If your business runs seasonal sales events, promotional pricing windows, or holiday discount campaigns, you already know how dramatically they can move the needle. What you may not know is how they interact with your Marketing Mix Model, and why ignoring them can lead to some genuinely misleading conclusions about your media.

This article explains the relationship between promotional pricing and MMM, what Paramark needs from you to model it properly, and what to expect in your results when promotions are a meaningful part of your business model.

The Core Problem: Promotions and Media Run at the Same Time

Most businesses do what intuitively makes sense: when a big sale is coming, they ramp up media spend to drive as many customers as possible to take advantage of it. The promotion and the media push happen together.

This creates a modeling challenge. When an MMM only sees "media spend went up and conversions spiked," it cannot automatically distinguish between two things:

• customers who converted because they saw an ad

• customers who converted because the price dropped

Without a promotional signal in the model, the spike in volume gets attributed to media channels. That inflates their apparent efficiency. Then, when the promotional period ends and volume returns to normal, those same channels look like they are underperforming, even if nothing about your media strategy changed. This is the cycle that promotional modeling is designed to break.

What Happens When Promotions Are Not in the Model

If promotional activity is not captured in your MMM, the most common effects are:

• Your channel efficiency looks artificially strong during promotional windows, because the model gives media credit for volume that the discount actually drove.

• Your channel efficiency looks artificially weak in non-promotional periods, because the model's history includes those inflated peaks and treats them as the baseline for what "good" looks like.

• Budget decisions made from these readings are likely to over-value channels that happen to run during sale events.

• Understanding the true incremental value of your media becomes significantly harder.

None of this means the model is broken. It means it is missing a variable that matters for your specific business.

How Paramark Models Promotional Activity

The most accurate approach is to model promotions as a non-media variable: a separate signal in the model that accounts for the pricing or promotional environment during any given period. This tells the model that something other than media was driving volume, so it can attribute lift accurately across all channels.

What this requires is a promotional calendar from you. Specifically, Paramark needs:

• Dates: when each promotional period started and ended.

• Discount depth: the nature of the offer, such as percent-off, dollar-off, buy-one-get-one, free shipping, or a combination.

• Channel scope: whether the promotion ran on your DTC site, through Amazon or retail partners, or across all channels at once. A promotion that runs only on your website behaves differently than one distributed across every retail channel simultaneously.

• Recurring vs. episodic: whether promotions follow a predictable cadence (like a quarterly discount cycle or a 52-week promotional pricing calendar) or happen as standalone events.

The more complete your promotional history, the cleaner the model can be. If you have gaps, your Growth Advisor can work with what you have and flag where interpretation should be more cautious during those periods.

What to Expect Once Promotions Are Modeled

Once promotional activity is captured in the model, a few things typically happen.

• Your channel efficiency estimates become more stable month to month, because the model can separate media lift from promotional lift rather than blending them together.

• You get a cleaner picture of what your media is actually driving, independent of your pricing strategy.

• Promotional periods stop being sources of confusion in your monthly readouts.

• Your baseline becomes more accurate, because promotional volume is no longer being misattributed to channels.

In practical terms: if you ran a Black Friday campaign and your Google and Meta spend both looked exceptionally efficient that week, once promotions are modeled you may see those efficiency estimates moderate somewhat. That is not a sign the channels got worse. It is the model correctly separating what the 30% discount drove from what the ads drove. That more conservative number is the more trustworthy one for making future budget decisions.

Promotions vs. Seasonality: A Quick Distinction

Promotions and seasonality are closely related but not the same thing, and your MMM accounts for both differently.

• Seasonality captures predictable demand patterns that repeat year over year based on time of year: Q4 being strong for e-commerce, January being high-intent for fitness apps, summer being soft for SaaS. These patterns are built into the model automatically through calendar variables.

• Promotions capture demand spikes driven specifically by pricing incentives: your Black Friday sale, a flash sale in March, a product launch discount. These require you to share data with Paramark because the model does not know they happened unless you tell it.

Many businesses experience both simultaneously. A holiday sale event in November is both seasonal (Q4 demand is naturally elevated) and promotional (the 25% discount is an additional driver on top of that seasonal baseline). Modeling both correctly is what allows the model to isolate what your media contributed on top of everything else.

A Note on Media Strategy During Promotional Windows

One question that often comes up: should you run more or less media during a promotional event?

The MMM can show you what channels looked efficient during past promotional windows, but it cannot cleanly separate how much of that efficiency came from the discount versus the ads without controlled testing. The most precise answer comes from a geo-based incrementality experiment run during a sale event: running media in some regions and holding it back in others lets you directly measure what the media added on top of the promotional pull.

The general Paramark guidance in the absence of that experimental evidence is to avoid dramatic budget changes during your largest promotional windows. Customers who are actively in-market due to a sale are more likely to convert, which means your media may genuinely be more efficient during those periods. But how much of that is real versus a promotional artifact is exactly what experimentation is designed to answer.

Your Growth Advisor can help you design an experiment around your next major promotional event if you want to answer this question with confidence.

When your MMM can (and can't) help you understand the impact of your promotions

Regardless of how your promotional strategy is structured, sharing promotional data with Paramark ensures your channel attribution stays accurate. That is what the earlier sections of this article describe.

There is a separate question customers often ask: can the MMM also tell you how much lift your promotions themselves drive? The answer depends on the structure of your discount strategy.

Your MMM can estimate promotional lift when:

• your discount depth varies meaningfully over time, week to week or day to day, in a way that is comparable to how your media spend varies.

• the model learns the relationship between discounting and conversions by observing how they move together.

• if your discount depth shifts by 5 or more percentage points on a regular basis, the model has enough signal to work with. At 10 or more percentage points of regular variation, the signal is substantially stronger.

In short: the more variation there is in your discount depth, the more the model can tell you about what your promotions are actually driving.

Your MMM cannot reliably estimate promotional lift when:

• your discount depth is essentially constant, or changes only once every few months. There is nothing for the model to learn from if the input never varies.

• the change in discount depth is too small to affect customer behavior. A one-percentage-point shift in discount is unlikely to move conversions in a detectable way.

• your customers anticipate your promotions and delay purchases to take advantage of them. If shoppers hold off buying because they know your sale is coming, the spike you see during the promotion partly reflects demand that was going to happen anyway, just shifted from the weeks before. The model can see the spike, but it cannot cleanly separate net-new demand from demand that was simply delayed.

If your promotional structure falls into one of these categories, the MMM's primary value with your promotional data is keeping your channel attribution clean. For understanding the lift your promotions themselves drive, a geo-based incrementality experiment is the more precise tool. Your Growth Advisor can help you design one around your next major sale event.

Getting Started: What to Share With Your Growth Advisor

If you have not already shared promotional data with Paramark, the simplest starting point is a spreadsheet with three columns: date range, promotion type, and discount depth. Start with your major annual events first, then add smaller or episodic events as you can.

If your business runs on a rolling promotional pricing calendar — for example, alternating between full price and two or three discount tiers throughout the year — share that cadence and your Growth Advisor will work with your team to structure it in a way the model can use.

The more context Paramark has about your promotional strategy, the more accurate and trustworthy your MMM results will be. This is one of the highest-leverage inputs you can provide.

Want to learn more about marketing mix modeling? Check out other Paramark marketing mix modeling articles here