ADP Bayesian Analysis - Blog Post 4

Now, to review Blog Post 4, which shows the Lottery Analysis of the technology blog posts utilizing Bayesian analysis. 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 4: Lottery Analysis – Ensuring Fairness in Reward Distribution

Overview: ADP will conduct a random drawing for a Hawaii trip to commemorate their campaign achievement. What methods can be implemented to maintain equitable treatment of all customer segments?

Probability Calculation per Category: Using the 333 total participants:

• HR Managers: 105/333 = 0.315
• Technical Staff: 92/333 = 0.276
• Executives: 50/333 = 0.150
• Recruiters: 86/333 = 0.258

Addressing Perception of Bias: Transparency is key. Building trust requires both third-party audited random number generation and transparent documentation of the random draw procedure.

Beyond Random Selection: The random draw produces fair statistical results but allows bonus selection to incorporate engagement metrics such as email click-through rates and demo attendance as weighted factors.

 

Bayesian Fairness Modeling: By starting with an equal distribution assumption and modifying for engagement behavior we can perform simulations with weighted probabilities which result in more merit-based outcomes.

Power BI Visualization:

Figure 2: Lottery Probability Pie Chart

Probability models maintain nondiscrimination objectives during the validation process of decisions based on randomness, according to DeGroot & Schervish (2012). According to Kotler & Keller (2016), transparent promotional methods build trust and strengthen brand equity.

References: 

DeGroot, M. H., & Schervish, M. J. (2012). Probability and Statistics (4th ed.). Pearson.

Kotler, P., & Keller, K. L. (2016). Marketing Management (15th ed.). Pearson.