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Marketers and brands today need solutions to help them quantify the impact of all of their marketing activities and optimize marketing spends.
Marketing Mix Modelling (MMM) is a statistical technique which can help address this need. It is also privacy-friendly and resilient to the changes occurring in the digital advertising ecosystem. However, a limitation of traditional MMM was the complete dependence of brands on external specialists such as large consulting firms or analytics providers. This was time consuming and expensive, resulting in organizations running it less
frequently and only for their largest brands and markets. Scaling MMM across the organization or updating models regularly was not financially feasible and resource intensive.
Today, with many brands having their own data science teams along with significant investments in cloud computing and data analytics, new MMM SaaS solutions allow brands to run models in-house and ondemand. It also allows them to scale MMM with less effort, cover more of their overall marketing budget by keeping costs down, and analyze their own data while keeping it in-house.
Marketers and brands today need solutions to help them quantify the impact of all of their marketing activities and optimize marketing spends.
Marketing Mix Modelling (MMM) is a statistical technique which can help address this need. It is also privacy-friendly and resilient to the changes occurring in the digital advertising ecosystem. However, a limitation of traditional MMM was the complete dependence of brands on external specialists such as large consulting firms or analytics providers. This was time consuming and expensive, resulting in organizations running it less frequently and only for their largest brands and markets. Scaling MMM across the organization or updating models regularly was not financially feasible and resource intensive.
Today, with many brands having their own data science teams along with significant investments in cloud computing and data analytics, new MMM SaaS solutions allow brands to run models in-house and ondemand. It also allows them to scale MMM with less effort, cover more of their overall marketing budget by keeping costs down, and analyze their own data while keeping it in-house.
Traditional MMM has been used by consumer brands for decades, but there are challenges which limit its viability for brands looking for speed, scale and cost-effectiveness in their marketing measurement and planning solution. Marketing Mix Models can be very time and resource intensive making it difficult and expensive to scale across the organization. Due to long lead times, the results and insights delivered are not ‘always-on’. Complex econometric modelling used in the process can sometimes make it look like a black box. And being a complex statistical technique based on data sets, MMM was typically limited to analysts and data scientists with programming skills.
Traditional MMM has been used by consumer brands for decades, but there are challenges which limit its viability for brands looking for speed, scale and cost-effectiveness in their marketing measurement and planning solution.
Facebook’s MMM Software as a Service (SaaS) program, in partnership with Analytic Edge, now addresses most of the challenges associated with traditional MMM. It helps advertisers adopt an incrementality based planning process through the integration of self-serve MMM platforms or tools. The tools are designed for automatic integration into the advertiser and publisher data ecosystem and enable faster turnaround of MMM processes.
In addition, user interfacebased platforms make modelling much more inclusive, allowing a wider set of teams such as marketers with analytical skills to participate in modelling, improving the process as well as the results.
Unilever is a British multinational consumer goods company with more than 400 brands across the categories of Foods & Refreshments, Home Care, and Beauty & Personal Care. The company is among the largest advertisers in the world. Unilever Poland wanted to explore how the sales of various brands could be increased by leveraging MMM to optimize advertising spends. Facebook and Unilever collaborated on the MMM SaaS program using Analytic Edge’s Marketing Mix Modelling SaaS platform. Using the Rexona women’s deodorant brand as a test case, Analytic Edge conducted training for the Rexona brand team, covering all platform modules and the MMM process. Analytic Edge also conducted hands-on marketing mix modelling training to give the team experience in developing an MMM model using in-house brand data.
Unilever Poland were able run the MMM in-house and estimate the influence of different marketing activations on Rexona brand sales. Media optimization simulations run in the platform resulted in media budget reallocation and increased sales. The work enabled decisive marketing investment decisions to be made for future advertising investment.
Facebook’s MMM SaaS program enables advertisers to run MMM models in-house and on-demand to address the challenges faced by traditional MMM. Further innovations are underway that will make MMM on SaaS platforms simpler, automated and Al-driven. This will enable widespread adoption of MMM for both large and small companies, who couldn’t access MMM before or couldn’t scale MMM across their whole business.
Analytic Edge is a global analytics company that leverages technology and advanced analytics to help companies make data-based marketing decisions. The company’s flagship platform Analytic Edge Qube offers a suite of marketing analytics solutions with a Software as a Service (SaaS) model. The solutions include DemandDriversTM for always-on Marketing Mix Modeling (MMM), SynTestTM for AI powered Test and Learn, PriceSenseTM for pricing and promotion analytics, and PowerViewTM for analytics visualization. Analytic Edge works with clients across industry verticals such as e-commerce, mobile apps, gaming, consumer packaged goods, retail, automotive and many others. The company has offices in Singapore, India, US, Mexico, Brazil, UK, China, Japan, South Korea, UAE and Australia.
