In a recent issue of eContent Magazine, Mindy Charski delves into data journalism—how new tools and data sources are highlighting compelling stories and trends that have been hidden by big data. More open access to public datasets has allowed data journalists to develop more compelling and dramatic stories, all based on a foundation of data analysis methodologies. The results are such articles as “FBI Bank Robbery Data Shows Armed Guards Increase Risk of Violence” and “How Dark Money Is Taking Over Judicial Elections.” You might wonder, however, if the authors or sponsors of such articles started with the premise and found the data that matched, or if the data journalists and analysts pored over a variety of datasets and discovered a story that no one else saw.
Using new data analysis tools and sources to tell a story that is simple and compelling may be on the rise in journalism and news reporting, but it’s old school, tried and true, in Customer Support. Understanding what the data is saying about customer usage, product behaviors, problems, issues, glitches, intermittent quirks, and plain old bugs has been an endeavor nearly as old as product support itself. Even understanding which knowledge-base articles are being viewed and searched for helps improve the support experience.
Zendesk, the customer service platform company, does a great job timelining the History of Customer Support, but what’s not as apparent is the importance of analytics on the back end as a way to gain insight into what is happening with your products in on-premises customer environments, in the cloud, and in Customer Support. But rather than starting with a premise and trying to prove it, support analytics is all about letting the data tell the story, simple or complex.
Using new data analysis tools and sources to tell a story that is simple and compelling may be on the rise in journalism and news reporting, but it’s old school, tried and true, in Customer Support.
Calls, cases, tickets, chats. Whatever the method of communicating with customers, it all contributes to the ever-growing dataset used by Customer Support to provide better service, better products, better experiences for customers, and ultimately, more value from your products. The heavy lifting of analyzing that data is typically done by Support Analysts, Customer Support Managers, even Knowledge Management professionals, such as myself. Translating all that data into actionable stories—simple and compelling—for customers and Product Management is the key to achieving all those wonderful results. But dramatic?
Hyperbole, embellishment, exaggeration—all things to avoid when taking a data analysis approach to customer support; however, decreasing product downtime, proactively identifying technical issues, and even fixing those issues before customers are aware they exist can be pretty dramatic. Understanding how you want to interact with us guides our evolving touch points. Knowing which features, functions, or tasks are problematic guides our product development. And understanding how you want to learn about or troubleshoot problems as you use the product guides our ever-increasing knowledge base.
Analyzing customer support data enables that understanding, leading to improved customer satisfaction with your products and services. Simple, compelling, AND dramatic.