Optimizing Media Spend: Strategies and Insights

Terence Einhorn, Sr. Solutions Consultant in Sales

Published 01/30/2024

Understanding Media Optimization

What is Media Optimization?

Media Optimization is the process of maximizing the ROI(i), or the Incremental Return on Investment, of a given media/marketing investment by either changing the allocation of that investment or improving the performance of the Marketing vehicle in which it’s invested.

Why Is Optimizing Media Spend Important?

We’ve all heard the classic axiom, “Half of my marketing investment is wasted; the problem is I don’t know which half.” 

While this is an oversimplification of the marketing optimization problem, it still rings true that if we knew which marketing vehicles truly worked versus those that didn’t, we could make much better use of our limited marketing resources.

In a world where marketing departments are increasingly asked to do more with less, making the best use of the budget you have is critical to the success of any brand.

Key Strategies for Optimizing Media Spend

In general, there are three key approaches to optimizing media performance:

Creative Optimization - Creative Optimization is the process of tailoring messaging, content, style, or the format of advertisements for them to have the most compelling impact on customers.

Channel Optimization - Channel optimization entails altering the mechanics of ad delivery, whether that involves audience targeting, on-platform bids, timing or pulsing, etc., to maximize performance and, ultimately, the ROI of that particular channel.

Allocation Optimization - Allocation Optimization is the practice of optimally distributing a marketing budget across different channels, campaigns, regions, or any other relevant dimensions to maximize overall return on investment.

Creative Optimization is generally (but not exclusively) a more qualitative process based on research and strategy. As such, we will focus more on channel optimization and allocation optimization in this post.

How Do You Measure Media Performance?

All three of the aforementioned approaches for optimizing media spend are contingent on measurement. 

In short, if you don’t have a clear idea of how your media is performing in the present and how specific attributes impact that performance, it’s impossible to know what you need to change to maximize performance in the future.

There are several methods to measure the current performance of a given marketing channel or campaign; for a full rundown of the strengths and weaknesses of these methods, see our overview on incrementality, media mix modeling (MMM), and multi-touch attribution (MTA).

Generally speaking, the gold standard for advertising measurement is incrementality experimentation, as this is the only technique that can credibly assess the causal impact of media on sales.

Channel Optimization: Analyzing Performance Metrics

While ROI(i) is the most important metric for media performance, all other metrics contribute to this final number, too, and here’s the kicker - they all tell you something different about how an ad is performing.

In general, there are three metrics that are most important to understanding ROI(i):

  • Cost Per Impression (CPM)/Cost per Click (CPC)
    • This represents how “expensive” it is to buy media
    • This can be impacted both by broad market factors, as well as your own buying strategy
  • Conversion Rate (CR)
    • This represents the responsiveness of customers to your media
    • CR can be affected by seasonality, frequency, targeting, creative, product, and many other factors
    • It can be expressed in different forms, using either impressions or reach, different conversion events, etc.
  • Incrementality
    • This represents the existing propensity of customers to purchase your product prior to receiving an ad
    • Mostly driven by audience targeting and general brand awareness

For example, if you see your ROI(i) decreased from last month, it could be because:

  • CPM increased (the ads became more expensive)
  • The conversion rate decreased (e.g., your new creative is less compelling, or perhaps you’ve entered a less seasonal period for demand)
  • Incrementality decreased (you are targeting a higher-propensity audience, meaning more of them are likely to convert regardless of advertising exposure)

It’s important to monitor all of these metrics individually so you know which levers to pull or how to adjust strategy accordingly in order to improve ROI(i).

For example, if your conversion rates are lower than usual, perhaps an update of fresh creative and messaging is in order.

Or if CPM is higher than normal, it may be best to deploy a bid-cap on the platform, or lower overall spend in an effort to bring CPM back to typical levels (that said, CPM is often driven by market factors and not totally in one’s control).

It should also be mentioned that these factors are not truly independent of one another.

For example, as you target more “qualified” or interested audiences, CR tends to increase, while incrementality tends to decrease (exposing an audience with a higher propensity to purchase will yield more conversions per user, but it also means more of these conversions were likely to have happened regardless).

As such, keeping a close eye on various platform metrics, in addition to routinely testing incrementality, is key to continuous channel optimization.

Allocation Optimization: Maximizing Total Performance

The goal of allocation optimization is to distribute funds across different marketing vehicles, regions, products, or any other dimension in an effort to either:

  1. A) Maximize sales for a fixed budget 
  2. B) Minimize spend required to reach a specific sales goal 
  3. C) Maximize spend while maintaining a profitable ROI(i).

Though strategies A and B satisfy certain use cases, the best optimization strategy, generally speaking, is strategy C, as it maximizes the number of sales that can be driven by marketing, all else being equal. 

Strategy A runs the risk of leaving profit on the table if total ROI(i) is well above “breakeven” after every dollar in the fixed budget is invested, or conversely could become unprofitable if the budget is too large.

Strategy B could similarly leave profit on the table if the given sales goal is too low (a higher goal could be achieved profitably with additional investment) or could spend well beyond the point of profitability to achieve a goal that was likely set too high to begin with.

In either case, the optimization principles work the same: reallocate investment from the channel with lower mROI(i) to higher mROI(i) until total ROI(i) is maximized across the entire portfolio.

Note: (mROI(i) = Marginal Incremental ROI, or the Incremental Return on Investment of the next dollar invested in a given channel. This is different from ROI(i) due to the law of diminishing returns. The channel with the highest ROI(i) may not necessarily have the highest mROI(i) depending on its current position on the diminishing return curve).

As money flows into a Channel, the mROI(i) (return on the next dollar invested) will decrease due to diminishing returns. As such, we generally want to keep spending money on a given tactic until the mROI(i) has reached the profitable “breakeven.”

This allocation is generally performed using an Optimizer or a tool/program that can automatically calculate the best channel to allocate the theoretical “next-dollar” until all the dollars within a budget are allocated or until “breakeven” mROI(i) is achieved across the board.

The Optimizer typically implements efficiently designed mathematical optimization algorithms that search through all possible media allocations and arrive at an optimal mix that best achieves the specified goals while working within certain business constraints.

In theory, an optimal allocation would yield every channel showing equal mROI(i), right at or just above “breakeven.” This rarely happens in actuality, as many channels are constrained by real-world factors that inhibit investing or divesting freely. Examples include:

  • Pre-committed and paid budget for next year's sponsorship deal with X sports league that cannot be reallocated
  • Paid search spend, which is demand constrained by how many people search for your keywords
  • Minimum budget requirements for media channels like national Linear TV 

Leveraging Technology & Data

There are a myriad of tools and datasets that can help brands optimize their media within the three approaches above. Here’s a brief list of the most common:

DatasetsTools
Creative Optimization Customer Data
Research Data
Syndicated Survey Data
Focus Groups/Survey Platforms
Research Firms (Gartner, Mintel, etc.)
DCO  - Dynamic Content Optimization
Channel OptimizationVendor Reporting
Media Market Research
Agency Benchmarks
3rd Party Channel Optimization Platforms (Smartly, etc.)
On Platform Optimization Tactic (Google Performance Max, etc.)
Allocation OptimizationHistorical Media and Sales Data
Vendor Reporting
3rd Party Measurement Provider
Incrementality Experimentation
Optimization Software

Getting Started with Optimizing Media Spend

Initial Steps

The first step to optimizing media spend is a strong data foundation. There are many brands that handle the aggregation and consolidation of various data sources themselves, though this can be a laborious and resource-intensive process. There are also many third-party providers that can facilitate this process.

Either way, having a consistent and reliable data architecture is critical to ensure you spend less time gathering data and more time analyzing it.

The second step is to deploy a systematic framework by which to analyze data and make subsequent decisions. After all, optimization is about ACTING, not analyzing. Many brands fall into the trap of “paralysis by analysis,” but this pitfall can be avoided by formalizing a cadence of reporting and making decisions and avoiding open-ended analysis that wastes time and resources with no clear end goal.

The third step for more sophisticated brands (and those with higher budgets) is to leverage a specialized third-party provider to facilitate more efficient data consolidation and reporting, systematic incrementality measurement, and automated budget allocation, among other various applications, to supercharge the performance of their marketing budget.

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