To apply accurate sentiment scores to customer feedback and analyze omni-channel data. This focus helps the company increase customer and employee satisfaction, improve products and services, and boost revenue.
Partnership With Stratifyd
The company sought Stratifyd’s help to analyze customer surveys because they had one analyst manually combing through upwards of 3,000 at a time, which took three or more weeks to categorize and assign sentiment.
The company wanted a platform with dynamic visualizations, something that could update all the time, and save analysts’ time.
Notable Use Case(s)
The company had trouble with other AI systems because it’s most concerned with data from disability and life insurance claims. Because these two types of claims are somber in nature, it would be hard to assign sentiment. For example, a customer may have responded saying, “They really helped me through this terrible time.” The fact that the review said “terrible” could attribute a negative sentiment to it, even though it’s actually positive.
Stratifyd’s unsupervised NLU and sentiment model drastically reduced the time spent separating these reviews into feedback versus complaint categories, and did so with great accuracy.
Thanks to our platform, multiple analysts saved more than 20 weeks of work per year to focus on mission-critical tasks.
Want to see how Stratifyd could help your company do the same? Schedule a demo today.