If you’re responsible for customer success, you know how important customer satisfaction metrics are in gaging and improving customer retention and lifetime value. But typically, these metrics are calculated with narrowly focused point solutions or complex analytic packages.
At Teradata Aster, our customers are looking at their customer data in a whole new way to predict satisfaction scores.
Our customers have found that point solutions – while perhaps satisfactory for the specific problem they are solving – provide limited development capabilities and make it difficult to factor in data sources beyond those for which they were originally designed. Leveraging analytic packages like R and SAS, data science teams have built incredibly insightful customer satisfaction models. But data science skills are in short supply, and executives and analysts are often left wondering about the impact of slight tweaks to the models and whether new data sources would add value – not to mention whether sampling techniques miss key activities in the data.
So what’s different about how our customers look at customer satisfaction? Consider these four points:
We dove into details on the above points in a webinar earlier this month. Enterprises operate in a manner in which small improvements can mean big changes in terms of real dollar impact. Imagine the impact of better understanding your customers' happiness. To learn more about how Teradata Aster lets you create extremely actionable customer satisfaction scores leveraging all data types at scale, watch the webinar recording or email me at firstname.lastname@example.org.
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