How CATA Can Benefit from Customer Analytics
Stratifyd used its AI-powered analytics platform to assess customer reviews of the Chicago Automobile Trade Association members on Yelp.com. By looking at the friendliness of service professionals and salespeople, the overall quality of customer service, and the quality of repair work, we could understand how the overall customer experience rated on a scale of 1-5 stars.
Stratifyd analyzed the unstructured data, looking at the sentiment behind and frequency of different word pairs in order to create a clear view of customer satisfaction. There were three areas in particular that were most important to the results.
The Three Key Areas:
Customer Service: We looked at how knowledgeable the salespeople and service professionals were, as well as how willing they were to help customers determine the right steps to repair or find the right car at the right price.
Friendliness: We focused on aspects such as how employees engaged with customers and how well they were taken through the process of either buying a car or having one repaired.
Quality of Work: We looked at how customers felt about repairs done to their vehicle, whether they felt the work was done properly and promptly.
With these three main areas and the associated data, Stratifyd was able to clearly see the customer voice and analyze the areas where the Chicago Automobile Trade Association members were excelling and where they were falling short.
- On a rating of 1-5 stars, this is where our three main aspects ranked:
- Customer Service rated 4.47 out of 5 stars
- Friendliness rated 4.51 out of 5 stars
- Quality of Work rated 4.39 out of 5 stars.
From these three major factors, we generated an Overall Customer Satisfaction score of 4.43 out of 5 stars.
Looking at multiple angles for each set of data, Stratifyd could provide CATA with actionable intelligence regarding customer experience and allow them to make adjustments in those areas or regions where they were deficient. With a single platform, our clients can aggregate data from multiple sources both structured and unstructured, as well as visualize the data in such a way that any member of the team can move quickly from raw data to valuable insight.
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