# Understanding Google My Business Reviews: How They Are Calculated and Scored (With Math Examples)

Google My Business (GMB) reviews play a crucial role in local SEO and consumer decision-making. Businesses rely on these reviews to establish trust with potential customers, and Google uses them as a key ranking factor in local search results. In this article, we’ll delve into how GMB reviews are calculated and scored, complete with examples to demonstrate how the math behind review scores works.

#### What Are Google My Business Reviews?

Google My Business reviews are user-generated reviews attached to a business’s GMB listing. Customers can leave a rating (out of 5 stars) and provide feedback. These reviews appear in Google Search and Google Maps, offering insights into the quality and reputation of the business.

#### Why Are GMB Reviews Important?

**Local SEO Ranking Factor**: Businesses with higher reviews and better overall ratings tend to rank higher in local search results. Google looks at the quantity and quality of reviews when determining search rankings.**Consumer Trust**: Positive reviews serve as social proof, making customers more likely to choose a business.**Conversion Rates**: High ratings increase the likelihood of users clicking on a business’s listing and converting into customers.

#### How Are Google My Business Reviews Calculated?

Google My Business reviews are calculated as an **average rating** based on the total number of reviews received by a business. This average is displayed in the form of a star rating on the business listing.

##### Formula for Calculating the Average Review Rating:

The formula for calculating a GMB average review rating is:

[

text{Average Rating} = frac{sum{text{Individual Review Ratings}}}{text{Total Number of Reviews}}

]

Where:

- ( sum text{Individual Review Ratings} ) is the sum of all the ratings given by reviewers (each rating is between 1 and 5 stars).
- ( text{Total Number of Reviews} ) is the total number of reviews the business has received.

#### Example 1: Calculating the Average Rating

Let’s say a local restaurant has received the following 5 reviews:

- 5 stars
- 4 stars
- 3 stars
- 5 stars
- 4 stars

To calculate the average rating:

- First, sum up all the individual review ratings:

[

5 + 4 + 3 + 5 + 4 = 21

] - Divide by the total number of reviews (5):

[

text{Average Rating} = frac{21}{5} = 4.2

]

Thus, the average rating for the restaurant is 4.2 stars.

#### Example 2: Adding New Reviews

Now, let’s assume that the restaurant receives two more reviews: 2 stars and 5 stars. Here’s how the updated average rating is calculated:

- Sum up all the review ratings:

[

5 + 4 + 3 + 5 + 4 + 2 + 5 = 28

] - Divide by the new total number of reviews (7):

[

text{Average Rating} = frac{28}{7} = 4.0

]

So, after receiving these two new reviews, the restaurant’s average rating drops slightly from 4.2 to 4.0 stars.

#### The Weight of Recent Reviews

While Google doesn’t explicitly disclose its full algorithm for ranking businesses based on reviews, it’s widely understood that **recency** plays a role. More recent reviews are typically given more weight than older ones, especially when there’s a noticeable trend in reviews (e.g., consistently improving or declining ratings).

Although the exact weighting formula isn’t public, an example to illustrate how it might work involves placing more emphasis on recent reviews by assigning them a higher multiplier than older ones. For example:

- Reviews older than 12 months may have a weight of 1.0.
- Reviews from the past 6 to 12 months may have a weight of 1.5.
- Reviews from the past 6 months may have a weight of 2.0.

#### Example 3: Weighted Reviews

Let’s calculate the weighted average for the following reviews, where newer reviews are given more importance:

- 12 months ago: 5 stars (weight = 1.0)
- 9 months ago: 4 stars (weight = 1.5)
- 3 months ago: 3 stars (weight = 2.0)
- 1 month ago: 5 stars (weight = 2.0)

To calculate the weighted average:

- Multiply each review by its weight:
- 5 stars ( times ) 1.0 = 5.0
- 4 stars ( times ) 1.5 = 6.0
- 3 stars ( times ) 2.0 = 6.0
- 5 stars ( times ) 2.0 = 10.0

- Add up all the weighted ratings:

[

5.0 + 6.0 + 6.0 + 10.0 = 27.0

] - Add up the total weights:

[

1.0 + 1.5 + 2.0 + 2.0 = 6.5

] - Divide the weighted ratings by the total weight:

[

text{Weighted Average} = frac{27.0}{6.5} = 4.15

]

In this case, the weighted average rating is 4.15 stars, which slightly adjusts the score to reflect the higher importance of recent reviews.

#### The Impact of Review Volume

The number of reviews also plays a role in the overall score’s reliability. A business with 100 reviews and an average score of 4.0 is perceived as more reliable than one with only 5 reviews and the same average score.

##### Formula for Review Volume Weighting:

A simplified version of the **Wilson score confidence interval** can be used to factor in review volume. This formula helps determine the lower bound of the confidence level for a business’s rating:

[

text{Wilson Score} = frac{p + frac{z^2}{2n} – z sqrt{frac{p(1-p)}{n} + frac{z^2}{4n^2}}}{1 + frac{z^2}{n}}

]

Where:

- ( p ) is the proportion of positive reviews.
- ( n ) is the number of reviews.
- ( z ) is the Z-score for the desired confidence level (e.g., 1.96 for 95% confidence).

This formula provides a more conservative estimate of a business’s rating, especially when the number of reviews is small, preventing a single bad review from drastically lowering the overall score.

#### Conclusion

Google My Business reviews are an essential factor in local SEO, and their impact goes beyond just the star rating. While the average rating is a simple calculation of all review scores divided by the total number of reviews, Google’s algorithms also take into account factors like review recency, volume, and even potentially more advanced statistical methods like the Wilson score.

Understanding how these reviews are calculated and scored helps businesses better manage their reputation and local SEO performance. It’s vital for businesses to not only collect reviews but also respond to them and use them strategically for both ranking improvement and customer trust.