The short answer
No. MMM can support daily campaign decisions when fresh data, incrementality testing, and attribution work together. Use MMM to calibrate attributed sales, translate the corrected results into campaign bids and budgets, and monitor changes within days. A quarterly reporting schedule does not have to dictate how often you learn or act.
Here's a summary video from Sellforte CEO, Juha Nuutinen:
This article is part of Asked by Marketers, a series answering real questions from marketing leaders.
Sellforte's team holds more than 1,450 meetings each year with marketing leaders in Ecommerce and Retail about Marketing Mix Modeling and incrementality testing. Each week, we anonymize at least one question from those conversations and answer it in depth, based on what marketers are actually struggling with, not what keyword tools suggest. About the series
Why marketers ask this
For an ecommerce or retail marketing team, a quarterly MMM report can arrive after the decisions it was meant to inform. Campaigns have changed, budgets have moved, and the team has spent weeks optimizing against the numbers available in Google, Meta, or its own attribution system.
The familiar division is MMM for strategic allocation and attribution for execution. But if those systems disagree about which channels generate incremental sales, the quarterly plan and the daily campaign decisions can pull in different directions. Updating the report more often helps only if its findings change how the team buys media.
How does MMM become useful for daily campaign decisions?
Start by using MMM and incrementality evidence to calibrate the attribution numbers your performance team already uses. That lets the team keep its campaign detail and familiar workflows while steering against estimated incremental sales.
Marketing Mix Modeling estimates how media and other factors contribute to sales. Incrementality tests provide experimental evidence about what changes when advertising is increased, reduced, or withheld. Calibrating MMM with that evidence helps establish the incremental return for the channels and campaign groups you need to manage.
The connection to attribution is an incrementality factor: a multiplier that adjusts attributed revenue toward the incremental revenue estimated for the corresponding activity. To calculate it, compare results for matching campaigns, dates, and sales definitions. A factor for net sales cannot be applied indiscriminately to attributed revenue before returns.
Keep meaningful campaign differences in that calibration. Prospecting and retargeting, or traffic and awareness campaigns, can need different factors. Applying one average correction across a whole platform can hide the very differences you need for a budget decision.
This gives you a calibrated estimate at campaign or ad set level. The detail still comes partly from attribution and the assumptions used to distribute the measured effect. It does not mean every individual ad has its own independent incrementality test.
What should the media team do with the corrected numbers?
Use them to set campaign targets and daily budgets that reflect the incremental return the business needs. The output should identify the campaign or ad set to change and the relevant bidding parameter.
“Increase Google Performance Max” leaves a performance marketer with plenty of work to do. If there are 20 campaigns, which ones should grow? What target ROAS should each use? For Meta, which ad sets should receive a higher daily budget?
MMM helps with this decision through advertising response curves, which estimate how sales change as spend changes. Look at the expected incremental return from the next dollar. A campaign can have a strong average incremental ROAS while having little room to absorb more budget profitably.
Translate that opportunity into the settings the platform actually uses, such as target ROAS, target CPA, or daily budget. Keep the distinction between the business target and the platform setting clear. If a platform is bidding against uncorrected conversion values, its target ROAS uses a different basis from your incremental ROAS target. The recommendation must account for that difference.
Does the MMM itself need to update every day?
For daily steering, the measurement system needs fresh inputs and regularly updated estimates. Sellforte’s approach is to automate data collection and retrain the model overnight when daily data is available, so the team can work with results through the previous day.
A dashboard refreshed this morning can still contain an old model. Check when the underlying data was last received and when the model was last trained. A new campaign or a change in performance needs to enter the measurement process before it can inform the next decision.
Data availability sets a practical limit. Digital media data can arrive daily while offline media data arrives later. Make those differences visible and review the estimates as delayed inputs arrive. A daily run cannot create information that has not been collected.
Quarterly planning can continue alongside this process. The annual budget, a quarterly business review, and tomorrow’s campaign settings serve different decisions. They can use the same measurement system without sharing one update schedule. Our guide to how often to retrain your MMM covers the model update process in more detail.
What can you learn within a day or two?
You should be able to start assessing whether a campaign change is moving spend and estimated incremental sales in the expected direction. That is the feedback loop a performance team needs when it makes changes every day.
Record the original setting, the new setting, and the expected outcome when you apply a change. Then review what happened. If a target ROAS adjustment was intended to let a campaign scale, did spend increase? Did the additional spend produce the expected sales response? Use that evidence to decide whether to keep, revise, or reverse the change.
The first day’s result is an early signal. Conversion delays, ordinary sales variation, and longer advertising effects still matter. Review performance over a period suited to the campaign before drawing a firm conclusion, and use incrementality tests where stronger causal evidence is needed. A simple increase in sales after a bid change does not, by itself, show that the bid change caused it.
The aim is to begin learning tomorrow or the day after. Waiting for the next quarterly presentation leaves the team buying media throughout that period without incorporating the new evidence.
How this looks in practice
Consider a hypothetical ecommerce team comparing two campaign groups. Each spent $1,000 over the same period. Assume MMM and experiment evidence support separate calibration factors, the revenue definitions match, and the factors remain applicable to the activity being reviewed. All figures below are illustrative.
| Campaign group | Attributed sales | Calibration factor | Estimated incremental sales | Incremental ROAS |
|---|---|---|---|---|
| A | $10,000 | 0.30 | $3,000 | 3.0 |
| B | $4,000 | 1.50 | $6,000 | 6.0 |
Attribution makes group A look much stronger. After calibration, group B has the higher estimated incremental return. A factor above one is possible when the attribution system misses sales impact; it is a correction to attributed sales, not a percentage of all sales that are incremental.
The team now checks the response curves and its required return. If group B has room to scale and the expected return on additional spend meets that requirement, it identifies the campaigns or ad sets to increase. The recommendation translates that increase into their bidding targets or daily budgets.
After applying the changes, the team checks the next available data to see whether spend moved as intended and starts tracking the sales response. It continues reviewing as conversions arrive. The quarterly review can later summarize the decisions and their outcomes, but the team has already used the measurement to change its buying.
How Sellforte helps
Sellforte combines MMM, incrementality testing, and calibrated attribution to support campaign and ad set decisions. Sellforte Performance translates those insights into bidding and budget recommendations, with tools to apply changes and review their effects. Book a demo.
Authors
Lauri Potka is the Chief Operating Officer at Sellforte and has 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, where he is one of the leading voices in MMM and marketing measurement.