Companies have long relied on web analytics data like click rates, page views and session lengths to gain customer behavior insights.This method looks at how customers react to what is presented to them, reactions driven by design and copy. But traditional web analytics fail to capture customers’ desires accurately. While marketers are pushing into predictive analytics, what about the way companies foster broader customer experience (CX)?
Leaders are increasingly adopting conversational analytics, a new paradigm for CX data. No longer will the emphasis be on how users react to what is presented to them, but rather what “intent” they convey through natural language. Companies able to capture intent data through conversational interfaces can be proactive in customer interactions, deliver hyper-personalized experiences, and position themselves more optimally in the marketplace.
Direct customer experiences based on customer disposition
Conversational AI, which powers these interfaces and automation systems and feeds data into conversational analytics engines, is a market predicted to grow from $4.2 billion in 2019 to $15.7 billion in 2024. As companies “conversationalize” their brands and open up new interfaces to customers, AI can inform CX decisions not only in how customer journeys are architected–such as curated buying experiences and paths to purchase–but also how to evolve overall product and service offerings. This insights edge could become a game-changer and competitive advantage for early adopters.
Today, there is wide variation in the degree of sophistication between conversational solutions from elementary, single-task chatbots to secure, user-centric, scalable AI. To unlock meaningful conversational analytics, companies need to ensure that they have deployed a few critical ingredients beyond the basics of parsing customer intent with natural language understanding (NLU).
While intent data is valuable, companies will up-level their engagements by collecting sentiment and tone data, including via emoji analysis. Such data can enable automation to adapt to a customer’s disposition, so if anger is detected regarding a bill that is overdue, a fast path to resolution can be provided. If a customer expresses joy after a product purchase, AI can respond with an upsell offer and collect more acute and actionable feedback for future customer journeys.
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