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From Data Overload to Decision Analytics with Contact Center AI – Three Tips

From Data Overload to Decision Analytics with Contact Center AI – Three Tips
February 09, 2021

Co-author: Aditi Lamba

Multi-experience is changing the way contact center leadership is thinking, as described by Yatindra in his blog. One takeaway is the drive for architects to move all new tech from science to solution. One such tech is sentiment analytics, which has been evolving for the past 3-5 years and has now become mature enough to branch out into different unique segments. Before we get into the tips, let us look at the need this tech is trying to solve.

From visiting brick-and-mortar stores for shopping to saving products in an e-cart, customers have evolved and so have their buying journeys. This evolution in customer sentiment demands even the enterprises to evolve in their business strategies, so that their methods complement the contemporary customers.

Taking contact centers into consideration, sentiment analytics entered the picture just recently, helping enterprises discover ways to understand customer sentiment and, therefore, increase the customer lifetime value and reduce churn. The next key step is to leverage sentiment analytics to automate call analysis and QA workflows. Incorporating such an AI-based sentiment analysis technology will bring speed and precision in decision-making. The need of adopting sentiment analysis can be emphasized by Gartner’s prediction regarding next-gen technologies:

“By 2022, 70% of customer interactions will involve emerging technologies such as machine learning, chatbots, and mobile messaging, up from 15% in 2018.”

By 2022 70% of customer interactions will involve emerging technologies such as machine learning, chatbots, and mobile messaging, up from 15% in 2018

Why transition from a data to a decision-making approach?

The pandemic situation and the lockdown measures have ushered in the new normal. All aspects of our lives have witnessed a drastic change, be it working in a remote location or even participating in virtual family events. This unpredictable scenario necessitates that we all adopt and adapt to the best practices to help us survive in the present times and thrive in the future.

The lockdown measures affected the supply chains, inventories, and, in turn, extended the time for deliveries, all of which directed more queries to the contact centers and put pressure on the agents. In such a situation, business leaders are focused on maintaining the CX quality by prioritizing their contact center strategies to give them the competitive edge.

Keeping this in mind, HCL is partnering with Observe.AI, a leading Contact Center AI provider, to go beyond traditional sentiment analytics programs and leverage decision analytics. Technologies such as Contact Center AI will assist the human insights by providing precise information. This AI assistance will help enterprises in the following ways:

Monitor agents’ performance and quality of response

  • Help decipher the meaning and context of customer interactions
  • Ensure 100% compliance and proactively prevent fraud

3 tips in making the transition from data to decision

  1. Analyze customer sentiment via text and tone

    While interacting with any person, we not only try to read between the lines but also try and decipher their tone and gestures to comprehend the intent. Enterprises are collecting the call transcript data to analyze the customer journey, but it is incomplete without including the understanding of the tone used.

    Enterprises need to deep-dive and get a clear picture as to what happens during a call. With Observe.AI, you can unlock tonality-based sentiment analysis by leveraging an underlying algorithm to understand if a voice interaction was favorable, neutral, or positive.

    For example, you can automatically sense moments of customer frustration where customers raised their voice and used profanity to understand what is going wrong, and how often that issue happens.

    Rather than data overload, you can access reports and integrated workflows that help put that data to immediate use. You can know if something is a one-off trend, or if it is a performance/compliance opportunity worth addressing.

  2. Leverage automation for more impactful evaluations

    Leveraging AI technology via decision analytics should ultimately empower the agent to perform better by providing deeper insights. Traditionally, QAs could not complete impactful evaluations quickly; it took 30 minutes or more to find and evaluate a single call. Agents received feedback on just 2-4 calls each month, sometimes weeks after they were completed, impacting rectification.

    Detailed evaluations alongside a timestamp transcript helps the agent to connect their behavior on a call to a root issue. Rather than ‘teaching to the test’ as traditional QA processes did, they can help agents understand how their actions impact the CX, as well as how they compare to their peers on key metrics. By using AI to automatically tag key points of interest on calls, such as supervisor escalations or excessive dead air, which indicate that an agent is lacking confidence, enterprises can improve the quality and relevancy of their evaluations.

  3. Unlock more time for celebration and agent coaching

    With the work-from-home model in place, it has become difficult to track the performance of the employees and compliment or train them according to the situation. Contact centers are one of the mediums where customers directly interact with the enterprise, and it is paramount that agents deliver the correct message, in conjunction with the brand guidelines. With Contact Center AI, you can see each agent’s top and bottom areas of performance to coach them quickly. More important, you can celebrate what top agents do best and scale it across the team.

    For example, a national moving company noticed that just 2% of its customer service representatives were up-selling packaging. When they coached every agent to listen for up-sells and use a new talk track, they drove hundreds of thousands of dollars in additional sales.

    Keeping soft skills in mind, one aspect of Contact Center AI is that it provides “empathy statements.” These are phrases like, “We are sorry for the inconvenience,” “I shall personally take care of that,” etc. These act as moments to connect with the customer on an emotional level.

The bottom line

Investment in the right technology at the right time will yield benefits for the enterprise, as can be seen in the trends of machine learning and mobile messaging, according to Gartner. With the adoption of Contact Center AI, enterprises can leverage its rapid response mechanism to maximize their own capabilities and utilize more time on training the frontline agents.

The pandemic situation made leaders realize that the key to maintain the competitive edge is to be rapid in response times. Enterprises that were slow to adopt and adapt to the changed working environment had to face the brunt. Being proactive in the adoption of technological intervention for the future will help enterprises stay ahead and sail through challenges smoothly.