Customer Success Analytics
Unlocking insights from customer complaints using Numbers Station
A leading B2C retail company was facing a significant challenge with a recent increase in customer churn rates. The company had a large volume of customer complaints but lacked the resources to analyze the data beyond manual annotation. The company deployed the Numbers Station Data Intelligence Suite to categorize customer complaints. Using Numbers Station, data analysts with no AI expertise developed custom complaint categorization models. They were able to identify that a large number of complaints were due to delivery issues in New York State. The analyst team shared these insights with the delivery operations team, who was able to address the issue and ultimately improve customer satisfaction and reduce churn.
A leading B2C retail company was facing a significant challenge with recent increase in customer churn rates. Most of their customer analysis had been based on structured data that was amenable to analysis (transaction and customer databases). However, all their hypotheses using that data did not lead to any meaningful conclusion on why customer churn had suddenly increased. The company also had a large volume of customer complaints which was highly unstructured data but lacked the resources to analyze the data beyond manual inspection. The company was seeking a more scalable solution to understand the root cause of customer dissatisfaction and reduce churn rates.
Categorizing customer complaints to understand the root cause of customer churn.
The company's main challenge was the lack of resources to analyze the unstructured complaint data. The team in charge of analyzing that data was mostly composed of data analysts with no ML or AI expertise. They tried to manually analyze the complaints but quickly realized that they would need a more scalable solution to derive any meaningful insight.
The company turned to Numbers Station's data intelligence suite to make sense of their complaint data. With the help of Numbers Station, analysts with no ML and AI expertise were able to build categorization models on top of their ServiceNow complaint tickets. Ultimately, they were able to identify patterns and trends in customer complaints, and found serious delivery issues in New York state.
With this information, the company was able to work with the delivery service to fix the issue and improve their delivery process, resulting in a reduction in customer churn rates and an improvement in customer satisfaction.
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