Sharang Sharma
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Sharang Sharma

Sharang Sharma is a senior analyst at Everest Group located in our Gurgaon, India office

Using Technology to Assess Contact Center Agents’ Language Skills | Blog

By | Automation/RPA/AI

Do you know anyone who hasn’t had a frustrating experience because the contact center rep they interacted with didn’t speak their native language? We didn’t think so.

The truth is that while enterprises have multiple business reasons for establishing their contact centers in offshore locations in Eastern Europe, Latin America, and Asia Pacific, the reps’ language and communication skills often have a negative impact on the overall customer and brand experience.

And although many companies have developed their own solutions to assess candidates’ language capabilities, they’re plagued with multiple challenges, including:

  • Resource intensive: Developing language assessment solutions takes considerable time and resources. They need to be thoughtfully designed, particularly around the local nuances of the markets where they are being leveraged. This can escalate the development budget and timelines, and put an additional burden on L&D teams.
  • Lack of standardization: Most language assessment tests are developed by in-house experts in a specific region. This approach can be detrimental to organizations with operations in multiple geographies, because it lacks consistency across regions, and can leave gaps in the evaluation criteria.
  • Involvement of human judgment: Because humans are responsible for evaluating candidates, a lot of subjectivity comes into play. And human bias, whether intentional or not, can greatly reduce transparency in the candidate selection process.
  • Maintenance issues: The real value of these solutions depends on their ability to test candidates for unprepared scenarios. But regularly updating the assessment materials to keep the content fresh and reflect changing requirements further strains internal resources.

Third-party vendors’ technology-based solutions can help

Commercial language and communication assessment solutions have been around for years. But innovative vendors – such as Pearson, an established player in this market, and Emmersion Learning, which incorporates the latest AI technology into its solution – are increasingly leveraging a combination of linguistic methodologies, technical automation, and advanced statistical processes to deliver a scalable assessment that can predict speaking, listening, and responding proficiency.

For instance, technology-driven solutions may test candidates’ “language chunking” ability, which means their ability to group chunks of semantic meaning. This concept is similar to techniques that are commonly used for memorizing numbers. By linking numbers to concepts, a person can be successful in retaining large sequences of digits in working memory. Without conceptual awareness, memorization is hard.

During an assessment, through automation and AI, the candidate may be asked to repeat sentences of increasing complexity. Success in this exercise relies on the candidate’s ability to memorize complex sentences, which can only be done when they can chunk for meaning. A candidate’s mastery of an exercise to repeat sentences of increasing complexity is a great predictor of the candidate’s language proficiency.

Organizations that embrace technology-based solutions for language assessments can anticipate multiple benefits: reduced costs, decreased hiring cycle times, improved quality of hires, better role placement, freed time to devote to value-add initiatives, and improved customer experience and satisfaction. Ultimately, it’s a triple win for the organization, its candidates, and its customers.

Race to Reality: Full Contact Center Automation vs Fully Automated Cars | Blog

By | Automation/RPA/AI, Blog

The contact center industry is changing considerably due to technology enablement. Contact center automation is rapidly becoming a priority as centers increasingly embrace technologies such as artificial intelligence (AI), chatbots, robotic process automation (RPA), and robotic desktop automation (RDA) to handle customer interactions on rote queries like account balances, package tracking, and reservation confirmations.

A similar transformation is also taking place in personal transportation. Advancing technologies and intense competition are driving amazing strides in the autonomous vehicle industry. While cars aren’t yet 100 percent self-driving, companies like Tesla are already offering advanced driver assistance solutions that can pretty much take control of driving, albeit with human supervision.

With the perceived nature of each of these two industries, it’s easy to assume that contact centers will be fully automated in far less time than the two to three years some believe it will take for autonomous driving solutions to get you from one point to another without human intervention.

However, this is an incorrect assumption.

Indeed, counter-intuitive as it seems, it’s much more difficult to completely automate contact centers than it is to automate driving. Why?

Driving involves a large, but still finite, number of scenarios that need to be programmed for. But a contact center environment can throw up potentially infinite unique problem statements and challenges that enterprises cannot possibly predict and program for in advance. Yes, AI helps, but even that can only get you so far. At the end of the day, the human mind’s problem-solving ability far exceeds anything that the current or foreseeable technology can offer. And while most people would be more than happy to let robots take over the wheels on the road, they still expect and require human touch, expertise, and judgment for the more complex pieces that usually make or break the customer experience. Technology just isn’t sophisticated enough to handle these yet.

The degree of contact center automation that can be leveraged within an industry varies by process complexity

Although technology use in contact centers is in the early stages, we are already witnessing higher agent satisfaction and lower attrition rates in an industry that has one of the highest churns globally. And as robots increasingly take care of customers’ simple, straightforward asks, we certainly expect agents’ satisfaction to increase.

Of course, agent profiles will continue to evolve as they are required to deal with more challenging and complex issues leveraging machine assistance. This will, in turn, demand greater investments into talent acquisition and upskilling programs.

It will be interesting to see how all of this plays out in the next few years as technology becomes increasingly advanced and capable. The only thing we can say with certainty is that the customer experience of the future will be much more pleasant as irritations like long wait times, inept IVR responses, and repetitive conversations with agents who hold incomplete information become issues of the past…or, shall we say, smaller and smaller objects in our rearview mirrors?

Chatbots are Getting Smarter, and That’s Good News for Contact Centers | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog, Customer Experience

Enterprises and their contact center operations are increasingly adopting self-service technologies to improve the customer experience. Over the past several years, chatbots have become one of the most embraced. And it’s easy to see why.

chatbots in contact centers

Chatbots are evolving…

First generation chatbots were software programs that could hold a natural conversation on a text-based channel such as chat, email, or social media. Modern day chatbots’ capabilities – interchangeably called virtual agents – have leapfrogged ahead through use of Artificial Intelligence (AI), machine learning, and Natural Language Processing (NLP), which allow them to understand free flowing language and generate real-time responses. They are no longer bound to a single channel, but instead cover the entire ambit of services, including voice support. This helps them fit well with enterprises’ broader omnichannel strategy, as they can deliver a seamless experience across channels.

chatbots in contact centers 2

…and they know your preferences

Unlike earlier versions, today’s chatbots can even contextualize information in a conversation. Here’s an example:

Jim: I would like to know the status of my flight. It’s booking # PNR AZ12356.
Chatbot: Hi Jim, checking into it. It appears that your flight’s departure will be delayed by 4 hours, due to scheduling changes.
Jim: Can you cancel it and check for an earlier flight in the morning?
Chatbot: Confirming…
As per your request, I have cancelled your flight American Airlines 235 from Boston to New York. Based on your past bookings, I have three suggestions for alternative flights on the same day:

  • Delta 2897: Departs from BOS at 0430 hours and arrives at JFK at 0550 hours
  • Delta 2471: Departs from BOS at 0600 hours and arrives at JFK at 0720 hours
  • Delta 3167: Departs from BOS at 0640 hours and arrives at JFK at 0800 hours

Jim: Book one ticket in the second option.
Chatbot: Confirming availability. Based on your preferences, would you like me to book an aisle seat, and select a bruschetta omelet with coffee for breakfast?
Jim: Yes, that will be great.
Chatbot: Excellent. Your flight Delta 2471 from BOS to JFK will depart at 0600 hours on October 30, 2017. Your seat number is 4C. I have sent a copy of the ticket to your personal email id. Have a safe flight.

Notice how the chatbot contextualized the information based on unstructured and more natural language flow, and offered recommendations based on the user’s past preferences. These degrees of evolution have made chatbots much more self-service capable, and are significantly enhancing the experience that contact centers deliver to their client’s customers.

As with all technologies, chatbots come with risks

The end goal for today’s enterprises is to deliver the best possible omnichannel customer experience. Chatbots can help customers solve problems on their preferred channel of communication (voice and non-voice). However, the technology does have shortcomings. The well-known example of Microsoft’s Tay – a Twitter-based intelligent bot that had to be pulled down within 16 hours of deployment due to offensive tweets – highlights one technology gap that needs to be addressed.

Everest Group’s just released viewpoint entitled, “Chatbots Delivering Enhanced Customer Experience: It’s Easy to Get It Wrong” details how chatbots can fit in enterprises’ omnichannel strategy, the risks they need to be aware of, and how they can mitigate them.