Though many companies have some process in place to measure marketing ROI, few are doing it right. That's because today's consumers connect with brands through more channels and devices than ever before.

So what are some best-practices now? What do you need to know to get marketing ROI right?

Top-Down Approach: Marketing Mix Modeling

If you're more of a statistician, marketing mix modeling (MMM) may be at the top of your mind. It is often referred to as a top-down approach, and it's great for providing a holistic view of all marketing investment, including both digital and nondigital marketing activities and sales. Nonmedia factors such as promotions, competitor activities and economic conditions can also be included in the model.

However, in statistical modeling, it seems a lot of the models can get the job done; and, when you talk to different data scientists or vendors, their approaches can be quite different. Which one to choose? It takes more than a PhD student to do it right, because MMM is not a straightforward linear regression model. The model needs to take into account the uniqueness of marketing problems, such as integrated marketing synergy, adstock, lag effects, and diminishing returns in consumer response.

What are the pros and cons of MMM? Many marketers use MMM for high-level budget planning, and it is often done with little granularity and updated only once in a while, which means it is not enough for optimizing marketing execution.

However, MMM is extremely helpful when you don't have user-level data that is required for a bottom-up approach, such as when the majority of your marketing activities are offline (such as TV, print, outdoor billboard, events) and branding-based; in addition, you can create a what-if scenario simulator that allows marketing managers to better understand the impact of any changes they are planning to make.

Bottom-Up Approach: Attribution

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Daisy Chen has 13 years' experience at well-known brands, management consulting firms, and advertising agencies, and 5 years' experience heading analytics functions at various companies.

LinkedIn: Daisy Chen

Twitter: @DaisyChen