Laura GroverSeptember 27, 20235 min read

A Real-Time World Demands More Than Your MMM Can Offer

As corporate America responds to rising inflation and diminished consumer buying power, marketing organizations have been forced to make budget cuts to preserve profitability. Indeed, a World Advertising Federation report released at the beginning of the year found that 30% of major advertisers are cutting budgets in 2023. Meanwhile, a recent report from Dentsu projects that television ad spend will drop by 3% throughout the year.

The upshot? Seventy-five percent of chief marketing officers now say they’re under pressure to do more with less.

But how, exactly, can marketers maximize their performance on a reduced budget? 

Right now, many marketing organizations make their budget allocation decisions using an advertising measurement tool known as Marketing Mix Modeling (MMM). Also known to some as Media Mix Modeling, MMM uses a complex time-series regression model to analyze the entirety of a brand’s sales data and all of its multi-channel marketing spend. This enables marketers to measure each channel’s contribution to sales and determine how they might allocate budgets more effectively moving forward. 

But while MMM is a highly effective tool for making high-level budgeting decisions, it lacks several key capabilities marketers need to make intelligent budget cuts in a tough economy—particularly when it comes to television advertising. If marketers want to ensure that their reduced TV budgets are spent effectively, they need additional, complementary tools to illuminate the blind spots increasingly inherent in MMM.

MMM provides a bird’s-eye view of marketing performance—but what’s happening on the ground?

Advertisers rely on MMM because of its unique ability to provide a bird’s-eye view of their entire marketing portfolio. Indeed, MMM uses a massively complex data model that ingests several years’ worth of sales data from every point of sale, as well as all of the marketing data from across every single channel. This includes public relations spend, promotions, linear television, streaming video, radio, digital, print, out-of-home—the whole shebang.

As a result, MMM is an invaluable tool for making high-level marketing decisions. For instance, it can tell you that you would have earned 5% more revenue last year if you’d shifted 10% of your budget from print media to digital. Or, it might tell you that you’d have optimized your performance if you’d moved 15% of your linear television budget from the New York market to the Los Angeles market.

But for all its strengths, MMM is missing two key ingredients that marketers need to make smart optimization decisions: granularity and immediacy.

Indeed, the high-level insights MMM provides are crucial to figuring out which channels to spend on, but it leaves a number of important questions unanswered when it comes to maximizing performance within each channel. For instance, MMM can tell you that you’re underspending on streaming television. But if you’re allocating additional funds to that channel, you need more granular information about the networks you should be spending on and the audiences you should be targeting.

Meanwhile, the complexity of the MMM regression model means that there’s a lag time between when you conduct your analysis and when you’re able to apply what you’ve learned. Even an effective model might be giving you information about last year’s budget that you can only use to plan for next year. If your boss walks into your office one day and tells you that you’ll have 15% less budget to spend in the upcoming quarter, you need information about what’s happening with your marketing performance right now.

Real-time outcomes data helps marketers make intelligent decisions for the immediate future

The gaps in MMM are especially prevalent in the worlds of linear and streaming TV. As marquee advertising channels with premium rates, marketers need their spend to drive real results. In order to do that, you need insight into how effectively your television advertising is driving consumers to take action. This means getting information that’s granular enough to help you make small, meaningful shifts in spend—and fast enough that you can make those shifts immediately.

It also means being able to make predictions about what will happen when you try something you’ve never done before. Since MMM relies on historical data from your past media buys, your MMM won’t be able to provide insight when you’re testing out your first major streaming TV buy.

With TV advertising outcomes data, you can measure the effectiveness of each individual ad you run within minutes of when it airs. For instance, this information can tell you how effectively your 11 a.m. ad on “The View” drove viewers to engage with your brand online. Or it might tell you that your ads on the USA Network are vastly outperforming those that run on TNT. Or you might learn that one piece of creative in your campaign is delivering far more value than the others when it runs during live sports.

This real-time data enables marketers to quickly and easily optimize spend—even amid budget cuts. With it, you can make intelligent decisions about the creative messages you show viewers, the networks and kinds of programming you advertise against, and the dayparts during which you advertise. In short, this information provides the granularity and speed you need in a rapidly changing advertising marketplace.

Your marketing team deserves the best of both worlds

When you combine MMM with TV advertising efficacy data, you’ll be able to maximize television advertising effectiveness without spending more on television advertising. By pairing the high-level insight of MMM with the granular, real-time information of TV efficacy data, marketers can make intelligent long-term plans with the flexibility to adjust spending for additional performance on the fly.

At a time when CMOs are being asked to do more with less, it’s never been more important to make every dollar of your TV advertising budget count. Why not give yourself all of the information you need to make the smartest possible media-buying decisions?


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