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) = (0.60 × 0.5) / 0.533 ≈ 0.562
P(LinkedIn|Engaged) = (0.45 × 0.5) / 0.533 ≈ 0.422

Conclusion: Bayesian inference analysis reveals that email marketing achieves superior success rates. ADP can expand this method by applying Bayesian A/B tests alongside Markov Chain Monte Carlo simulations to perform multi-channel attribution modeling.

Power BI Visualization: This decision tree has branches representing posterior probabilities for each channel, along with a time-series chart that demonstrates how priors change throughout the campaign period.

Figure 3: Posterior Probability of Engagement by Channel

Through modeling data uncertainties, Bayesian inference enables marketing optimization by learning from information Kruschke, (2015).  Predictive personalization technology adapts to shifting consumer behavior according to Carpenter et al., (2017).

Bayesian inference supports continuous improvement in marketing by modeling uncertainties and learning from data Kruschke, (2015).  It enables predictive personalization that adapts to changing consumer behavior Carpenter et al., (2017).

References: 

Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1).

Kruschke, J. K. (2015). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2nd ed.). Academic Press.