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From: Andrew Kortina To: [email protected] Subject: Improving Operations Quality through Measurement and Performance Management Date: Thu, 19 Apr 2018 16:15:52 +0000 Hi I'm one of the co-founders of Fin and wanted to let you know about some of the improvements we've made over the past couple months to deliver quality experiences. I also wanted to offer you a $30 credit towards your first requests if you want to try Fin out. Earlier this year, I wrote about some changes we were making at Fin to level up the quality of our service. This included providing a Dedicated Account Manager for every customer, as a well as a bunch of updates to our internal metrics and processes. In this post, I want to talk a bit more about improvements to service quality that resulted from two of these changes: (i) more comprehensive and accurate measurement of quality signals and (ii) better 'windowing' of metrics to accelerate performance management. How do you accurately measure service quality? A few common methods for measuring quality of service for a team include CSAT and NPS. CSAT can be useful for gauging relative quality (either comparing the average performance of different individuals on a team, or comparing your company to another in a similar category). NPS is commonly used to predict customer loyalty and historical trends in the quality of a service or product over time. Because both of these methods rely on customer surveys and provide only a sampling of customer sentiment and feedback, neither has 100% coverage that alerts you of all poor customer interactions. You must assume that many customers who had a poor experience will not take the time to provide you feedback. Furthermore, sometimes a customer may subjectively label an interaction poor when it's objectively not a failure. (NB: This may be the result of mismatched expectations, which is arguably itself a kind of failure if you subscribe to 'customer is always right' doctrine, but that is

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Independent research project. Not affiliated with the U.S. Department of Justice, FBI, any government agency, or Anthropic. All analytical text on this site is AI-generated (Claude, Anthropic) and iteratively fact-checked against source documents, but may contain errors. Verify all claims against linked EFTA sources before citing.
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