Win Over Audiences with Effective Finance Content Marketing
Learn how to define, collect and use zero-party data, first-party data, second-party data, and third-party data in your marketing strategy.
Every brand wants to pre-optimize their campaigns for success right out of the gate, with parameters in place to quickly attribute success to specific channels, audiences, and creatives. However, as we look toward a future without the simple attribution offered by cookies, we need to get back to our statistical roots of traditional media strategies, those without the easy button involved. That’s why marketers should lean further into the value that’s offered with media mix modeling.
At a high level, media mix modeling is a way to define optimal budget allocation for media channels by looking at previous campaign performance. It requires analyzing sales-related data and media metrics (Coegi’s data and technology team recommends 2-3 years worth) to make predictions and strategy optimizations that will, in theory, improve future campaign performance.
In short, these models should be considered and analyzed alongside each other, as they both offer valuable insights from unique perspectives. Attribution can give you quick, real-time information about how specific media parameters are impacting your business goals, information that is useful when making mid-flight optimizations and short-term reporting. Media mix modeling, on the other hand, zooms out to give a bird’s-eye view of how all the pieces are working together to affect long-term strategy and performance. Each model informs the other, but tells different stories.
It is crucial to fully understand the data you’re analyzing, not just the standard media metrics from campaign reports. What are all of the factors that may have contributed to performance fluctuations? Creative? Messaging? Audience strategy? Seasonality? Knowing the context surrounding the numbers will give you a strong foundation from which you can build future strategies.
Using that context as the framework, determine what story the data points are telling you. The numbers don’t lie, but the numbers alone don’t always tell you the whole story. By asking the right questions, and maintaining a test and learn mentality, you will ensure strategic decisions are based on multiple factors rather than just one KPI.
A media mix model makes predictions, but it is not a crystal ball – just because Facebook performed well in campaigns historically does not mean that it will continue to do so into the future. That said, it is important to develop a continuous learning agenda to design your models. Test your assumptions based on historical performance. For example, what will happen if you increase the budget for programmatic channels? Do overall business results change? The only way to know for sure is to strategically make the budget adjustment and measure incremental results. From there, you can make more informed decisions about your channel strategy and budget allocation.