Deciphering Customer Intent With Conversational Analytics


We’ve heard the buzzwords: artificial intelligence (AI), natural language processing (NLP), predictive analytics, and even internet of things (IoT). What does this technical jargon really mean for a business struggling to gain a competitive advantage?

Many companies often fall into the trap of chasing every new tech-enabled solution to help improve their customer’s experience and drive up sales. These distractions can cause business leaders to lose sight of the ultimate objective of why marketing even exists.

Renowned business management consultant and professor Peter Drucker, once wrote that the need for selling could be eliminated if the function of marketing was performed well. Drucker’s 1973 definition of marketing is still widely used today at many business schools to properly explain the task. Drucker said that while there will always be a need for some selling, “the aim of marketing is to know and understand the customer so well that the product or service fits them perfectly and sells itself.”

Drucker was referring to the process of learning as much as possible about a person’s intent to buy, and, more importantly, why they don’t.

Understanding intent begins with the simple act of listening. Today, marketing leaders can listen to their audiences more effectively than ever. New AI technologies have provided market researchers with the ability to collect consumer data that can be mined for marketplace sentiment intelligence. Many of these machine-learning methods are utilized as intent predictor mechanisms.

However, when the right AI is combined with NLP, business leaders have an easier way to ask questions about the data and to receive actionable insights. NLP allows any organization to parse through consumer intent and understand sentiment and perception. Now businesses have powerful devices for collecting behavioral signals, and these instruments are fueled by conversational analytics.

Gartner defines conversational analytics as the process of extracting usable data from human speech and conversation using natural language processing (NLP) allowing computers to obtain and organize data from the speech. This data can then be analyzed for sentiment and tone to understand consumer disposition during the buyer’s journey.

Evan Kohn clarifies in his recent TechCrunch article how leaders are increasingly adopting conversational analytics to inform decisions not only in how customer journeys are architected, but also to evolve overall product and service offerings. Such insights could become a game-changer and competitive advantage for early adopters. In fact, 51% of B2B leaders indicated that they were using intent data as a tool to better identify and assess their prospects in a buying cycle.

At Metric Centric we’ve seen how conversational analytics can impact an organization when it’s correctly applied and well-adopted:

  • Event coordinators leverage conversational insights to determine top-of-mind topics and interests to structure conference agendas and keynote themes
  • Media groups gather conversational data to determine which influential content needs to be prioritized based on audience sentiment
  • Private equity groups analyze VoC data collected from review sites like Amazon, Consumer Reports and ThriveMarket to acquire marketplace insights that cannot be found on any balance sheet
  • Universities are using NLP and chatbots to observe the interests of lifelong learners seeking professional development, thus advancing their continuing education programs
  • The USDA listens to farmers and ranchers across blogs, forums and social networks about practices, procedures, and other regulatory issues. Administrators gain insights from these peer-to-peer conversations and can better understand the day-to-day challenges of agriculturists

How you sell and what your process is does matter. But how your customers feel when they engage with you matters more. Bring the voice-of-the-customer into the heart of your organization as you align their needs with your commercial objectives.

Use conversational analytics to understand consumer intent and recognize that your buyer’s journey is personal.