Perspectives: The Easier Way to Drive Higher Marketing ROI

Perspectives: The Easier Way to Drive Higher Marketing ROI

With the advancements in big data, advertisers know more about consumers than ever before. And yet, they’re still challenged with how to drive the greatest return for their marketing budgets.

Nielsen’s database of more than 40,000 marketing activities (ranging across vehicles such as TV, print, digital, radio, outdoor and trade spend) across more than 30 countries shows that the average return on investment is just under $0.70 for every $1 spent—meaning many marketing budgets are delivering less incremental profit than it costs to run them. And we all know what happens when executives don’t see the ROI they’re expecting—they cut budgets.

Increasing ROI isn’t easy, and most levers aren’t even squarely in the marketer’s control. For example, reducing media cost per impression is one way to improve ROI. That’s easy to say, but try to find a media company that will simply offer a lower rate without sacrificing placement quality. Making better ads that resonate more with your target audiences to drive purchase is another way, but that’s already Job 1 for marketers, and it’s almost impossible to do that reliably. Another way is to leverage the power of bigger brands that are already household names. Ads for well-known brands will generate more sales than ads for lesser-known ones simply because consumers are already familiar with them. But unfortunately, there isn’t a way to magically make your brands suddenly bigger.

But there’s another way marketers can drive more profitable marketing budget decisions without changing their media costs, creative or brand size: Use simulation and optimization tools, which help marketers better allocate their marketing dollars based on existing brand size, media costs and ad copy effectiveness.


Marketing plans can be executed in thousands of ways, from how much to spend to how to allocate spending across your brands, media vehicles, campaigns and weeks. Trying to figure out which combination of actions will deliver the strongest return would be nearly impossible to calculate manually. That’s where simulation and optimization tools come into play, as they use robust data sets of marketing results and complex calculations to determine the best allocation for your brand. Imagine if you could easily and quickly simulate what might happen if you change your media plan, or automatically sort through thousands of spend options to come up with the best approach.

These tools help marketers understand how they should allocate marketing dollars across media types, brands and time periods to drive the best return. So you don’t have to move mountains to get a better return, but rather use the tools to tell you how to more effectively navigate the mountains.

Despite being built on sophisticated data modeling engines, the best simulation and optimization tools are easy to use and clearly show the impact of spend change decisions. At their core, these tools factor in countless variables and scenarios to provide marketers with prospective strategy options across their entire portfolios. They estimate the impact of each marketing effort. The outcome can help you determine how to shift spend by shifting spend by product, by tactic, by week to achieve the highest return.

In the real world, simulation and optimization tools are the most effective ways to answer some pretty critical questions. “What would happen if I spent $5 million on TV instead of $3 million?” “How should I allocate my $10 million marketing budget to drive the most profit?” Or the more-often-seen, “How would a cut in my media budget affect sales?” In our experience, optimizations have identified an average of 10%-20% increase in ROI for the same dollars spent. That 10%-20% represents millions of dollars in incremental profit.


Example 1: A pharmaceutical company was looking to improve the impact of its TV efforts. Using Nielsen’s marketing simulation and optimization tools, the company learned they could drive 15% more incremental volume with the same TV spend by better timing advertising with seasonal peaks in demand for their product. Specifically, they learned that advertising more heavily in April and slowly reducing spend every month through September would have a better result than spreading spend out equally month by month.

Example 2: A beverage company wanted to understand if there was a better way to allocate its marketing budget compared with what its media agency was planning to do. So the company used Nielsen’s marketing optimization tools to identify an opportunity to gain 30% in incremental sales volume if it reduced spend on TV, banner and digital coupons and shifted it to print, online video and search. For the same spend, the company could achieve a higher sales return by shifting spend to a more impactful level by marketing vehicle to maximize the impact.


Simulating and optimizing a media plan requires a few key data inputs to inform assumptions and calculations:

  • The estimated impact from a marketing activity, also known as a “response curve,” or how much sales will increase at any given level of activity
  • The cost per point for each media type
  • The assumed margin per sales unit to understand the return at a given level
  • Baseline sales levels for the brand ( e.g., assumed sales in absence of marketing)

Of the necessary variables, the estimated impact from a marketing activity is the most challenging to obtain. This is where Nielsen’s unmatched analytics and benchmark response database can help. Estimated impact of marketing activity data can come from one of two places: historic data for the brand or Nielsen’s database of marketing mix studies from other brands.

With historic data available, brands can use marketing mix modeling to assess the impact of their specific brands’ marketing activities on sales.  Marketing mix models tease out all sales drivers and identify how different marketing activities influenced their brands.  This relationship is then used as the basis for simulating and optimizing marketing scenarios in the future.  If brands don’t have marketing mix model results for all or some of their brands, or for certain marketing vehicles that they’d like to possibly incorporate into their media plan—they can infer marketing impact from Nielsen’s database of more than 1,600 marketing mix studies, which cover the results of over 40,000 tactics in more than 30 countries. This database offers simulation and optimization to brands that have never done marketing mix modeling before and want to understand how the results of other brands in their categories can inform their marketing budgets.

While it’s still true that there is no silver bullet to improving your marketing ROI, advertisers willing to delve into analytics-driven strategies are realizing attainable and dramatic ROI gains by employing optimization and simulation tools to drive their marketing efforts.