NICE Nexidia sheds new light on Customer Interaction Analysis
What defines a great customer experience? Everyone knows what it’s like to have a good experience, or indeed the opposite. But identifying the contributing factors to that top NPS score and raving fan has always been subjective and difficult to measure, never mind to proactively improve.
Lag indicators like customer feedback forms simply don’t deliver the necessary insight to enable contact centre managers to surface the factors which contribute to excellence. What’s more, with the customer conversation now taking place via omni-channel digital platforms, comparing like-with-like becomes impossible. That is until you bring in AI-powered sentiment analytics, which automate review and analysis of the entire experience, and distil the learning into actionable feedback.
Nexidia Analytics reports across 100% of customer interactions through the complete customer journey, using natural language processing to meaningfully parse complex datasets and compare outcomes regardless of the channel used. This enables customers not only to increase customer satisfaction and conversions, but also to identify any risk and compliance issues and training opportunities, the kind of data which might only surface via a random check or complaint investigation — thus improving operational efficiency as well as sales.
This kind of deep prescriptive data analytics is only possible with the advent of artificial intelligence and is orders of magnitude more sophisticated than the ‘positive-neutral-negative’ sentiment analysis algorithms of the past.
The way human beings talk in a customer service interaction is not only forever changing as language, metaphor, reference and slang continue to evolve, it is also nuanced and complex — encompassing linguistic phenomena from sarcasm to hyperbole. Detecting the real underlying emotion being expressed is something most humans do well, but until very recently, computers struggled with.
Agents are human too, and with Nexidia Analytics in place, problems such as compliance violations can be pinpointed and resolved in near-real time, and complete transparency and accountability provided. Remedial coaching and correction for agents can be completely personalised and relevant, helping all parties work towards better outcomes: a win-win for sales and support teams.
Machine learning technology enables the application to improve continually as the dataset grows, comparing each outcome against the interaction which produced it, to iterate continually towards near-perfect accuracy: the extent to which the emotion detected correlates with the NPS and outcome of the call. Being able to organise the content via topics and themes yields powerful overviews, which support the KPIs of the business and its ongoing requirement to remain competitive.
For the user, the complexities of the analyses are hidden behind a single, unified view, which makes trends and shifts effortless to identify, via customised dashboards. Whether a product is creating more problems than it is worth to support, or a particular agent has standout metrics good or bad, Nexidia Analytics surfaces the learning in a fast and intuitive way, without the need for data science expertise to decipher.
These insights will ultimately benefit the end customers too, because while they’ll never see the contact centre’s data visualisation, they’ll get more of the kind of conversations which score highly in terms of their own satisfaction. They’ll be served by well-trained and compliant agents, who solve their problems, and address their real needs — whether they state those needs clearly and explicitly, or not.
So ultimately, it’s a win for all, escalating improvements for everyone interacting with the system, from the brand to the agent to the customer.
That’s very satisfactory.