Taking a Data-driven Approach to Manage Customer Data in Support
There’s no way around it – in the modern business world, companies need data from all areas of their business to keep up. One industry that hasn’t typically been at the forefront of the data revolution is customer support. However, with many businesses now realizing that support isn’t always a cost center (and can sometimes even pay for itself) the demand for actionable data has been on the rise.
So, how exactly are companies leveraging support data for their overall business strategies? Let’s look at a few different ways to take a data-driven approach to managing customer data in support…
Sentiment Analysis – Every experienced support agent has received a long email from a customer. But, before committing 10 minutes just to read an email, wouldn’t it be nice to know the mood or tone of the message beforehand? This is what sentiment analysis does for support teams. It uses AI (artificial intelligence) technology to automatically assign a sentiment, such as “satisfied” or “frustrated”, to an email when it hits the inbox. In addition, a confidence score is added to each email so agents can understand the accuracy of the assigned sentiments. The true business value with this technology is speed and efficiency. For example, if a long email reaches a junior agent with the sentiments “sad” and “impolite” and corresponding high confidence scores, they can skip reading it and forward it on to their supervisor immediately.
SLAs (Service Level Agreements) – Regardless of the industry, responding back to customers in a timely and consistent manner is important. As a result, many companies have created SLAs that are agreed upon by both the business and the customer. These mutual agreements create a defined communication process that ensures a business isn’t ignoring the customer. It also creates an expectation with the customer, so they aren’t asking for updates on their issue every hour (unless of course that is defined as a requirement in the SLA). With sophisticated customer support software, SLAs can be monitored in real-time, with alerts popping up in the software should an issue be on the brink of violation.
Overall Customer Distress – We’ve talked about data and information including sentiment analysis and SLAs. But how do you connect these with traditional customer support metrics such as total number of tickets and average ticket close time? The answer is through a CDI, or Customer Distress Index. This enables a business to aggregate most of their key data points to create a score that lets them know how happy or unhappy a customer truly is. CDI is provided directly within a support software solution and can be easily monitored to help businesses craft their communication strategies with customers. For example, if a support manager notices a customer has a high distress score, a ticket automation rule may be set up so all tickets from that customer go only to a small group of experienced agents. In addition, a business can change the weights of each customer support KPI (key performance indicator) within the CDI so the score accurately reflects the expectations of their customers.
Highly Customized Reporting – Last but not least, sometimes a business needs to take their data offline to find those truly actionable nuggets of information. By leveraging customer support software that features highly customized reporting, it’s easy to get the data needed to find the right information so a business can make those essential decisions. Combining support data with information from other departments such as product and sales can paint a clear picture of every customer including their praises, pain points, and areas for growth. Aggregating business data from multiple sources isn’t just a great way of keeping customers happy, it’s also ideal for creating successful upsell opportunities. If you look at product data and see a customer constantly poking around your software trying to find a feature, then they reach out to support and ask about a feature that’s in a higher price tier, it’s time to capitalize on what may be a good sales opportunity.
To summarize, taking a data-driven approach to managing customer support is all about making the most of technology and collaboration. Use your customer support software to obtain a better understanding of customers on the support side, then take the information obtained and share it with other functional areas. Customer support data is essential to business growth because when a customer communicates, it opens a window into their perspective about your business. Don’t let that window close right away and for good, instead work with colleagues to maximize the immediate communication and long-term approach so an even stronger customer relationship can be built.
