Track customer service KPIs that go beyond vanity metrics. Let's talk about the data-driven metrics that align with retention, automation, and customer experience success.
These customer retention strategies improve your onboarding, support, and feedback loops so you can effectively build loyalty and gain referrals.
Customer service challenges can go beyond slow response times. Understanding these eight common customer service challenges can help your team prepare.
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A practical guide for leaders who want the benefits of outsourcing without sacrificing quality, control, or brand voice.
Learn how to use AI in customer service without losing your brand voice. These 5 practical examples show how AI + human teams deliver better outcomes.
Outsourcing SaaS development isn’t just about saving money—it’s about scaling smarter. Learn how AI-powered outsourcing partners can help you build faster, better, and with less stress.
Data cleansing (or cleaning), is used to refer to the process of detecting and correcting inaccurate, corrupt or unusable data. It is an essential step before any data analysis project, since every step after it assumes the data is “clean” or, in other words, trustworthy and accurate.
In Part 1, we were introduced to the main data types and what you need to look out for in your dataset before you set out to clean it. Here, we will be taking a look at the actual cleaning steps required to get your data ready for service.
The outsourcing industry is notoriously confusing and we’ve all heard horror stories about what can happen if you choose the wrong vendor. We compiled a list of six questions you should absolutely ask each outsourcing vendor that you consider to help determine if they will provide a quality service.
An effective customer service strategy is a cornerstone for building lasting customer relationships, driving customer loyalty, and enhancing overall brand reputation.
