Company Ranking in Bayesian Perspective

Lukman Aji Samudra
Jul 31 · 5 min read

Background

Recently I came across one article: Revou Happiness Index. The report assesses 38 technology companies in Indonesia and ranks them using company reviews in Jobstreet and Glassdoor. Below is a cropped image of their result. Do you notice that there are companies with a lower number of reviews but have higher rank while having a pretty small difference in their average review? Example: Payfazz (23 reviews, avg 4.7) and Happyfresh (62 reviews, avg 4.65) with 0.05 difference in average review.

Problem Statement

Data

We could get reviews for 38 companies from JobStreet using this script. This analysis uses JobStreet data per 20 July 2021. Below is the top 10 companies based on the mean review. Notice that the fewer the number of reviews, the higher the standard deviation. It represents our uncertainty about the data we have. Less data, less confidence. Later, we will incorporate the uncertainty for ranking.

Alternative Methods

  1. Rank by Weighted Average Review
  2. Rank by Statistics from Posterior distribution (Bayesian Analysis)
weighted rank (WR) = (v ÷ (v+m)) × R + (m ÷ (v+m)) × C where:
R = average review for the company = (mean_review)
v = number of review for the company = (sum_review)
m = minimum num of review required to be listed in the analysis
C = the average review across the whole report
note for this analysis
m = 5
C = 4.318

Conclusion

We now see that’s there are alternative methods for ranking companies using the Bayesian perspective. These methods suit you if you wanted to incorporate “uncertainty” in your analysis. With that, you won’t blindly trust the data you have. Rather, you will update your prior belief with the data accordingly.

Reference

  1. Probabilistic Programming & Bayesian Methods for Hacker, Cameron Davidson-Pilon.
  2. Google Answer, Alternative to IMDb formula.
  3. Revou
  4. JobStreet

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