To analyze both structured and unstructured data from general and targeted surveys to understand pain points and improve the overall experience for banking customers.
Manually analyzing and reporting on the large amount of customer experience surveys was taking analysts a significant amount of time. The company turned to Stratifyd for a solution in terms of ingesting unlimited data volume, providing opportunities for more interactive data analysis, the ability to dive deeper into insights, and easily maintaining the accuracy of various taxonomies.
During the analysis of a particular time period’s Net Promoter Scores (NPS), Stratifyd built a dashboard with unsupervised topic modeling functionality. This resulted in about 14 different topics, the ones with the lowest sentiment scores being “driver’s license,” “new policy,” and “show ID.”
The customer feedback team spent weeks trying to figure out why NPS had dipped across all lines of business during this time period and theorized that it was because of a new policy requiring customers to show their IDs at financial centers to increase security. This use case supplied confirmation through numerical data about the fluctuation in customer satisfaction scores and provided tangible reasoning that analysts could take to leadership, giving them the opportunity to decide whether to keep or discard a policy.
Another use case surfaced from a time when analysts saw an increase in NPS but couldn’t determine the reason for the shift. Stratifyd split the data into two subsets, one with surveys that had numerical and textual data and one that only had numerical data. The former remained flat while the latter saw an increase in NPS. This allowed the company to see how easy it is to use our platform and see an increase in accuracy in terms of customer analytics.
Stratifyd’s semiautomatic taxonomies and machine-learning functionality resulted in time saved for analysts, the ability to dive deep into data, and easily analyze textual data.
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