• 6.2. Can you optimize channel allocation for a fixed total budget and a chosen business outcome?
• 6.3. Can you estimate the budget needed for an outcome or efficiency target?
• 6.4. Can users set channel minimums, maximums and fixed commitments that the optimizer respects?
• 6.5. Can you allocate spend across the trading calendar while taking seasonality into account?
• 6.6. Can you allocate a shared budget across brands, markets or business units using their response estimates and constraints?
• 6.7. Can a scenario combine media changes with a controllable commercial decision, such as pricing, with the relationships and assumptions explained?
• 6.8. Can you show uncertainty or sensitivity around recommendations and flag spend outside supported ranges?

7. Reporting, speed and decision workflows

Specify when the business needs updated results. Daily data ingestion, updated model outputs and re-estimation of model parameters are different events, so ask suppliers to describe each cadence.

• 7.1. What self-service UI capabilities do you offer?
• 7.2. Are the results updated daily? If not, what is the frequency?
• 7.3. Can users explain period-to-period sales changes through media, promotions and baseline?
• 7.4. Can the solution recommend spend and bidding parameters at campaign and ad-set level?
• 7.5. Can it export granular MMM and planning results as usable tables, with an API or warehouse route for recurring downstream use?
• 7.6. Can users ask questions in natural language and receive answers grounded in their MMM results, with the relevant data, period or scenario identified?

8. Data integration and quality

Review each proposed production feed. Record which steps are automated, which require a person, and who investigates a failed or incomplete delivery.

• 8.1. Which named advertising-platform connectors ingest spend and campaign metadata on a recurring schedule?
• 8.2. How do recurring integrations bring in store and ecommerce sales from our systems or warehouse, including customer or transaction detail where needed?
• 8.3. How do you accept offline media and custom business inputs, and what templates, fields and transfer methods are supported?
• 8.4. Can you use our own channel, campaign, objective, product-group and geographic taxonomies?
• 8.5. What visual checks and automated tools help validate incoming data?
• 8.6. What MMM data specifications can you provide before implementation?
• 8.7. Which tools clean, harmonize and aggregate data, including inputs delivered at different frequencies?

9. Enterprise security and governance

Ask security and IT to verify the proposed service and data flows. For a solution built on an open-source framework, the organization operating it must provide the relevant evidence.

• 9.1. How is customer data encrypted in storage and in transit, and which services and flows does the documentation cover?
• 9.2. What relevant independent IT security assurances are available?
• 9.3. Which enterprise single sign-on standards or identity providers are supported?
• 9.4. Can roles restrict access by team, agency, brand or market, including separate viewing and editing permissions?
• 9.5. What EU and US storage and processing choices are available, and which arrangement would apply to our deployment?

10. Retail experience, implementation and support

Connect the proposal to the people who will deliver it. Request implementation milestones, examples of ongoing support and references that reflect a comparable retail business.

• 10.1. Can you provide at least five named retail case studies describing actual MMM use and the retailer's business or sales-channel context?
• 10.2. Can you document an MMM insight that led to an implemented retail decision and a quantified outcome, with the metric and comparison period or baseline stated?
• 10.3. What onboarding stages, responsibilities, data dependencies and milestones lead to the first usable insights?
• 10.4. What support routes, regional coverage, response arrangements and escalation process are included?
• 10.5. How will marketers and analysts be trained, including support for continued use after launch?
• 10.6. What access will we have to MMM or retail analytics specialists for interpretation, model reviews and planning?
• 10.7. What is the charging basis, and which software, implementation, support and optional services are included?

The original deployment criterion awards full credit for five or more qualifying named cases and partial credit for one to four; logos alone do not qualify. Use reference conversations to examine delivery experience in more depth.

Score evidence consistently and keep mandatory requirements separate

Agree the scoring method before proposals arrive. Begin by deciding which requirements are mandatory, which differentiate acceptable suppliers and which are outside the current scope.

For example, separate store measurement may be mandatory for an omnichannel deployment. A required security control must also pass independently. A high score elsewhere should not compensate for a failed mandatory requirement.

Here is an example scoring scale:

For a manageable scorecard, weight the ten categories and calculate the average criterion score within each. The following weights are an illustrative starting point for an omnichannel retailer, not the weights used in the published comparison:

Calculate each category's contribution as its weight multiplied by the average score of its in-scope criteria. If modeling fundamentals has a 15% weight and a supplier earns 6 points across its eight criteria, that category contributes 11.25 percentage points. Normalizing within categories avoids giving a category extra influence solely because it contains more questions.

Keep unverified items visible and retain their place in the agreed denominator. You can report a verified-points subtotal and evidence completeness, but do not present an incomplete assessment as a final score or improve a supplier's result by dropping unanswered items. Resolve mandatory unknowns before award. Apply any agreed out-of-scope exclusions consistently to all suppliers.

Have reviewers score their assigned areas independently, then discuss differences against the evidence. Record the reason for changes. Keep the capability score, mandatory pass/fail assessment and commercial comparison visible as separate parts of the decision.

Give every finalist the same demonstration brief

A demonstration should show how the proposed solution handles the retailer's decisions. Use the same scenarios and time allowance for every finalist, and identify which demonstrations use retailer data, anonymized customer data or a generic sample.

Five scenarios cover several of the framework's categories without requiring a separate presentation for every question:

1. Explain a promotional trading period. Show baseline, media and promotion effects across stores and ecommerce.
2. Build a constrained budget plan. Compare the current plan with a reallocation, retaining fixed commitments. Show the expected outcome and its limitations.
3. Investigate conflicting measurement. Use an experiment that differs from MMM or attribution. Explain the definitions, diagnosis and any resulting calibration.
4. Complete a routine user task for a performance marketer: Identify campaigns & ad sets across Google and Meta that should be scaled or cut, and get recommendations for optimal bid values.
5. Complete a routine task for the head of paid media: Compare marketing ROI across channels and identify improvement opportunities.

Ask the proposed delivery team to participate. Track whether each result comes from standard software, included analyst support, extra paid work or future development. For a limited trial, write down any excluded channels or capabilities. A digital-only preview cannot establish how the complete proposed solution will perform with offline media.

Use a separate technical session where necessary. Analytics reviewers need time for assumptions and diagnostics; business users need time to work through the decision. Both sessions should refer to the same scope and evidence log.

Use a pilot to resolve a defined uncertainty

A proof of concept is useful when the remaining risk cannot be resolved through documentation, demonstrations and references. Define the question first. It might concern a difficult data integration, whether the data supports the required retail detail, or whether teams can use the outputs in a planning cycle.

Agree a pilot charter before work starts. It should state scope, datasets, responsibilities, deliverables, cost, duration, acceptance criteria and the decision at the end. Identify which capabilities are being tested and which remain unverified.

Distinguish three milestones:

• Data readiness: the required inputs are delivered, mapped and reconciled.
• First usable insights: reviewed results can support the agreed decision.
• Observed business impact: the retailer has acted and enough time has elapsed to assess the outcome.

Ask when the supplier's delivery clock starts. “Twelve weeks” can mean twelve weeks from signature, kickoff or acceptance of complete data. The project plan should show the dependency explicitly.

Set acceptance criteria before results are available

Use the relevant framework criteria to define evidence for the pilot:

Set dataset-appropriate validation thresholds with analytics reviewers before testing. Record how holdout periods are selected and prevent their use in fitting or tuning for the final assessment. Where possible, use the same evaluation data and outcome definitions across finalists.

When replacing an incumbent, ask for a reconciliation of differences in inputs, assumptions and results. Exact reproduction of the incumbent's ROI should not be the sole acceptance test: agreement does not establish correctness. Require an explanation for material changes and evidence supporting the new estimates.

If the pilot is meant to demonstrate business impact, agree how actions and outcomes will be assessed. Record the recommendation, decision owner, implementation date and comparison method. A forecast of additional revenue remains a modeled forecast until there is evidence about the implemented change.

Finish with an explicit decision: proceed to the agreed rollout, resolve named gaps within a limited extension, or stop. Price any extension and clarify how pilot fees relate to a later contract.

Compare total cost against the same scope

Request a common pricing schedule even if suppliers use different charging models. A subscription, consulting engagement and internally operated framework can all involve different combinations of software and people.

Compare the complete initial term and a defined expansion scenario. Ask each bidder to map its fees to the same business coverage and service levels.

Define “refresh” in the pricing schedule. Specify whether it covers new data ingestion, recalculated outputs, parameter re-estimation, diagnostics, dashboard publication or a review meeting. Also define what triggers a separately charged rebuild.

For illustration, a retailer comparing a two-year commitment could calculate total cost as setup plus 24 months of recurring fees, required additional services, estimated internal effort and the agreed expansion costs. Use supplier quotes and the retailer's own effort estimates; keep optional work separate from the required total.

Compare implementation effort as carefully as subscription fees. If one proposal relies on weekly agency files and another includes maintained integrations, that difference belongs in the operating-cost discussion. Ask who corrects late or inconsistent inputs after launch.

The proposal does need to state the charging basis and included scope clearly enough for procurement to compare and contract for them.

Carry the evaluation into the contract and rollout

Turn the accepted proposal into a delivery schedule with named owners. Carry over the scope definitions, mandatory commitments, evidence still to be supplied and pilot acceptance terms. Ask procurement and legal to resolve the relevant contract issues before signature, using the retailer's standard review process.

Specify the start conditions and milestones for data acceptance, first results, validation, user access and training. Include the recurring service cadence, escalation routes and process for changing scope. Clarify rights to retailer data, exported results, documentation and any custom work, along with the practical arrangements for transition at the end of the service.

For an incumbent replacement, plan the handover around existing business deadlines. Preserve relevant data definitions and historical outputs so teams can explain changes. A phased rollout may let one business validate the new process before other markets move across. Price any overlap in service and assign responsibility for continuity.

Before go-live, schedule the first planning meeting that will use the results. Identify who prepares the scenarios, who can approve a budget change and how actions will be recorded. Training should cover those tasks for marketers and the review needs of analysts. Include a later review of actual usage and recurring workload so gaps have an owner after implementation ends.

Common mistakes in a retail MMM RFP

Several avoidable problems follow from imprecise requirements:

• Pricing based on a vaguely defined "number of models" metric. Instead, provide a detailed solution specification, which enables vendors to give accurate and comparable pricing.
• Data work discovered after award. A connector list leaves custom and agency feeds unresolved. Agree owners and recurring effort during selection.
• Evaluation criteria that change between finalists. Different scenarios or undocumented weights weaken comparability. Set the method first and record any revisions.
• A pilot judged only on historical fit or agreement with an incumbent. 
• A commercial offer that leaves expansion open. Define the scope and pricing assumptions for the next market or business unit before the initial award.

Frequently asked questions

How long should an MMM RFP take?

Use the selection timetable as a planning exercise for your own approval process. The twelve-week example above assumes a reasonably clear scope and excludes a substantive pilot and implementation. Work backwards from the first business decision, then add the time required for supplier responses, review, contracting and data readiness.

Should we require a proof of concept?

Require one when it will resolve a material uncertainty. Document the question it must answer and the evidence needed to proceed. Existing deployment evidence, a representative data review and a structured demonstration may be sufficient for some requirements; others need testing with the retailer's data.

How much data should we include in the RFP?

Provide an inventory and representative schemas first, then controlled access to samples where needed. State the available history and granularity. Ask suppliers to justify whether those inputs support the required outcomes and detail, including what additional data would change their assessment.

Can an open-source MMM framework participate in the evaluation?

Yes, through a proposed delivery arrangement with an internal team or implementation partner. Apply the same business and modeling requirements, then establish who will provide integrations, hosting, security, reporting and ongoing support. Include that work in the cost comparison.

Should we copy scores from a published MMM comparison?

Use the comparison to inform the shortlist and identify questions to investigate. Your procurement scores should reflect evidence for your own scope. A missing public source is different from a capability shown to be unavailable, and a published high score does not confirm that your data or delivery requirements will be met.

What should happen after a supplier is selected?

Approve the detailed scope and implementation plan, assign data owners and book the first review and planning sessions. Keep the requirements and acceptance record available to the delivery team. They provide the basis for checking that the purchased service is the one the retailer receives.

If you are preparing a retail MMM RFP, use the full evaluation framework alongside your scope schedule. You can also book a discussion with Sellforte to review your use cases and data readiness before requesting a proposal.

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.