Product reviews and ratings play an important role in consumer decision making. Online shoppers look for products with the highest ratings. They often read reviews that give details behind the ratings. In the search and discovery context, businesses consider product reviews to be as relevant as product descriptions. Both are relevant for matching users’ queries.

What is TrustFinance TrustScore?

The TrustFinance TrustScore is a numerical rating that reflects the trustworthiness and credibility of financial service providers based on user reviews. This score is crucial for consumers looking to make informed decisions about which financial services to use. The TrustScore is calculated using a statistical method known as the Bayesian average, which provides a more accurate representation of a company's reliability by incorporating the principle of prior probability. This method helps mitigate the impact of having too few reviews, which can skew traditional average ratings.

Importance of TrustFinance TrustScore for Users and Financial Service Providers

For Users: The TrustFinance TrustScore is an essential tool for users as it provides an immediate sense of a company's reputation and reliability. With this score, users can quickly compare different financial service providers and make choices that best fit their needs, based on the experiences of other customers. It offers a layer of protection and assurance, helping users avoid businesses that might not meet their standards for trust and service quality.

For Financial Service Providers: For companies in the financial sector, a high TrustScore can be a significant competitive advantage. It enhances their credibility and attracts new customers who prefer transacting with trustworthy firms. It also serves as feedback, encouraging companies to continuously improve their customer service and operational standards to maintain or improve their scores. Responding to reviews and improving services based on customer feedback can lead to better customer satisfaction and higher scores, creating a positive feedback loop that benefits both the company and its clients.

How TrustFinance TrustScore is Calculated

The TrustScore on TrustFinance is now calculated exclusively based on user reviews using the Bayesian average method. This method considers the likelihood (or prior probability) of receiving a review score based on all reviews across the platform, which helps to normalize scores and reduce bias. This statistical approach adjusts the mean of the reviews based on the number and consistency of the reviews, providing a more accurate and fair representation of a company's performance. The use of the Bayesian average ensures that the TrustScore reflects both the quantity and the quality of the reviews, making it a reliable indicator of a company's reputation.
 

Bayesian Average

The Bayesian Average is a statistical technique that adjusts the average rating based on the number of reviews and their distribution. It helps mitigate the impact of extreme ratings, whether overly positive or negative, that could skew the overall score.
 

Why Use the Bayesian Average?

Traditional averages can be misleading, especially for businesses with few reviews. A single exceptionally high or low rating can significantly alter the average, making it less representative of the general user sentiment. The Bayesian Average addresses this by incorporating a more balanced approach.
 

Comparing different ratings rankings

Consider two ways to rank star ratings:

• Use an arithmetic average that adds together all ratings and divides by the total quantity of ratings. If there are 100 1-star ratings and 10 5-star ratings, the calculation is ((100×1) + (10×5))/ (100+10) = 1.36.
• Use a Bayesian average that adjusts a company’s average rating by how much it varies from the category average. This favors company’s with a higher quantity of ratings

As already suggested, ignoring the quantity of ratings doesn’t help distinguish between companies with 10 ratings and 1000 ratings. You need to at least calculate an average that includes the quantity of ratings.

The following image shows three items ranked by different averages. The left side uses the arithmetic average for ranking. The right side uses the Bayesian average.

Both sides display the arithmetic average in parenthesis just right of the stars. They also display the average used for ranking as avg_star_rating and bayes_avg respectively, under each item.

By putting Item A at the top, the left side’s ranking is both misleading and unsatisfying. The ranking on the right, based on the Bayesian average, reflects a better balance of rating and quantity of ratings. This example shows how the Bayesian average lowered item A’s average to 4.3 because it measured A’s 10 ratings against B and C’s much larger numbers of ratings. As described later, the Bayesian average left Items B and C unchanged because the Bayesian average affects items with low rating counts much more than those that have more ratings.

In sum, by relativizing ratings in this way, the Bayesian average creates a more reliable comparison between companies. It ensures that companies with lower numbers of ratings have less weight in the ranking. 

Understanding the Bayesian average

The Bayesian average adjusts the average rating of companies whose rating counts fall below a threshold. Suppose the threshold amount is calculated to be 100. That means average ratings with less than 100 ratings get adjusted, while average ratings with more than 100 ratings change only very slightly. This threshold amount of 100 is called a confidence number, because it gives you confidence that averages with 100 or more ratings are more reliable than averages with less than 100 ratings.

This confidence number derives from the catalog’s distribution of rating counts and the average rating of all companies. By factoring in ratings counts and averages from the whole catalog, the Bayesian average has the following effect on a company’s individual average rating:

• For a company with a fewer than average quantity of ratings, the Bayesian average lowers its artificially high rating by weighing it down (slightly) to the lower catalog average.
• For a company with a lot of ratings (that is, more than the threshold), the Bayesian average doesn’t change its rating average by a significant amount.

How to calculate the Bayesian average

The Bayesian average uses two constants to offset the arithmetic average of an individual companies:

• the arithmetic average rating of all companies (m)
• a confidence number (C).

The calculation for m is a straightforward arithmetic average for all companies: the sum of all ratings divided by the count of the quantity of ratings.

Calculating C requires a bit more math. This tutorial calculates C based on the distribution of the rating counts for each company, where C is equal to the 25% percentile (= the lower quartile). 

For example, suppose there are 100 companies. To compute C, you take all the companies and sort them by the quantity of ratings each has. Some have 10 ratings and others have 100 or 1000 ratings. Once sorted, you find the company at the 25% position on the sorted list and look at how many ratings it has. This is the lower quartile for C. For simplicity, this guide sets C = 100.

Thus, if you calculate the overall average rating (m) of TrustFinance to be 3.6, the Bayesian average uses both of these values ( m = 3.6 and C = 100) to adjust the arithmetic average. It does this using the following formula:

Here’s the same formula with the example numbers plugged in:

bayesAvg = companyRatingsAvg companyRatingsCount + 100 3.6 companyRatingsCount + 100

Conclusion

TrustFinance’s TrustScore is an invaluable tool that leverages user reviews to offer a transparent and fair assessment of financial institutions. By employing the Bayesian average, TrustFinance ensures that its TrustScore is not disproportionately affected by either a small number of extremely positive or negative reviews but reflects a balanced view of user experiences and sentiments. This calculation method makes TrustScore a robust tool for assessing the reliability and trustworthiness of financial service providers.