The 'So What?' of Customer Reviews: Moving from Data to Actionable Strategy
Getting feedback is a great start. Now, what do you do with it? Many of us get a report full of customer review data, maybe from Google or an e-commerce platform. Our first thought is often to just find the average score. But this common approach misses the whole story and doesn't provide enough insight to act on.
This post will show you three better ways to analyze customer reviews that give you real business value and help you make clear decisions.
To see these methods in action, watch the video below. And while you're there, be sure to subscribe to the channel for more tips on using data to drive business value!
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You can get far more value from your customer review data than a simple average. By changing how you visualize the numbers, you can find insights that directly relate to business decisions. Here are three methods you can start using today.
The average review score can be misleading. Imagine you have a product with an average rating of 3 out of 5.
You might think, "Okay, we're doing okay, right in the middle." But what if you have a lot of one-star reviews and a lot of five-star reviews, with very few in the middle?
Your average is a 3, but that's not the full picture. You have a love-it-or-hate-it product.
This is where a histogram or a bar chart comes in. A histogram shows the distribution of scores.
Instead of just one number, you can see how many customers gave you a 1, a 2, a 3, and so on.
This helps you see if your product is consistently good, consistently bad, or polarizing. This view gives you much better insight than a single average score.
To get even more value from your data, compare your review scores to another business metric.
This is where you can find a connection between a customer's experience and their rating.
Let's use an example from a Zendesk data set: comparing customer review stars to hold time in seconds.
A simple scatter plot can show if there's a relationship between these two metrics.
You can plot review stars on the x-axis and hold time on the y-axis. You might see a clear trend: as a customer's hold time goes up, their review score goes down.
And when the hold time goes down, the scores go up. This shows a correlation you can act on.
While a scatter plot is useful, it can hide an important detail: density. When many data points overlap, a simple scatter plot just shows a single dot. You don't know if that dot represents one person or a hundred people. This lack of density information can lead to wrong conclusions.
To solve the density problem, you can create a jitter chart. This chart combines the idea of a bar chart with a scatter plot.
It spreads out the dots so you can see where the data points are most concentrated.
You can build a jitter chart by adding two new columns to your spreadsheet:
One for "review jitter"
One for "hold time jitter".
Using a simple formula, you can add a small, random number to each review star and hold time value. This moves the points just enough to show their density without changing the actual data.
With a jitter chart, you can clearly see that a shorter hold time (under 4 seconds) dramatically increases the chance of a 5-star review. This is a clear, actionable insight you can take to your team.
Avoid the average. The average review score doesn't show the full story and lacks business value.
Use a bar chart or histogram. This lets you see the distribution of scores, not just one number.
Compare metrics. Plotting review scores against another metric, like hold time, can show important correlations.
Go beyond the scatter plot. A simple scatter plot hides data density.
Use a jitter chart. A jitter chart spreads out data points to show density and reveal actionable insights, like the link between short hold times and high review scores.
Why is the average review score not enough?
The average score can be misleading because it hides the distribution of your reviews. You could have a mix of 1-star and 5-star reviews that average to a 3, but this doesn't show you the real story.
How do I know what metric to compare reviews to?
Start by thinking about what might affect a customer's experience. You could compare reviews to hold time, support ticket resolution time, or time from order to delivery.
What is the problem with a simple scatter plot?
A simple scatter plot can hide the density of data points. If multiple points have the same value, they appear as a single dot, which can be misleading.
Does adding the "jitter" change my data?
No. The jitter is a small, random change that spreads the points for better visualization, but it does not change the original data values or the insights you get.
What business value do these charts provide?
These charts provide value by turning numbers into actionable insights. They help you understand why customers are giving certain scores, which allows you to make better decisions.