Top

Adding Machine Learning And Artificial Intelligence To Customer Service

Paul Selby | Oct 23, 2017 676 views No Comments

Share on LinkedIn

ai-smallMachine learning (ML) and artificial intelligence (AI) continue to dominate the news, as companies race to create and adopt solutions to increase speed and efficiency as well as improve the customer experience. Last week, ServiceNow followed up on announcements made at the Knowledge17 user conference by formally introducing Agent Intelligence. This blog post provides details.

Two pieces of research were also released with this announcement. The first, a survey of 500 CIOs in 11 countries on three continents and across 25 industries conducted in conjunction with Oxford Economics, revealed:
  • Nearly 90% of CIOs are using or plan to use ML
  • The number of CIOs that will make at least some investment in ML will almost double (from 35% in 2017 to 63% in 2020)
  • More than half of CIOs (52%) say they are already automating more complex decisions
  • In terms of the value expected from decision automation over the next three years, 83% cited speed of decision, 87% said accuracy of decisions, and 69% believe it will drive top-line growth
The second piece of research was a report by Accenture. In it, they identify some of the challenges facing customer service (such as agents challenged to select from hundreds of categories and assignment options for high case volumes) and how ML and AI can improve the accuracy and speed of this type of work. Using those figures, they also conservatively estimate the real financial benefits of deploying Agent Intelligence for customer service.

So what does it all mean for customer service? For one thing, that ML and AI are becoming more mainstream and easier to adopt. For another, companies that fail to take advantage of them will not only continue to struggle to keep up with customer service work, but they might also be at a competitive disadvantage. If you have not yet made the move to ML and AI in customer service–or elsewhere in your business–allow me to summarize the advice found in the research cited. (But don’t let that be an excuse to not review them when you have a chance.)

Identify Automation Opportunities

The Accenture report demonstrates the business value of ML and AI is there, but today only in certain circumstances. Unstructured, redundant, and mundane tasks are the works patterns that typically benefit from automation. The increases in productivity and time savings then allow humans to focus on higher value work.

Agent Intelligence, for example, focuses on the categorization, prioritization, and assignment of customer cases–a high-volume, arduous activity that can be slow and fraught with errors. Not only can Agent Intelligence improve the speed and accuracy, but it can have a positive impact on customer satisfaction by preventing such delays to resolution.

Start With And Maintain High-Quality Data

Your data is the basis upon which your ML is founded, and serves as the foundational knowledge AI will use to perform its duties. That being the case, what is capable of automation will be highly dependent upon your data quality. Companies must evaluate if their processes have been digitized to the extent they can capture the correct data to build and improve ML algorithms. It is also worth investigating if there is data obtainable from outside your company that could further enhance the quality of your ML efforts.

Measure And Report

It is critical to continuously measure outcomes to reinforce the value ML and AI brings to your organization. The same metrics for volume, productivity, and efficiency are great initially to compare ML and AI to human efforts, but new metrics are also necessary.

A capability like Agent Intelligence that employs both ML and AI means measuring both sides of that coin: the percentage of ML recommendations accepted as correct and put into productive use as well as the speed, efficiency, and accuracy of the AI component. This will aid in continuous improvement efforts.

Empowering Your Customer Service With ML and AI

As ML and AI continue to improve, the limits of what’s possible will further erode while it also becomes easier for companies to adopt and expand automation throughout their business. ServiceNow’s Agent Intelligence is but one example of automating the mundane, daily work in customer service thanks to ML and AI that doesn’t rely on data scientists. As you plan or even expand your investment in this area, look around the marketplace: you now have the option of selecting a platform that provides strong customer service capabilities with a growing arsenal of ML and AI capabilities.
Print Friendly, PDF & Email


Recent Editor's Picks:


Categories: BlogCustomer AnalyticsEnterprise TechnologyService and Support

676 views

No responses yet, why not leave yours?

Add Your Comment (All comments are reviewed by moderator, no spam permitted!)