MarTech Brief:
Marketing Mix Modeling

Setting the New Standard for Marketing Mix Modeling with Pecan 

Pecan’s marketing mix modeling (MMM), also known as media mix modeling, integrates state-of-the-art machine learning and automation into the classical statistical analysis solution for marketing budget allocation. Without specialized data science resources, marketers can now add MMM to their planning and measurement strategies, gaining invaluable insights into channel performance and simulating budget allocation to maximize return on marketing spend (ROAS). 

MMM has traditionally been expensive, inflexible, and infrequently updated. Pecan’s innovative approach makes it easy to connect to your data, iterate on your models, and refresh your data analysis. Even messy data is handled easily with Pecan’s automated techniques. 

Here are three great reasons marketers should use MMM:

  1. It allows marketers to understand the relative effectiveness of different marketing efforts, such as advertising, promotions, and pricing, and make data-driven decisions about allocating resources.
  2. MMM can help identify which elements of the marketing mix are having an impact on sales and which are not, allowing marketers to refine their marketing strategy, select marketing tactics, optimize their spending, and improve their return on investment.
  3. Marketers can also use MMM to project the potential impact of future marketing campaigns and strategies, which can help inform budgeting and planning decisions. 

Overall, MMM is a powerful tool that can help marketers make more informed and effective decisions about their marketing channels and strategies.

Read this helpful marketing tech brief to learn: 

  • Top three highlights of Pecan’s MMM solution
  • A Pecan customer testimonial  
  • How to get started with Pecan AI

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