Traditional VoC programs help brands measure their audience through surveys, interviews and focus groups. Today, it also includes measurement of social media and product reviews. The product reviews segment of…
Amazon Review Tracker
People trust Amazon reviews. Are you monitoring yours?
In the past 20 years, there have been well over 150 million product reviews on Amazon. It is one of the original sources for trusted product reviews, and trust them customers certainly do. With every product from A to Z, even when a customer isn’t completing their purchase on the Internet behemoth, they’re likely to be doing some research there.
The leading Amazon review tracker
With such immense influence, you may be seeking out a service to track all of your Amazon reviews so that you can analyze and take action to improve them. Channel Signal does just that. Monitor your Amazon reviews:
- From any date range
- For any set of products, including competitors
- Filter by star rating
- Filter by product category, text or topic
Not only does Channel Signal provide Amazon review analytics, you can see beyond Amazon data. If you have a sizable volume of reviews on other websites, we’ll pull them in. You can compare and contrast how reviews on Amazon fare with other partner sites.
How Real Brands Use Reviews: Tracking Reviews on Amazon Leads to 80% Bump in Understanding the Consumer
A leading shoe company was working with a product review monitoring system with limited functionality. This system did not allow for Amazon reviews to be monitored or analyzed. More than 80% of all their product reviews came from Amazon, so the company was missing out a lot of important consumer data about their products. The company decided to make a change based on a limited dataset from the few reviews they were collecting. Sales declined.
Then, the company gained access to all their Amazon reviews using Channel Signal. After analyzing the data, they discovered the component they changed was what most customers really enjoyed the most. It was just a small piece that they needed to change to boost overall satisfaction of the product. They learned not to make product changes based on an incomplete dataset.