The Crystal ball in Sales - Knowing What Customers Want Before They Know It Themselves

By Nick Adams - May 1, 2015

On a showroom or store floor, salespeople have the advantage of observing customers from afar before making their approach. They can assess body language, observe facial expressions, and see how the customer is interacting with the merchandise presented before them. The salesperson may even recognize the customer from a previous visit, then establish a connection with them regarding a past experience, or even pick up the sales process right where it left off. A seasoned sales professional puts all of these “tricks” to good use to understand what customers want and how to seal the deal with them.


In the world of remote customer engagement, most of these nuances are not available to the salesperson on the other end of a phone call, chat session, or even a video chat. However, in most cases, there is a wealth of data that can be captured and analyzed by taking a close look at customers’ most recent actions prior to calling in, and their complete history of previous engagements. The data is out there; the challenge comes in pulling the bits and bytes from a host of disparate resources, such as chat logs, agent notes, transaction history, browser breadcrumbs, email correspondence and even social media channels, and turning that information into strategic knowledge during the collaboration session.

The part about knowing what customers want before they know it themselves really comes to fruition when deploying insightful analytics. As humans, we often forget some of the details about what we have seen or researched, but the analytics engine does not. Powerful analytics platforms, such as the one developed by companies like Humanify, go beyond analyzing the what; they also consider the who and the how, with the ability to predict behavior in real-time. When predictive analytics is used in conjunction with business collaboration systems, customer engagement can become more personalized and efficient. For example, the system can look at transaction history to match the customer to a familiar agent – and if an agent that has worked with the customer before isn’t available, the system analyzes agent profiles to match the customer to an agent with a compatible profile. Sort of like for the contact center.

In addition to personality matching, expertise matching also comes into play. With a predictive collaboration based approach, true expertise matching can be achieved beyond what can be hoped for by a simple IVR button-press; the system will match a specific customer issue with specific agent’s expertise for enabling a significantly more productive engagement that can result in greater customer satisfaction as well as higher first-call resolution rates – whether in sales or support.

The salesperson in a remote access setting may not have the benefit of reading body language or delivering the potential customer a cup of coffee, but with the power of predictive analytics combined with modern in-application communication technologies such as WebRTC, s/he can come pretty close to something even greater: reading the customer’s mind.