Applying Conversational Analysis to the Digital World
Digital body language is the equivalent of nonverbal signals in a dialogue.
Posted Jan 13, 2018
“It was fine.”
Anyone who has ever been a relationship, had children or interacted with other human beings in any context anywhere on the planet, knows the power of the word fine. The very quintessence of linguistic flexibility, fine can be used to describe anything on the scale of human experience — from eating a hot fudge sundae to sticking one’s hand into a box of scorpions.
The trick, as any conversational analyst will tell you, is to listen to what comes before, after, and during the uttering of “fine.” Was it preceded by a pregnant pause? Was it long and drawn out? (Think of a teenager rolling her eyes before slamming the door to her room.) To understand which version of fine they’re hearing, people learn to process and interpret the pronunciation, the intonation and — especially — the context.
Since the 1960s, the science of conversational analysis has evolved to treat every verbal interaction as a continuum with definable points along the way. Conversation, the practitioners of conversational analysis have shown, is linear and finite. The development of each conversational journey can be quantified and analyzed, they say.
But today, the nature of interaction and conversation has fundamentally changed. Anyone who has been offended by a misinterpreted WhatsApp message can tell you that digital interaction can pack an emotional punch that’s just a powerful as an in-person interaction. What it generally lacks is interpretable context.
Or does it?
Digital Interaction Is Conversation
In a previous post, I discussed the emerging science of interpreting digital body language. Digital body language, I explained, is the digital equivalent of nonverbal behavior in the physical world. In fact, digital body language is arguably more interpretable than its real-world counterpart, since it can be objectively quantified and measured. What customers do with their keyboards, mice or fingertips while shopping can be accurately and consistently interpreted, and an individual’s state of mind can be inferred — just as we all do intuitively in our face-to-face interactions.
This offline-online parallel holds true with traditional conversational analysis, too. In face-to-face conversation, speakers automatically or purposefully apply contextualization cues — including voice pitch, intonation, word choice, how the spoken information is structured, and more — to signal the actual meaning behind what they say. This is how we can differentiate one “fine” from another.
Digital interactions with customers as they progress linearly from touchpoint to touchpoint is, in a very real sense, a conversation; it is quantifiable, measurable and highly contextual in meaning. Thus, using the same tools with which we evaluate digital body language, we can examine the context of the interaction, interpret the meaning of what each customer is saying and — more important — react to it effectively.
Digital Conversational Analysis Case Study
I recently worked with a major U.S. online retailer, examining a large segment of customers as they journeyed across multiple touchpoints in the digital funnel.
My team examined multiple digital conversations — linear customer interactions in which messages, both literal and inferred, were exchanged bilaterally. Specifically, we were looking to compare the behavioral patterns of goal-oriented customers with those of people who just came to browse. As these individuals made their intentions clear (via their queries, in conversational terms), we wanted to test their reactions to different types of user experience (via our responses).
The results were dramatic.
Customers who were identified by their digital body language as being instrumental buyers (goal-oriented shoppers) responded far better to an informational page. These folks knew what they were looking for and just needed to get an overview of the options available so they could choose.
Customers who displayed behavior consistent with “hedonic buying” (seeking an emotional experience through shopping) responded better to a page that let them see products in a more holistic, tangible context. These people came seeking fun and sensory stimulation, and a colorful, experiential user experience worked better for them.
In conversational terms, we listened when each type of customer told us what they needed via their digital body language. Not surprisingly, our response (the customer experience) was better received when it was in line with what they were asking for.
Listen to What Your Customers Are Really Saying
When we choose to conceptually see the digital customer journey as a conversation, we realize that the customer experience we’re delivering may indeed be “fine,” based on our market-standard conversion rates. But we also understand that our customers may say “fine” and mean something else entirely (think of the approximately 97 percent of people who don’t convert).
By leveraging the principles of digital body language and closely examining the digital context of each customer journey, we can perceive the differences in the various versions of the word fine. We can learn to listen to what our customers are truly saying and do a better job of meeting their needs.