The advertising industry has long sought to answer the Wanamaker question: which half of their advertising works. Media mix modeling (MMM) was once the preferred method to solve this, although it was slow to produce results. In the aftermath of Covid-19 and amidst the decline of third-party cookies, marketing mix modeling (MMM) has evolved, becoming more accessible to mid-sized and smaller businesses.
Advances in technology, particularly cloud computing and machine learning, have democratized the use of MMM, providing marketers with greater transparency and the ability to measure the effectiveness of their media planning more efficiently.
One example of modern MMM is FutureSight, developed by Mediastruction, which leverages cloud computing and machine learning to offer a software-as-a-service (SaaS) model for mid-market clients. Marilois Snowman, founder and CEO of Mediastruction, emphasizes that FutureSight is transparent, likening it to a “glass box” rather than a “black box.”
This transparency allows for customizable models tailored to specific brands, ensuring more accurate attributions of media effectiveness. The model’s adaptability, such as using different algorithms for various industries, enhances its relevance and accuracy.
Modern Marketing Mix Modeling Evolves with Cloud and Machine Learning for Better Ad Insights
Jen Marino, a fractional CMO, has utilized FutureSight for clients including Rockland Trust Bank, achieving significant savings and efficiency.
Marino highlights that FutureSight not only reports past performance but also forecasts future outcomes with high accuracy. This tool enabled Marino to optimize marketing spend and improve channel effectiveness, leading to more efficient budget allocation and increased spending due to demonstrated effectiveness.
Media Matters Worldwide is another agency advancing MMM through its agile mix modeling (AMM), which integrates machine learning and AI. AMM provides clients with weekly campaign ROI updates, adapting to the decline of traditional attribution methods.
The automated data collection of AMM is more frequent and accurate compared to traditional manual methods, reducing processing time and errors. This approach has benefitted clients like Sierra Nevada Brewing Company by offering comprehensive metrics and optimizing their media mix for higher return on ad spend (ROAS).
Lastly, Keen Decision Systems offers a SaaS marketing mix modeling tool with a free trial to attract smaller marketers. This tool aims to provide valuable insights even for brands with modest marketing budgets, helping them forecast outcomes, measure agency performance, and prove the value of their investments more rapidly.
Brad Keefer, the firm’s chief revenue officer, notes that clients like Athletic Brewing and Poppi benefit from these capabilities, showcasing the broadening accessibility and utility of modern marketing mix modeling tools.
