BirdEye’s NLP engine, Athena, humanizes big data so you can understand the sentiments expressed in feedback and improve operations at the location and corporate level.
Monitor sentiment levels from customer feedback to understand your strengths and weaknesses and discover trending topics in your market as a whole.
Easily group relevant keywords into custom categories to measure different areas of your business. Refine customer feedback insights even further with sub-categories.
View sentiment trends by topic, zoom in on specific keywords and adjectives, and put it all in context with verbatim snippets from review text.
On pace to open one new location every five days, Blaze needed to accurately measure and address the increasingly
overwhelming volume of online customer feedback received across a breadth of social channels.
With BirdEye, merge related topics into clusters automatically to improve accuracy. Manually un-merge topics at any time.
Measure review volume, ratings, and sentiment from customer feedback at a location level to identify your highest and lowest performers.
Teach Athena what topics you value by changing topics and keywords to positive or negative, or adding new topics and keywords you want to track.
Higher customer satisfaction drives higher same-store sales
We’ve proud to announce our Natural Language
Processing engine, Athena, just won a silver
Stevie Award for Best New Product.
How does BirdEye turn customer feedback into