Leveraging AI to assess voice of customer insights from unstructured online customer reviews

Customers on Amazon provide more than 1 million product reviews daily. Home Depot, Walmart, Best Buy and Google Shop are other e-commerce platforms equally rich in VOC. This data can be easily and cheaply accessed via APIs. However, to date, insights from all this data have been elusive for marketers and product managers. That is because the reviews are scattered across multiple platforms, they vary significantly in of level of detail (i.e. star ratings vs. text) and quality (i.e. fake reviews) and most importantly, written reviews are unstructured text which makes them hard to analyze in scale.

ReQiew was developed to solve this problem. Using proprietary AI technology, it collects, cleanses and analyses customer reviews in scale, identifying product features that were included in a written review and the customer sentiment towards those features.

In this session, we will present the insights of a project we did in partnership with Newsweek in which we created a ranking of Americas top 10 Home and Garden brands across 90+ product categories and 350+ brands only using ReQiew to analyze three years worth of customer reviews.

With ReQiew, you can answer questions like:

1. What is important to my customers when making purchase decisions in our category.
2. How do our products compare to our competitors’ on features that matter to customers.
3. How have customer perceptions of ours and competitors products changed over time (i.e. due to new version launches, product recalls, etc).

Consumers Demo Presentation by Supplier