ADP Bayesian Analysis - Blog Post 5
Blog Post 5 will show the Bayesian Analysis technology by presenting interesting Bayesian paradigms. Every post demonstrates how ADP uses Bayesian inference to address marketing challenges in business settings. Every computation presented here is fictional but demonstrates practical applications of probability theory and data analysis techniques. Tech Blog Post 5: Bayesian Analysis – Driving Smarter Marketing Decisions Overview: ADP plans to integrate Bayesian paradigms within its marketing intelligence framework. The objective is to move beyond fixed KPIs through the integration of real-time behavioral data into predictive models. Scenario: ADP launched two ad campaigns: Email: 500 sent, 300 opened → P(Engaged|Email) = 0.60 LinkedIn: 400 sent, 180 opened → P(Engaged|LinkedIn) = 0.45 Using Bayes’ Theorem: Let A = customer engagement, B = ad channel Total Engagement: 300 + 180 = 480 Total Sent: 900 → P(Engaged) = 480 / 900 = 0.533 P(Email|Engaged) = ...