Uncovering Support Personnel’s Potential with Machine Learning


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In the last few years, there has been a lot of concern on the part of many white-collar employees who fear that their jobs will be replaced by computers.

In the same way that factory workers lost their jobs to robots and machines in the 1980s and 1990s, office and service personnel worry that their jobs will be lost to automation and new AI and machine learning technologies. Isn’t the bot being developed to eliminate the customer service rep?

From my perspective working with several clients with extensive global support teams (who use our technology improve support team allocation), I’d say that the opposite is true. The more advanced technologies become, the greater the need for a human touch in customer service.

Though machine learning technologies are the engines behind many of today’s bots, I believe that the greatest value that this technology is providing the support industry is by helping human support workers do a better job supporting their clients.

One thing which our machine learning technology enables is uncovering the best support rep for each specific task. For example, for one client, our technology facilitated the understanding that the fourth most effective agent overall at one call center was the most efficient at dealing with high severity tickets dealing with newsletters and chats. This enabled that company to restructure their call center teams to improve specific agent efficiencies. But perhaps the biggest takeaway came from agent satisfaction: the agents themselves were happier because they could focus on the support issues they liked solving best. Though there are always support areas which none of the agents like, by using machine learning technology to optimize agent allocation according to specific task efficiencies, both performance and employee morale improve while costs decline.

We also utilized our machine learning technology to analyze the patterns of inbound support requests in order to predict the next day’s customer support tickets. By predicting the support requests, we enable clients to more effectively assign support agents according to the predicted issues, resulting in better operating support shifts, as well as improved management of surges in support requests.

According to this global client, the results of our predictions were more than 85% accurate, facilitating cost savings through the more efficient allocation of support teams.

And this client actually shared some of the specific results with support team members, which showed them first-hand how machine learning technology could improve their job, an important step in making them more open and embracing of machine learning technology.

By collaborating with support staff on machine learning implementations, as this client did, companies can reduce the skepticism and increase the openness to machine learning technologies. And with the job trends of the future, this is a necessary first step as we all migrate to a more technology-empowered work environment.

Roei Livneh
Roei Livneh is the CEO & founder of Curve (https://curve.tech/), a provider of machine learning-automated insights for business. He is a seasoned executive with expertise in customer experience and customer retention. Roei founded Gingee Games after having served as a product manager at Plarium Games. He has seen the change in the dynamics of organizations over the years and has created technology that grows and answers those changes. Curve came about as a need experienced by Roei’s customers – creating a marketing tool kit that is both easy to use and provides results.


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