Marketing Mix Modeling (MMM) is a marketing analytics solution that considers both controllable and uncontrollable variables to determine the best distribution of your marketing dollars to maximize sales.
As the evolution of marketing continues, it is vital for organizations to understand the impact and effectiveness of their efforts. Take for example the recent global pandemic. Marketing tactics that may have once been successful could not even be viable as consumers changed their spending habits.
Marketing mix modeling utilizes data as a tool to estimate the impact of various marketing tactics while also predicting future outcomes on sales. With many differing factors, the tool can have a greater impact on future marketing goals formerly unseen. Utilizing MMM is more common than you may think! For instance, many Fortune 500 companies such as P&G, AT&T, Kraft, Coca-Cola, and Pepsi have made MMM an integral part of their marketing planning.
Artificial Intelligence and Machine Learning
As marketing efforts have become more sophisticated, so have data analytics tools. The increasing adoption of Artificial Intelligence (AI) and Machine Learning (ML) have allowed organizations the ability to use predictive analytics in their marketing campaigns.
AI/ML are both integral parts of marketing mix modeling, as manual data input is tedious and time-consuming. Additionally, companies may have thousands of variables to account for in their marketing mix modeling. With AI/ML algorithms, marketing teams have much more freedom to actually make strategic decisions. For more optimal MMM, applying artificial intelligence and machine learning can be beneficial in analytics for organizations.
Navigating Marketing Pain Points
There’s no doubt that marketers face challenges when it comes to trying to balance budget spending and sales. Marketing mix models can be used to address common pain points, like:
- How is each activity contributing to our goal?
- How do these activities work together?
- Are we delivering the most optimal campaign?
- Are we optimizing the allocation of marketing spend?
As businesses become more customer-focused in their marketing and sales offerings, solutions such as marketing mix modeling have become more common. Navigating pain points can become messy and complex. In the end, it’s the job of marketers to overcome these challenges. That’s where MMM comes in, in order to give a complete view of the internal and external variables that can affect consumer behavior.
Marketing Mix Modeling Variables
There are many different variables that marketers need to consider when implementing campaigns. Variables such as industry trends, seasonality, and the impact of advertising are all important factors to consider when launching marketing campaigns.
With a marketing mix model, these variables are accounted for all at once. Former marketing efforts used traditional marketing mix models, taking aggregate data over a long period of time to showcase which marketing mix channels were most effective. If a television advertising campaign converted to more sales, the marketing team may try to run similar campaigns.
In today’s marketplace, an omnichannel approach is necessary due to the fact that to truly understand marketing campaign return on investment (ROI), there is a need to track consumer interactions with their brand across all media mix channels.
Marketing Mix Model and Strategy
A marketing strategy that utilizes MMM in tandem with AI/ML predictive analytics provides three distinct advantages.
First, marketing mix modeling provides an evaluation of current marketing efforts. This means that organizations are able to identify key volume drivers, campaign effectiveness, and ROI. Data-backed decisions can have a huge impact on future campaigns and budgets.
Next, a marketing mix model offers a prediction of the future. Essentially, organizations can simulate an optimum plan over time. That way, spending can be reallocated, offering insight into higher and lower spending.
Last but not least, an MMM showcases ongoing evaluation of campaigns. Marketers can use this to identify volume drivers and adjust as necessary, as tracking is in real-time. In the end, marketing mixed modeling can be utilized for iterative scenario planning. Which can be useful for variables such as seasonality. Or, insight into the state of the economy amid a global pandemic!
Transform Your Marketing Reporting into Actionable Insights
As artificial intelligence and machine learning become more of the norm in data analytics, organizations are utilizing their power to transform their marketing.
Coupled with other cloud-based solutions, a marketing mix model has the potential to provide valuable insights. This, in turn, opens the door for more optimal channel engagement, and an approach to effective spending management.
Pandera, a Google Cloud Premier Partner, can assist organizations to transform their marketing reporting into actionable insights!
Through our experience in delivering countless analytical solutions, we are able to accelerate this process by providing organizations with a scalable analytical architecture and a Smart Analytics Playbook equipped with proven analytics blueprints for various use cases.
For more information on marketing mix modeling and how it can be implemented in your digital strategy, please contact us today!