Home On TV & Video MMMs Can Tell You If CTV Worked. They Can’t Always Tell You Why

MMMs Can Tell You If CTV Worked. They Can’t Always Tell You Why

SHARE:

Marketing mix models (MMMs) deserve the attention they are getting. Used well, they can pull brands out of a measurement culture that still gives too much credit to the click, especially in channels where clicking was never the point.

That is a healthy shift. A click can be accidental, low intent or simply easy to capture. Anyone who has opened Instagram has probably inadvertently tapped a Reel or ad. The platform still records the action. Leadership sees a number, and a weak signal eventually becomes a budget argument.

When properly considered, MMMs give marketers a broader view of how channels contribute to growth, where spend creates incremental impact and how different parts of the media mix interact. This is especially useful when so much marketing activity happens on mobile devices, while many conversions still happen elsewhere. A consumer may see the ad on a phone, search later from a laptop and convert on a desktop. Looking only at the last visible action misses the way demand is actually created. 

The best MMM work also helps marketers understand marginal impact. Not just whether a channel worked in the aggregate but what happens when the next dollar is added. For CTV, that discipline is badly needed. 

The problem is that many marketers expect MMMs to answer questions they were not built for.

One CTV number can hide a lot

CTV is often treated as a single channel in a model, but it does not behave like one in practice. A campaign might run across hundreds of networks, dozens or hundreds of audience segments, multiple devices, different inventory paths, varied frequency levels and different creative rotations. Two campaigns can both be labeled “CTV” and have very little in common operationally. 

If an MMM says CTV was incremental, the result is encouraging but incomplete. The reverse is even more dangerous. If an MMM says CTV underperformed, the conclusion can sound cleaner than it is. A brand might read that as a channel failure when the issue was actually a planning failure, a supply issue, a poor audience match, an overexposed segment or a bad test design.

Imagine a brand running one campaign on Hulu and another on Peacock. One produces incremental reach and downstream lift. The other does not. A channel-level readout may collapse both into the same CTV bucket. The result might be technically useful, but it does not tell the marketer what to do next with their budget.

That is where MMMs should be treated as calibration tools rather than arbiters of judgment. A good calibration tool can tell you something is off or where the mix needs attention. It can help a brand understand which channels are contributing. But it can’t isolate every ingredient inside a complicated campaign.

Geo-testing adds another layer of complexity. If a brand excludes one market from a campaign, but a small amount of media still reaches people in that market because of imperfect location signals or cross-device matching, the test can be contaminated. When the expected lift is small, even minor leakage can change the read. A 2% lift that appears as 1.8% may look like a rounding difference on paper, but it can influence millions of dollars in planning decisions.

This is not an argument against MMMs. CTV marketers should be leaning into them. Independent incrementality work is a necessary counterweight to platform-reported attribution, especially in a non-clickable channel. It helps marketers escape the false comfort of last-click reporting and gives finance teams a more credible way to evaluate media investment.

The better argument is for using MMMs in the right sequence. The model should always be to sharpen the next question rather than end the conversation.

  • Start with the model to understand the broader contribution and marginal impact. 
  • Use more granular diagnostics to figure out why the result happened. 
  • Look at network mix, audience composition, frequency, creative exposure, device distribution, geo delivery and the relationship between CTV and other demand signals. 

Accountability without oversimplification

CTV does not need to be judged by the same metrics that made search and social easy to buy. It also should not be excused from accountability because it is harder to measure. The channel can create demand, influence other channels and drive business outcomes. Proving that requires a measurement approach that respects the complexity of how media is bought and consumed.

MMMs are useful because they reduce the industry’s dependence on the click. They become risky when they replace one oversimplification with another. 

For CTV, the right use of MMM is: “What did we learn, what needs to be adjusted and where should the next dollar go?” 

That is the kind of answer marketers can actually use.

“On TV & Video” is a column exploring opportunities and challenges in advanced TV and video.

Follow Keynes and AdExchanger on LinkedIn.

For more articles featuring Dan Larkman, click here.

Must Read

Single continuous line drawing robot riding pencil rocket flying in the sky. Tech education and creativity. Future technology development. Artificial intelligence. One line design vector illustration

Q3: Omnicom’s Media Business Is Growing, But Is It Agent-Proof?

If AI agents can plan and buy media, who needs a holding company? Good question. It came up during Omnicom’s Q3 earnings on Thursday.

Netflix Touts New Measurement Capabilities As It Struggles With “Modest” Engagement Growth

Netflix’s revenue grew 12% YOY to $12.9 billion in Q3, which it maintains is in line with its forecast. Its ads business, however, is lagging slightly compared with expectations, causing Netflix to tweak its forecast for the rest of the year.

Apple Publishes Its Plan To Wipe Out Web Tracking

Apple is coming for identity resolution. WebKit’s latest tracking crackdown targets the infrastructure behind post-cookie IDs – and switching to a first-party setup won’t necessarily spare vendors.

Privacy! Commerce! Connected TV! Read all about it. Subscribe to AdExchanger Newsletters

AI Slop Sites Are On The Rise. Is Ad Tech Ready For Its New MFA Challenge?

Advertising supply chain intelligence company DecryptAds uncovered more than 2,100 AI-generated recipe sites that are monetized with programmatic ads and feature fabricated author profiles.

How Australian Home Lender Lendi Measures The Invisible Work Of AI

Most brands and businesses are trying to figure out how to improve their metrics when it comes to AI – and also what those metrics even are.

AI Startup Minerva Built An Agentic CDP, But Don’t Call It That

Minerva wants to turn fragmented customer data into something marketers can use, with AI agents doing the work behind the scenes.