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Machine Learning and Customer Experience for Business Scalability
Human Resources. Human-centric customer service. Humans in cooperation and collaboration with smart technology. Both in B2B and B2C businesses, putting the human back in focus is imperative to success.
Consider Netflix. How it began, how it’s evolved, and how its efforts are seemingly leading the way for next gen personalization. Think: If you like this, then you may like (insert service or product here). Amazon does much the same.
Putting the Human Element Back in CX
When you call customer service with a concern or problem. What happens? Either there’s no phone number at all and you’re forced to send an email which you hope gets read by a person. Or if you do call, you push buttons trying to figure out which branch of the tree will get you to the correct person.
Chatbots have been one answer but they really only alleviate acknowledgement. We’ve all called a customer service number and spoken to two or more people about our issue. Bill Paterson, EVP of Salesforce, suggests a four-point, human-centric customer service engagement strategy, to help solve the problem. In addition, his article takes a deeper dive into putting the human back in customer service.
At the heart of the matter is putting Emotional Intelligence, care, and empathy back into the equation. Technology may be how people reach out, but it’s a human they want to speak to and connect with. When the two are paired, there’s a much better chance of success. And repeat customers.
Pairing Machine Learning with a Human-Centric Touch
While strategies and metrics still have a big role to play, there are other ways to measure customer success. Data gathered from your customers will only get you so far, but the human element, the human connection, supported by technology, is the next shift in Digital Transformation.
Machine Learning models can help predict what customers will want or need, but meaningful customer relationships are just as vital. It’s this pairing which can generate great service and scalability of today’s modern business.
Though there is a strong underpinning of engineering components in building models, only a portion involves code. Much of the effort goes into the pipeline and workflow systems and infrastructure. It’s at this systems level, Data Scientists can focus on design and implementation of production. This strategy ensures that before building good models, a good foundation must be laid. One portion of this workflow has been called the ‘art of Machine Learning’.
The ‘Art’ of Machine Learning
Data Scientists and Machine Learning Engineers have any number of ways to solve a problem. Dealing with such vast amounts of Data within a model is not unlike determining how to scale for a website which needs to handle large fluctuations in web traffic. The nuances of technology within the realm of human experience is an artform.
Though in the future, most engineering challenges will be automated and open-source will be a go-to framework. As tools improve and ETL processes improve, ML Engineers and Data Scientists will get the opportunity to focus more on models and less on systems.
But beyond the artform of experimentation and intuition is the growing trend for soft skills in tandem with technical skills. Those who can lead a technical team, who can communicate to non-technical professionals, and still have the Emotional Intelligence to navigate the human psyche. It’s these individuals who will be ready for the next step in leading businesses into the next generation of customer service.
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