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4 proven approaches to CX strategy that make customers feel loved
Customers have been “experiencing” business since the ancient Romans browsed the Forum for produce, pottery and leather goods. But digitization has radically recalibrated the buyer-seller dynamic, fueling the rise of one of the most talked-about industry acronyms: CX (customer experience).
Part paradigm, part category and part multibillion-dollar market, CX is a broad term used across a myriad of contexts. But great CX boils down to delighting every customer on an emotional level, anytime and anywhere a business interaction takes place.
Great CX boils down to delighting every customer on an emotional level, anytime and anywhere a business interaction takes place.
Optimizing CX requires a sophisticated tool stack. Customer behavior should be tracked, their needs must be understood, and opportunities to engage proactively must be identified. Wall Street, for one, is taking note: Qualtrics, the creator of “XM” (experience management) as a category, was spun-out from SAP and IPO’d in January, and Sprinklr, a social media listening solution that has expanded into a “Digital CXM” platform, recently filed to go public.
Thinking critically about customer experience is hardly a new concept, but a few factors are spurring an inflection point in investment by enterprises and VCs.
Firstly, brands are now expected to create a consistent, cohesive experience across multiple channels, both online and offline, with an ever-increasing focus on the former. Customer experience and the digital customer experience are rapidly becoming synonymous.
The sheer volume of customer data has also reached new heights. As a McKinsey report put it, “Today, companies can regularly, lawfully, and seamlessly collect smartphone and interaction data from across their customer, financial, and operations systems, yielding deep insights about their customers … These companies can better understand their interactions with customers and even preempt problems in customer journeys. Their customers are reaping benefits: Think quick compensation for a flight delay, or outreach from an insurance company when a patient is having trouble resolving a problem.”
Moreover, the app economy continues to raise the bar on user experience, and end users have less patience than ever before. Each time Netflix displays just the right movie, Instagram recommends just the right shoes, or TikTok plays just the right dog video, people are being trained to demand just a bit more magic.
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Conversational analytics are about to change customer experiences forever
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.
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
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Topbox raises $5M for its customer experience analytics service
Topbox helps businesses understand how their customers experience their products and where they run into issues by analyzing voice and text chats to surveys, social media posts and online reviews. Today, the company announced it has raised a $5 million funding round led by Telescope Partners, with participation from Cascade Angels, Flyover Capital and the Maryland Venture Fund.
Topbox CEO Chris Tranquill told me he first experienced the problem he’s trying to solve when he was running call centers with thousands of agents. All of the companies that contracted his services faced the same problem: understanding the friction points their customers were experiencing.
“We always had this vision that being able to really understand those friction points with deep context — that’s what the key is — but really getting to that granular level of detail so that you can have that context to support a decision,” Tranquill said. Say you want to understand what issues customers are having with a new shoe. Ideally, Topbox will aggregate all of the data across all channels about that shoe and help the company understand who the wearers are and what issues they are experiencing.
Theoretically, companies could do this on their own, but all of this data exists in various silos and combining those disparate data sets is a major challenge. Topbox uses its technology to ingest this data (and it’s pretty agnostic about where it comes from) and then runs it through its classification models. Indeed, as Tranquill told me, it’s this model that’s the secret sauce behind the company’s ability to classify data.
It’s not just about getting a high-level overview of your customer’s reactions, though. Tranquill stressed that users can go deeper. “The big thing for us is granularity,” he told me. “I can find high-level data all day long, but can I find the root cause?” With a few clicks, any Topbox user should be able to understand what issues their customers are facing, no matter whether that’s a product issue, a shipping problem or something else.
Current Topbox customers include the likes of Orvis, Bed Bath & Beyond and Western Union. With this new round, Topbox expects to build out its go-to-market strategy and continue to develop its product. Currently, the company focuses on a number of verticals where its model works best (retailers, mobile telcos, cable and broadband providers and healthcare companies), and Tranquill tells me this is where it will focus its energy for now. The company will also soon launch a new user interface and bring on more machine learning experts as it looks to provide its users deeper insights into their data.
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