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What is Media Mix Optimization?

Media mix optimization is an analytical process during which marketers evaluate the performance of various campaigns to determine which have a positive impact on their core audience, and which do not. With this information, marketers can then optimize their media mix, investing more time and capital in the channels and messages that better resonated with consumers.

As consumers have become more adept at tuning out marketing messaging, creating a tailored, positive customer experience has become a number one priority for marketers. To do so, marketers are investing more in campaigns, with marketing budgets growing steadily over the last few years. However, recently, marketing budgets have stalled, with executives and C-suites looking for hard data that demonstrate the impact of these campaigns and their precise effects on ROI.

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This is why media mix optimization has become so important to marketers. The insights derived from this data allow them to create personalized campaigns that target consumers at the person level, thereby increasing ROI.

Ultimately, the goal of media mix optimization is to discover the types of messages consumers respond to, and then optimize campaigns to ensure they meet these requirements on the right channel at the right time.

The Importance of Media Mix Optimization in Modern Marketing

Media mix optimization plays a crucial role in modern marketing, as it helps to achieve two main goals:

  • Create the most tailored customer experience  
  • Maximize marketing ROI

Media mix optimization meets both of these needs. By evaluating data on engagements across touchpoints, marketers get an understanding of what channels, creative, timing, etc. that an individual will engage with. Marketers can then devote more time and money creating those experiences for that user.

In turn, this user is more likely to have a positive perception of the brand. Moreover, through omnichannel efforts, the brand can ensure they reach the consumer with these optimized messages the moment they decide to shop or make a purchase, thus increasing ROI.

Optimization Challenges for Marketers 

There are three core questions marketers need answered when trying to determine where to optimize their campaigns.

The main overarching questions is: where should I spend my money?

This question can only be answered when these feeder questions are answered:

  • Which channels do my target audience use?
  • When and what type of creative and messaging does my target audience respond to?

These are challenging questions to answer. Learning this information to ultimately know where marketers should spend their money requires that they have in-depth analytical capabilities.

Understanding where, when, and with what message to target consumers most effectively requires marketers to deploy advanced attribution models that offer granular person-level data on each touchpoint engaged with while considering external factors.

This leads to two of the major challenges affecting media mix optimization.

Selecting and Configuring Attribution Models

To get the data required of media mix optimization, marketers need to determine which type of attribution model will give them the best data on their campaigns. For example, some models are better suited for digital campaigns, while others will provide better data on offline campaigns. From there, they need to ensure they have the analytical processing power to distill vast quantities of big data into actionable insights that can be leveraged for in-campaign optimizations.  

Making Sense of Your Data 

It is not enough to just have this data; marketers must be able to make sense of it. This requires competencies in data literacy. Marketers must be able to evaluate the data yielded from their attribution models and marketing analytics platforms to form insights on their target customers and the current market.

Data literacyunderstanding and applying insights derived from datahas been documented as a major roadblock to organizational success. This is the second challenge marketers face when optimizing their media mix. They need personnel who can derive quality analytics, otherwise these numbers will never steer them in the right direction. This can actually lead to misattribution that causes them to spend more on less effective tactics.

Common Mistakes in Media Optimization

Based on these core challenges, some of the most common mistakes when it comes to media mix optimization are relying on the wrong attribution models and misattribution in general.

Marketers need to select marketing attribution models that will give them the most information on an individual customer's journey to purchase. Therefore, selecting the correct attribution model is crucial.

For example, let’s say that an organization relies on a last-touch attribution model. The consumer might see a display ad and visit the online store but not make a purchase. Later, they might receive an email that brings them back to the store for eventual purchase. This attribution model would give full credit for the sale to the email. Marketers may then refocus all of their campaigns for that user around email, when the display ad is what initially piqued their interest.

This results in a campaign being optimized around inaccurate data.

Today, there is no one attribution model that tells the entire marketing story. Unless marketers leverage a unified measurement, which combines the information from multiple attribution sources (such as media mix modeling and multi-touch attribution), they will not have a complete understanding of their marketing effectiveness, and therefore where they should optimize.

Steps to Optimize the Media Mix

To effectively optimize the media mix for increased ROI, marketers need five core capabilities.

Unified Customer and Market Data: Marketers must collect person-level data on how each customer engages with their touchpoints across channels. This goes beyond just online campaigns. Effective optimization will require accurate offline measurement that can be correlated with online campaigns to determine how each contributed to a conversion.  Additionally, they will need aggregate data, offered by media mix modeling, to understand how external factors impact purchase decisions. To compile all of this information, marketers must leverage unified marketing measurement.

Advanced Analytics Platform: Marketers must then deploy an advanced analytics platform that can process all of the data collected through the various models that make up unified measurement. This platform will aggregate and normalize this data in a way that provides marketing teams the context they need to develop actionable insights, which will translate to an optimized media mix.  

Real-Time Insights: Beyond just providing this data, the analytics platform must be able to do so at a speed that enables in-campaign optimization. This is part of the reason methods like media mix modeling are not reliable today. They require long-term data that does not allow for real-time updates. Faster data acquisitions means that marketers can evaluate findings and make updates to enhance the user experience right away.

Data Literacy: As stated, the ability to turn this data into action requires a high degree of data literacy. With no way of evaluating the raw data, marketers can easily find themselves relying on inaccurate insights to optimize their campaigns. 

Brand & Creative Measurements: This step is often forgotten, as it is challenging to evaluate this type of more qualitative data. However, it is an important part of media mix optimization. Marketers must be able to understand how the perception of their brand impacts a purchase decision, as well as which creative is resonating most with target consumers. 

Media Optimization Software and Tools

When selecting a media mix optimization tool, marketers must ensure it is providing quality analytics with strong statistical backup. It is important that marketers have a very transparent understanding of their approach to the algorithm they use and the analytics process.

From there, ensure you are using a modern marketing analytics platform that provides:

  • Real-Time Analytics and Insights
  • Brand Measurement Capabilities
  • Granular, Person-Level Data
  • The Ability to Correlate Online and Offline Attribution Metrics
  • Contextualized Customer and Market Insights

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