Today鈥檚 marketing landscape is constantly evolving, and as a result, marketers are finding it increasingly difficult to accurately understand the elements within their marketing mix that are generating ROI. When trying to determine campaign spend optimization through marketing mix models (MMM), marketers today have been taking a traditional approach. However, today鈥檚 marketing combines a variety of digital and traditional media鈥攁dding complexity that requires faster insights than MMM can provide.
In order to properly optimize future marketing spend while using media mix modeling, marketers need to understand the core strengths and weaknesses of MMM, and where it fits in today鈥檚 landscape. More specifically, they need to utilize their marketing mix modeling in combination with accurate, timely campaign insights that can provide a cohesive view into the where, when, why, and how, of their marketing ROI.
In this post, we鈥檒l explore the difficulties affecting marketers leveraging traditional use of MMM, and how it can be combined with other marketing performance measurements to accurately optimize campaign spending.
黑料社入口 mix modeling has been a tried and true method of providing marketers with a way to measure high-level impact for decades. The practice of MMM has typically been leveraged to guide marketers鈥� investments by highlighting the channels and strategies that provide overall results. This is done through long-term data collection, focusing on media spending across channels as well as additional factors that may influence campaign optimization like promotions, competitors, brand equity, or seasonal consumer trends.
This 鈥渢op down鈥� method of insight gathering focuses primarily on collecting aggregate data from a number of market-level insights. From there, marketers can leverage regression analysis to determine the relationship between marketing mixes and their overall impact on sales. When used effectively, marketing mix modeling can provide valuable insights into marketing effect on sales volume, broad media impact on sales, and overall ROI generated.
Prior to the emergence of digital marketing, traditional marketing mix modeling was effective because high-level insights were all that was needed. Marketers in the past didn鈥檛 need to incorporate messaging or audience targeting at the person-level into their efforts, which meant that they could use fewer insights in their campaign planning while still generating effective ROI.
Today however, the marketing landscape is vastly different than it was even ten years ago. In the past, marketing strategies consisted of getting as much information out to the public as possible. However, this approach is now obsolete. Now the focus is on getting specific information to the right person.
Results from traditional marketing mix models are often only available when the model is complete鈥攚hich can be weeks or months after campaign are live. Marketers simply can鈥檛 wait several months to understand how consumers are interacting with media. By the time a marketing mix model is prepared, the reported insights needed for effective campaign optimization have already shifted.
Furthermore, MMM focuses on recommending media mixes without taking into account messaging and audiences, critical components to media mix ROI.
Without updated approaches to match today鈥檚 marketing needs, they鈥檙e missing out on opportunities for their campaign spend optimization and media mix optimization.
MMM provides much needed top-down insights into the big picture of overall media mix performance. However, as the marketing landscape has shifted toward digital marketing, MMM falls short. Sure, it can bucket impressions into where in the digital spectrum opportunity lies, but it fails to offer insights into how those individual opportunities could be effectively optimized.
To cope, marketers are leveraging a variety of strategies that focus on gaining the necessary insights into campaign optimization that go beyond top-down, overall performance indicators. 黑料社入口 attribution models help marketers discover where in their digital mix has the most impact in driving conversions, while lift studies help showcase the impact of a single media channel along the marketing mix.
However, like marketing mix modeling, attribution modeling has flaws of its own that make it ineffective to solely rely on when measuring marketing mix effectiveness. There are several known attribution biases that can lead to misattributing a media channel鈥檚 importance along the marketing mix, including in-market/target bias, cheap inventory bias, and short-term signal bias. Additionally, attribution models still don鈥檛 distill information down to the person-level, missing the critical why of a media channel鈥檚 effectiveness.
With these challenges in mind, marketers should consider solutions that combine marketing analytics and modeling efforts into one cohesive and consolidated insight tool that can provide both the necessary top-down and bottom-up insights, but also the person-level insights that tell marketers not only where along the marketing mix their efforts are having the most impact, but also what messaging to send at what time along the right media channels. In short, marketers need a unified measurement approach.
Unified marketing measurement takes all of the disparate marketing analytics and big data found across a marketing team鈥檚 efforts and combines them into a single, holistic view of market effectiveness. Today, the most advanced tools for the job are those that can deliver the high-level insights of marketing mix modeling, the individual attribution insights into media effectiveness, and insights into what messaging works best for which media when. Combined, this information can give marketers the capability to deliver actionable spend optimization recommendations during the campaign, helping marketers increase ROI in the present without waiting for the next campaign.
When looking for a marketing analytics platform that can provide proper unified measurement, it鈥檚 important that marketers consider whether or not the solution has the capability to:
Leverage quality data:
Today鈥檚 marketing landscape is incredibly diverse, and as a result, the amount of data needed to properly guide decision-making is growing larger by the day. As a result, marketers have found themselves with a large amount of data that needs to be distilled into quality analytics before it can be used to impact campaign optimization. In short, marketers need a data-driven marketing solution that can distill the quality from the quantity of their big data quickly.
Regardless of industry, market performance changes at a rapid pace, meaning yesterday鈥檚 data may not provide the best insights for today. Marketers need analytics that can take into account the changing market and provide the data that accurately leverages these changes.
Today鈥檚 consumers are in the driver鈥檚 seat of their own media consumption. If marketing efforts aren鈥檛 tailored to consumers鈥� individual preferences, it鈥檚 likely that they won鈥檛 make an impact. Marketers need insights that show how consumers today are engaging with media, and what messages impact those media elements the most.
黑料社入口 mix modeling has been around for decades and continues to provide useful insights into campaign performance and spending. However, today鈥檚 marketing landscape is too fast paced to rely solely on MMM for campaign optimization insights. While newer methods like attribution modeling aim to address the gaps, marketers need more. By incorporating MMM and other marketing insights into a unified measurement solution, marketers can simplify their data analytics efforts and focus on the person-level insights that are critical for campaign spend optimization today.