Long Term Effects of Marketing Using Bayesian Networks

Long-Term Effects of Marketing Using Bayesian Networks

Presented at the 9th Annual BayesiaLab Conference on October 11, 2021.


Long-term brand marketing is important to achieve sustainable growth. There are many important areas that companies can invest in paid ads, performance marketing, and affiliate marketing. They will all help with growth, but eventually, you will hit that glass ceiling. Plus, with nearly every market oversaturated at this point, we will need to spend a whole lot of money to have any chance of standing out.

In my talk, I demonstrate how Course5 is disrupting the space of Digital Media Optimization with its new solution offering and why it is important for the industry to consider and adopt such solutions in the challenging times we are in.

Presentation Video

Presentation Slides

About the Presenters

Anand Wilson, Lead Data Science Consultant, Advanced Analytics Course5 Intelligence

Anand comes with 9+ years of experience in applied artificial intelligence and data sciences. He has worked for marque clients such as Lenovo, Intel, Microsoft, Novartis, Novo Nordisk, GE, Mars Wrigley, PepsiCo, etc., enabling digital transformation using A.I.

In his current role, Anand focuses on developing and market solutions based on the Bayesian Network model theory, which enables us to quantify the causality in an observational study. A major area of work/research includes Knowledge Modelling, Machine Learning with BayesiaLab, and Inference.

Anand comes from applied statistics background. He has a master's degree in statistics. Anand carries an acute interest in machine reasoning, causal inference, and experimental designs, along with machine learning and data science.

Buvana Iyer, Principal Solution Architect, Discovery Solution Course5 Intelligence

Buvana does consult for C-level clients and has led projects achieving org-level implementation of enterprise analytics, including Software Development, BI & Analytics, and ML & AI solutions (at scale), adopting DevOps philosophy with agile delivery. Expertise in leveraging both traditional statistics and machine learning techniques to create solutions and deliver business value.

Buvana comes from applied Mathematics background. She has a master's in Mathematics. Buvana carries an acute interest in Predictive Analytics, Statistical modeling, machine reasoning, and experimental designs, along with machine learning and data science.

Previous Presentations by Course5 Intelligence

  • Customer Preference Sequencing for Better Customer Engagement (Laval Virtual World, 2020)
  • Modern Approaches to Causal Modelling in Customer Experience Measurement (Durham, 2019)

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