Spotlight On Econometric Modeling
How should we allocate and optimize our marketing spend across all products and channels? How can we forecast how a budget change will impact our sales in one year? Five years? Ten years? The data shows us that awareness behind our product is increasing – why is this not translating to sales?
As marketers, these are some of the questions we are faced with as we grapple to understand how to optimize marketing spend. While we often work against assumptions such as industry norms or brand comparisons within a category to determine how to allocate budget or build awareness, looking at historic and competitive data is not always enough.
Today, more is being demanded of marketers and with that comes the pressure to precisely quantify the impact of marketing decisions. Econometric modeling is key to ﬁnding the answers to these questions, particularly when brand-‐ speciﬁc, precise recommendations and results are required.
This Spotlight On goes behind the scenes to discover how econometric modeling works, how to choose the appropriate model based on common questions, and how to quickly startleveraging analytics for advantage.
What is Econometric Modeling?
‘Econometric modeling’ is a term that has become so prevalent, it’s beginning to sound like a buzzword, or industry jargon, used a to make learnings and results seem more scientiﬁc.
The fact is econometric modeling sounds scientiﬁc because it is. Econometric Modeling leverages the variation in granular data to statistically tease apart the impacts of marketing activities on the KPI of interest.
Today, various types of regression analysis are implemented in econometric modeling within the industry, including: multivariate, time series, vector auto regression and Bayesian methods.
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