Templates are intended to create consistency in response to customers and efficiency for support reps. However, they often end up doing the opposite simply because they’re very difficult to manage. Especially with support teams that handle a high volume of requests throughout the day. It’s not uncommon for them to spend a lot of time sifting through hundreds of response templates and still not find the one that works.
How Support Templates Go Awry
An initial set of templates is usually created by the company for the support team’s use. Then, as product and service offerings evolve and new customer requests come in, new templates are created by reps or management in response to the changes.
If they’re added to the global database, there’s a level of control still available. But more often than not, support reps begin creating their own templates, either from scratch or by editing the existing templates, which makes it nearly impossible to track how templates are being used. Even when teams use fields that are triggered when the template is applied, that tracking often breaks if the language gets changed by an agent.
Each rep may have their own system for recalling which templates are where and how to best access them. However, these personal templates are only available for their use and aren't shared automatically with other agents. Others on the team are unable to benefit from them and management can’t readily review them for quality.
The end result, especially if this is allowed to go on for months or years, is a disconnected group of idiosyncratic templates that may or may not be of the best quality and certainly are not consistent with each other. It also causes duplicate template situations because every rep has their own unique take on frequently asked questions. In many organizations, templates have run wild.
How Machine Learning Tames the Herd
One of the simplest and most elegant means of gaining control of templates in your support team is to employ machine learning applications (ML).
ML can help connect the dots through the use of natural language processing, which means reading through a sample set of responses from the recent past and running them through a sophisticated series of ML algorithms that mimic human language associations. This allows a computer to:
Automatically identify the different templates used.
Determine whether templates are being used consistently across the support team.
Match which templates most effectively resolve the customer’s issues.
Show support managers if/what/how macros are being used, even to the point of identifying rogue agents not using appropriate templates.
It can even find the most effective personal templates created by agents and roll those out globally for the whole team. In other words, regardless of whether templates are made by the company or the individual reps, the very best versions of each response template can be surfaced for every rep’s use. Ultimately, machine learning improves the efficiency of customer support teams by actively optimizing the number of highly effective templates that can be used consistently across the board.
If you’d like to see this solution in action in your own support department, request a Wise Support demo today.