Today, $41 billion is lost by US companies each year following a bad customer experience. Now more than ever, a company’s ability to create a support experience that can be personalized and improved in real-time is a critical market differentiator.
But making customers happy isn’t the easiest job—even when you think you are doing everything right.
In study after study, consumers cite a lacking customer service experience as the reason they parted ways with a brand:
- 32% were fed up with speaking to multiple agents
- 29% were annoyed by a lack of staff knowledge
- 25% simply didn’t want to be kept on hold anymore
Today, customers expect the right answers, across multiple platforms, with as little wait as possible.
Personalizing the support experience by meeting each customer with the right message at the right moment is seen as critical to reducing churn, but can be complicated.
Before big data, churn rates often came as a quarterly surprise and the effect of labor-intensive improvement efforts weren’t known until after the fact. Without real-time data to react to, solving customer service problems like churn and long wait times were often a matter of trial and error.
Luckily, businesses today are able to integrate a form of artificial intelligence, or “machine learning”, into all of their business operations—including support—to make improvements on-the-fly. This technology also has the capability of “learning” from the data continuously flowing in to make incremental improvements that add up to big successes. Here are 3 ways machine learning is putting an end to customer service problems:
Route tickets to the best agent
Machine learning can help “unclog” the bottlenecks that often occur when agents receive tickets they are not best suited to solve.
Download our Pinterest Case Study to learn how Pinterest achieved 87% first-touch close rates and 250% improvement on CSAT.
Support centers solve the issue of long wait times by letting technology classify tickets to the right queue, then routing tickets to the team with the best capabilities to solve the problem. With machine learning, it’s possible to utilize a vast amount of data points, such as the sentiment of the customer's inquiry, to escalate tickets appropriately, and provide customers with the best possible experience.
Provide ideal templates, consistently
Rogue, one-off templates often slow down processes, confuse efforts and work against all attempts at consistency in the customer experience.
Machine learning is able to create and improve customer support templates continually and anticipate which one should be used, so the message each customer is getting is always the right one at the right time.
Scale services, not headcount
Allocating tickets efficiently while keeping overhead at a minimum is a common challenge. Often companies experiencing quick growth have difficult investment choices to make. They need capital to support continued production but without investing in headcount they stand to take a hit when it comes to customer satisfaction levels. This can especially be a problem for companies who experience seasonal spikes in support calls.
Machine learning allows companies to be remarkably more efficient in routing and response time, to effortlessly add and improve on templates as services are added, and to anticipate support issues by using data coming in across every channel. For this reason, companies utilizing machine learning technology are able to get much more out of the resources they already have and add headcount only when it is absolutely necessary instead of as a knee-jerk reaction.
If you’d like to find out how Wise.io can help your team eliminate these common customer service problems and many others while increasing customer satisfaction and ROI, download our Automation ebook.