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Conversational analytics are about to change customer experiences forever

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Evan Kohn

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A digital marketing and customer experience leader, Evan Kohn is chief business officer at Pypestream, where he created PypePro, an AI onboarding methodology used by Fortune 500 firms.

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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.

Tap into a multitude of conversational data points

How can companies actually consume conversational analytics? User traversal heatmaps illustrate how customers are navigating conversational journeys and interacting with the logic of specific flows. Moreover, free text topic modeling identifies trends among user utterances–the way different users pose the same question–which can be used to prioritize what customer service or marketing use cases merit further investments in AI and automation. And metrics such as the percent of engagements that escalate to a human agent, or survey responses based on emoji reactions, will add to a company’s understanding of how its products and services are ultimately performing.

Companies empowered by this level of CX data can generate insights that help them on a variety of fronts.

Create effortless customer journeys

How many insurers could stand to improve their claims submission process? Or how many mortgage lenders could reduce the number of hoops applicants need to jump through to refinance? Such linear processes can be improved by analysis of step-by-step dropoff data from conversational analytics, such as by identifying where in user traversal heatmaps customers might abandon a flow. Companies can then re-engineer these journeys to compensate.

Open-ended use cases can also be improved by gathering conversational insights. If a user runs into a technical troubleshooting issue with their online streaming provider, allowing the user to describe the issue in plain text rather than reacting to a list of frequently asked questions has the potential to not only speed up the resolution process for that specific user, but it can also offer the provider a faster cue to tackle an issue that may be affecting a much larger audience.

Offload manual efforts through hyper-personalization

Most customers ask similar questions, which means companies can offload the menial taskwork of answering them to automation. Thanks to real-time analytics, automation can adjust responses to a user’s disposition and their unique question.

This reflects the more significant trend toward hyper-personalization, where user-centric conversational journeys connect with customers on an individual basis. Traditionally, CX has been tied to transactions, with companies following a generic process to accomplish tasks. And while this type of analytical data used to require excessive time and human resources to compile, conversational AI compiles it quickly and uses it to power experiences with a high enough quality to deflect calls and emails into a contact center.

Evolve offerings

Staying in touch with what customers want drives significant business value: PwC found customers spend up to 16% more on products and services if they come with better CX. Traditional web analytics show user reactions, which limit the information companies can use to build better products and delight customers.

Insights from analytical data show what features connect well with users and what gaps may still exist. Users’ interactions produce data that ultimately allow them to “vote” on what matters most. Companies can use these behavioral insights to better align with their customers’ desires.

Empower customers to guide your company forward

Conversational analytics eliminate the “reaction only” paradigm as the power of communication shifts to the customer to speak as they would with their family and friends.

While conversational analytics adoption might seem daunting, many of the most common analytics tools like Tableau, Looker and Google Studio can visualize conversational data. The real barrier to cross is for companies to open up conversational interfaces in the first place, and in the right places. The sooner companies do so, the sooner they will accelerate the feedback loop and use conversational data as a competitive advantage.

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