How Machine Learning Can Improve Your Contact Center

In recent years, businesses and other stakeholders have increased spending on artificial intelligence (AI) and machine learning (ML) projects. The machine learning industry’s value crossed a billion dollars in 2020, with predictions of tripling by 2025. Businesses of all sizes leverage ML projects for several functions, including contact center operations. Here are some ways your company can use machine learning to improve your contact canter.

Efficiency

 

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Often, the first thing that comes to the mind of a small or mid-sized business owner when talking about an ML project is the cost.

Of course, that’s a valid concern. However, the cost of machine learning is nothing compared to the core benefits. Machine learning can be a powerful solution for your call center. It reduces the burden on live agents and can significantly cut costs in setting up and maintaining contact centers.

 

Traditional call centers demand huge expenses to procure phone hardware, build operational centers and traditional channels to field their contact center operations. Modern companies can use SaaS virtual call center platforms, which reduces all expenses to periodic subscription fees.

Automation

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Today’s customers have endless expectations, often outpacing traditional call centers. This can be ascribed to several reasons, including the progress in worldwide internet and smartphone proliferation rates. Close to 80 percent of the entire world’s population are online, and they create over a billion posts on Facebook, half a billion on Twitter, and several million on Instagram. Managing repetitive queries from all these platforms can be a little demanding. But thanks to cloud options like www.brightpattern.com, contact centers are gradually catching up.

 

So, how are virtual call centers using ML technologies to satisfy customer expectations? The answer is simple: automation. Traditional call centers may have to pick up phone calls one customer after the other, and business users may have to queue when there is a downtime issue or when live agents are out of the office. In contrast, ML platforms use chatbots to manage customer queries from varying platforms in real-time, a unique characteristic of omnichannel solutions.

 

Due to their iteration benefits, modern companies are increasingly adopting omnichannel contact center solutions. Bright Pattern, for instance, is touted as the only true omnichannel cloud platform. Using such platforms affords you multiple options to leverage ML automation for scalability and significant competitive advantage perks.

Analytics

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Data is the mainstay of efficient customer relationship management; the more you know about customers, the better your chances of providing solutions that fit their unique needs. Machine learning is a deep learning technology that enables and affords contact centers a well-rounded view of their customers’ behaviors. Data from McKinsey and Gartner indicate that companies can reduce average handle time by up to 40 percent, and less time dealing with customer queries means more time to ramp up operational value.

 

Companies can also cut employee costs by up to $5 million and boost conversion rates on service-to-sales calls by nearly 50 percent. Businesses can record and process data from customer interactions faster with machine learning. Deploying features like sentiment analysis, natural language processing, etc., to enhance data quality and ensure better prediction results can be a good idea.

Personalization

The world’s reliance on data keeps expanding; however, excessive data with no systems to clean and manage the data volumes usually churns minimal results. Data abounds in all our efforts today. What is left is how businesses can scale past the optimization problem? Machine learning can give customers a better grasp of what customers say, when they say it, and what they expect.

Businesses can trust their contact center function to field an effortless customer experience with these insights.

 

All in all, machine learning comes with several use cases, and businesses can rely on ML to produce contact center results matching the fast-paced business environment and highly-changing customer needs.

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