In recent years, few business disciplines have evolved as rapidly as digital marketing. This fast evolution introduces considerable complexity. On one hand, there is an overwhelming flood of marketing data; on the other, increasing privacy constraints. The critical question facing marketers today is how to effectively see through this massive amount of information. Which marketing techniques yield the highest return on investment? Where is my budget best used? This is where Marketing Mix Modeling (MMM) comes into play: decoding your marketing data to understand your consumer preferences, behaviors, and purchase patterns. Ultimately, Marketing Mix Modeling turns data overload and complex attribution into strategic insights to help you make the best marketing decisions.
Imagine that you are managing a store, and you suddenly see a high number of customers. How did those people come here? Did they see your ad in the street? A social media post on their phone? Did they come because they received a coupon in their mailbox? Or everything at once…? If you are selling services or if you have an e-commerce site, the questions are very similar: What works best in your marketing mix? Where should you invest, or disinvest?
These questions are even more complex since the introduction of privacy regulations such as GDPR (General Data Protection Regulation). When consumers do not give their consent to third-party cookies, or reject tracking from their apps, understanding their behavior becomes even more difficult. In short, marketers today face increasing challenges to attribute consumer actions to specific campaigns or touchpoints.
The good news is that the advent of Big Data Analytics (BDA) and machine learning has revolutionized our ability to analyze and interpret vast data sets, offering much more visibility in this marketing maze. This is what the Marketing Mix Modeling offers.
Marketing Mix Modeling is a statistical approach, analyzing all your marketing variables (e.g. your media tactics, but it can also include your promotions, or external factors like the weather) and determining their respective contribution to your sales.
Marketing Mix Modeling is not a newcomer in the marketing arena. Traditionally, it was a resource-intensive process, accessible only to a few, and most of the time not including all of the newcoming digital channels. But with new technologies, times have changed. MMM now reconciles both offline and online data and can be configured to deliver easily actionable recommendations for marketers.
Marketing Mix Modeling allows businesses to interpret vast amounts of data. But it can also offer:
In this way, Marketing Mix Modeling is not just a tool. It is a strategic compass guiding marketers through the ever-changing landscape of digital marketing. It ensures that every decision is backed by robust, actionable data. This also helps marketers justifying their budget needs internally and setting clear KPIs.
To help marketers implement and develop their own MMM, we are happy to announce Universem’s partnership with Valkuren. We leverage our in-depth knowledge of digital marketing data and its collection, whilst relying on Valkuren’s expertise in big data engineering. The result? The development of our client’s Marketing Digital Twin: a virtual model that replicates the client’s marketing mix and its customers’ behavior.
Here is how the process works to develop your Digital Twin:
This model allows our clients to analyze and predict the impact of their marketing strategies.
In summary, Marketing Mix Modeling provides a strategic edge for marketers. If you want to be able to make data-driven decisions, optimize your marketing expenses, and justify your spending internally, Marketing Mix Modeling is right for you!
If you want to understand what MMM can bring to your business, contact us for an introductory discussion!
