New Analytics, Machine Learning Tools Enable Better Customer Outcomes

Customer Analytics

New Analytics, Machine Learning Tools Enable Better Customer Outcomes

By Paula Bernier, Executive Editor, TMC  |  July 06, 2017

There’s lots of talk these days about using chatbots as a means to serve customers. And we typically imagine such interactions taking place without any human intervention. But there’s also a great opportunity to leverage artificial intelligence and analytics to assist living and breathing customer service representatives.

Just take a look at what companies like Salesforce and DigitalGenius are doing.

Salesforce in February unveiled Service Cloud Einstein. As we discussed in the previous issue of CUSTOMER, Einstein was designed to allow any companies to deliver more predictive and personalized customer experiences across commerce, sales, service, marketing, and more. And now Salesforce has brought Einstein, which was introduced in September of 2016, to the contact center.

Service Cloud Einstein includes Einstein Case Management, Einstein Supervisor, and Intelligent Mobile Service.

The case management piece identifies and appropriately routes high-priority cases. It presents agents with information about incoming inquiries. It can automatically serve up knowledge articles and videos to expedite resolution. And it can employ bots to make the first contact with customers, and then supply the basics to actual agents if and when they’re needed.

Einstein Supervisor provides managers with current data on such parameters as agent availability, queues and wait times so they can make more informed decisions. It also can predict customer satisfaction and make specific recommendations to improve the customer experience.

“For instance, a service supervisor at an appliance manufacturer gets an alert telling her there’s an increase in calls coming from owners of a specific dishwasher model,” Salesforce explains. “Drilling into the data, she discovers all the cases involve dishwashers made during a three-month period at one factory. The supervisor alerts management, who then proactively alerts other impacted customers and begins deploying mobile employees to fix all of the potentially impacted dishwashers – heading off what could have become a larger service issue.”

Intelligent Mobile Service, an app for Android (News - Alert) and iOS devices, can be used for scheduling and routing, provides real-time access to CRM data, and also has offline capabilities so mobile workers can work even when cell coverage is not available.

As for DigitalGenius, it automates the tagging and classifying of customer interactions, suggests answers, and can help leverage what’s been learned during interactions for future use. This solution, which works with other suppliers’ contact center software (the company has integrations with Salesforce and Zendesk and is adding more), does not focus on the voice channel. Rather, its focus is on email, mobile messaging, social messaging, and webchat communications.

Say you’re an airline and customer emails you that he lost his luggage during travel. Before an agent can respond to that question, the agent has to use a drop-down menu to classify the message, explained Mikhail Naumov, DigitalGenius co-founder and CSO, adding that it’s been working with KLM Royal Dutch Airlines.  One choice might be “lost bags”. But just finding and clicking on that category takes up 10 to 35 percent of  average handling time if the agent does it manually, he said. So the DigitalGenius system automates that.

As noted earlier, DigitalGenius also suggests answers to customer inquiries, which the customer service agent decides whether or not to use. And once a customer request has been satisfied, the system rates the resolution with a confidence threshold of between zero and 100 percent. If the machine has produced an answer with at least a 90 percent confidence rating, that email becomes automated so customers don’t have to wait for agent involvement to get an answer in the future.

DigitalGenius is among the companies mentioned in Gartner’s (News - Alert) 2016 Market Guide for Virtual Customer Assistants. Others include [24]7, Aivo, Artificial Solutions, Creative Virtual, CX Company, eGain, IBM, iDAvatars (CodeBaby), Inbenta, Interactions (News - Alert), IPsoft, Kasisto, Next IT, noHold, Nuance, Pat Group, and Xiaoi., which GE in November announced plans to acquire, also provides machine learning solutions. In an article in the September 2015 issue of CUSTOMER magazine, Jeff Erhardt, CEO of, wrote: “With the help of machine learning, you can put a lens on individual customer behavior and past outcomes to drive the desired outcome for every interaction. And in doing so, you will be taking advantage of data in a way that helps you be more flexible and relevant when engaging with your customers.”

Edited by Alicia Young
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