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In today’s fiercely competitive market, measuring marketing effectiveness has become a critical need for marketers and brands. With numerous channels, strategies, and campaigns running simultaneously, understanding which efforts are driving results is essential. It not only helps in quantifying the impact of marketing activities but also in optimizing marketing spends to ensure maximum return on investment (ROI). In this landscape, Marketing Mix Modelling (MMM) emerges as a powerful tool, particularly in an era where traditional attribution methods face significant challenges.
With the rapid rise of digital marketing in recent years, attribution quickly became the go-to technique for many companies looking to measure media effectiveness. Attribution models provided a granular, user-level view of which online marketing
channels delivered the most conversions. This precision made it particularly appealing for digital-first brands that operated primarily online. However, attribution was an online-only tool, making it unsuitable for brands whose marketing campaigns span both offline and online channels. For companies that invest in television, print, radio, or out-of-home (OOH) advertising alongside their digital efforts, attribution couldn’t capture the full picture of their marketing effectiveness.
A more significant limitation emerged with the increasing focus on user privacy. Regulations like the General Data Protection Regulation (GDPR), the deprecation of third-party cookies, and privacy-tightening changes to iOS have all contributed to a substantial loss of signal in digital attribution models. These changes make it difficult for marketers to accurately track user behavior across the digital ecosystem, diminishing the effectiveness of attribution as a reliable measurement tool.
Marketing Mix Modelling (MMM) offers a robust alternative. MMM is a statistical technique that helps companies measure the impact of both marketing and non-marketing drivers on business outcomes, such as sales and revenue. Unlike attribution, MMM is privacy-friendly and resilient to the changes in the digital advertising ecosystem. It doesn’t rely on user-level data, making it immune to privacy regulations and data restrictions. Traditional MMM has been used by consumer brands for decades, offering valuable insights into how various media and non-media factors – from marketing spend to competitor activity to seasonality and economic conditions – impact sales.
However, traditional MMM also comes with its challenges. It has historically been time-consuming and resource-intensive, making it difficult and expensive to scale across an organization. The long lead times required for building and analyzing MMM models meant that insights were not always actionable for guiding forward-looking marketing strategies. These limitations have prevented many brands, particularly smaller ones, from fully leveraging the power of MMM.
Analytic Edge’s SaaS MMM solution, Demand Drivers, addresses these challenges head-on. By leveraging automation and AI, Demand Drivers transforms MMM into a fast, scalable, and costeffective solution that can be used by companies of all sizes.
The Demand Drivers SaaS platform allows companies to bring MMM in-house. With an in-house, always-accessible platform, marketing teams can build models quickly, refresh them as frequently as every week, and generate insights in a matter of days rather than months. This speed and flexibility make the solution ideal for companies looking for an ‘always-on’ approach to marketing measurement and planning.
Importantly, Demand Drivers is a no-code platform, making
MMM accessible even to companies without in-house data science and analytics expertise. The platform’s user-friendly interface, combined with automation, allows marketers to start using MMM immediately, without the need for extensive training or technical knowledge. Analytic Edge offers affordable pay-asyou-go models, further lowering the barrier to entry. This makes it possible even for small and mid-sized companies to benefit from advanced marketing analytics.
For companies that prefer additional support, Analytic Edge offers expertise to help build and refresh models, ensuring that every company, regardless of its resources, can fully leverage the power of MMM.
The Demand Drivers SaaS platform makes it easy for companies to scale MMM across more brands and markets. This ensures that more of a company’s marketing budget benefits from measurement and ROI optimization. By applying MMM across the organization, companies can make more informed decisions, allocate marketing spend more effectively, and ultimately drive better business outcomes.
SaaS MMM solutions like Analytic Edge Demand Drivers are revolutionizing how companies measure marketing effectiveness and optimize their marketing spends. By offering a fast, scalable, and cost-effective alternative to traditional MMM, Demand Drivers empowers companies to make data-driven decisions that maximize ROI. As marketing continues to evolve, tools like SaaS MMM will be critical in helping companies navigate the complexities of the modern marketing landscape and achieve their business goals.
