Customer support is already the hero to many of your customers – offering an instant solution to their problems – but too often, they’re not seen by company leaders as the true asset they represent in the company.
Like a trip to the doctor’s office, your support department is responsible for taking the pulse of your company – instantly understanding what issues are clogging the arteries of business, and providing daily diagnostic tests of what works, and what needs improvement, fast.
Support also generates a tremendous amount of valuable data in the process. The information pulled from ticket resolutions alone offers a virtual snapshot of major issues. Those logs can be critical in strategizing better avenues of consumer interaction, as well as offering concise and measurable feedback of great value to other departments – sales, product development and marketing included.
In order to drive these broader organizational initiatives, support needs a data-driven approach to integrate the themes and information they directly receive from customers with information that can convince and influence other departments. The issue is, data gathered from these interactions, especially from support tickets, increases on a daily basis and becomes harder to manage and understand as the company grows. This is where machine learning can step in and help.
Machine learning is not just about analyzing the data, it’s also about understanding the “why” behind the issues as they emerge, and what support – and the organization as a whole – can do about them. With machine learning, support not only helps identify problems, but helps to provide solutions, using data to head off problems before they escalate. It can turn simple day-to-day client interactions into practical advice that can be used by all departments within a company, making the customer service team the hero of your organization.
Take your onboarding process, for instance, which marketing is always trying to improve. Support is the first line of contact and certainly knows the inside scoop on customers’ stories and experiences; good and bad. Machine learning can analyze these data points to identify potential sticking points and offer solid, actionable avenues for marketing (and other departments) to pursue as onboarding continues.
The benefits to be gained from integrating predictive solutions into your support team’s operations are many. Here are five ways to make support the MVP of your entire working group.
Elevate the conversation to the entire organization
Support is right there on the front lines, the first folks in the organization to hear about obstacles, challenges, and frustrations. This means that their interactions with customers can produce invaluable insights and spin-free, first-hand information that’s of immediate importance to your VPs of Sales, Marketing and Product Development. Support has a huge opportunity to take their frequently anecdotal, conversation-based information and make it usable and useful for other teams. The growing use of SaaS tools has made that process easier, but machine learning can help do this in an automated way, making customer-specific information available to a support agent - and also incorporating it into marketing automation software, sales CRMs and other systems where those teams can get the most value from it.
Connect the dots along the customer journey
In real time, information garnered from the support department can clearly demonstrate the key indicators of inefficient processes or gaps in channels. That data can also paint a clear picture of the good and bad of user engagement, in an anecdotal form, from customers themselves: “Our product person implemented a new flow procedure, and we noticed an influx of support tickets as an immediate result.”
Better understand consumer needs
Support is the first to hear clues about user frustration, and what it takes for problems to escalate to the point where a call is necessary. This data can point specific departments in the right direction. Marketing can get a clear picture of which customers required higher levels of support, or product development can see which products needed the most hands-on support. As well, support can offer warning signs of users frustrated enough to leave, an important heads-up to help reduce churn.
Predict and influence future actions
Just like “Groundhog Day,” customer support is in the unenviable position of handling the same issues, each and every day. But the traceable numbers in those patterns of repetition can also be an important tool in helping support learn from previous events, adapt to changing circumstances and optimize their team’s performance.
Given the dynamic mix of customers and product usage, support also has to adapt to be able to understand and explain as new products and services are released. The net result is an amazingly rich and deep source of customer data that can be collected and analyzed to find patterns, all of it helping your organization anticipate and react to your customers’ past and future decisions. Machine learning makes it easier for customer support to serve a more dynamic mix of customers and scenarios, and also adapt much faster whenever new products or services are released.
Be the champion of customer success
Your support team is made up of a wide range of talented individuals, many with particular strengths. Why not mine support’s data to help automatically pick the right person for the right case? Machine learning can route incoming tickets to agents with a better understanding of regional markets, to a seasoned expert for an important, long-term customer, or to an agent who’s especially skilled at calming down a frustrated, angry customer.
Customer support’s treasure trove of valuable insights is waiting. Are you ready to extract and make the best of that information?