When technologists talk about artificial intelligence and customer experience in the same sentence, they’re usually talking about bots. More specifically, pundits generally focus on how the mass deployment of support bots has seriously reduced CSat scores in nearly every major industry.
Bots, however, employ a very simplistic and inexpensive form of AI programming that’s far too primitive to really meet the needs of customers in the real world. Savvy companies are now deploying far more sophisticated AI mechanisms alongside skilled technicians to reduce wait times and ensure that everyone’s diverse needs are met.
Defining CSat Metrics
Computer scientists and marketing experts don’t have very much in common. Statistics may be the only area where the two fields do overlap regularly. Programmers and customer service gurus both want to interpret information in terms of easily digestible metrics.
Measuring customer satisfaction isn’t easy, but some companies are using what they refer to as CSat key performance indicators. These are often part of the balanced scorecard sheets businesses use to find out how frustrated people are with their support crews.
Outsourcing support teams began in the early 1990s, and customers quickly jumped ship from many brands that adopted the practice. An overwhelming majority of larger firms have been able to move sales and support teams overseas because they revamped the process they use to select overseas personnel. Companies are quickly adopting the same CSat metrics used to judge call centers when considering their customer’s experience with AI support agents.
Using AI to Replace the Most Disliked Aspects of Customer Service
A major cellular provider has decided to double down on this kind of thinking and look at CSat scores for the journeys their customers currently take. They found that most people disliked phone menus and call center runaround procedures. As a result, they replaced phone menus with real people who can speak to a customer. However, they armed this personnel with predictive apps due to the length of time it would take a representative to find an answer in a data.
The kind of AI that powers these apps can learn more about a company’s particular needs over time. Since they can draw connections between different types of questions using neural networks and cryptographic ledgers, these apps can often figure out which questions customers will ask before they’ve even picked up the phone. This saves time and manpower, but most importantly it eliminates some of the things customers like the least about making a support request.
Picking the Right Tools for the Job
Expedience is always valued in today’s fast-paced business environment, but that doesn’t mean firms should rush to select AI solutions to work alongside their customer service reps. This kind of thinking lead companies to replace their human teams with bots. Firms also need to budget their time to provide a generous onboarding period in order to work out any bugs with their AI tools before they’re let loose on actual customers.
Larger enterprise-level firms often find it easier to install a cloud-based knowledge management solution before they opt to integrate AI technology everywhere. Even then, managers will have several different choices to consider before they arrive at the right solution for them. Some tools store each query as a block in a cryptographic ledger chain, which provides a constant history for reps to go back and look through when searching for answers.
Other companies have sought to leverage existing local cloud networks to deploy their knowledge management tools. These might save customer queries to a traditional transactional database or tree structure. The software can then later recall common questions to help representatives predict what kinds of questions they might be facing as soon as they receive a call.
Privacy concerns are a major issue in either case. Customers may want their support requests to remain discrete. Fortunately, companies that use SMS or email services to communicate with their clients can easily anonymize their client’s data without losing the ability to analyze it for quality control purposes.
Leveraging Every Bit of Stored Information
Providing that companies are able to collect this information without harming their already fragile customer relationships, then they have a great opportunity to leverage it by using predictive AI apps. Considering that at least one study suggests over 80 percent of customer interactions will be managed without human intervention in just two years, there shouldn’t be any shortage of data to look at.
By tracking where service requests are coming from and what kind of product they’re in reference to, these apps can match end-users with answers before they even finish asking their questions. This kind of technique can actually lead to greater levels of customer satisfaction when applied correctly. Companies that leverage it correctly stand to drastically reduce their wait times, but the best idea might be to move slowly and deliberately in the near future to prevent missteps.
