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October 17, 2022
February 14, 2019
Today, we'll discuss some of the major concepts driving a good customer experience in today's digital world.There are four best practices utilized by low-effort companies:
Our team at Tethr has now taken each of these concepts and explored them in far more depth than we ever could using post-transactional surveys or interviews. We did this by teaching our machine learning platform to “listen” for effort in customer conversations…and then we ran hundreds of millions of minutes through the platform to see how these concepts would show up in real conversations.
The concept advocacy—this is when reps use language that sends the customer the message that you are on the customer’s side of the issue and are going to advocate for them to reach a positive resolution—has the largest impact on reducing customer effort. When reps use this kind of language, it can reduce customer effort by as much as 77 percent.I spent time with one of Tethr's lead speech analytics team members, Amanda Luciano, and one of our data scientists, Jonathan Walker, to get a better sense for how this process unfolds.“Think about how you or I teach Pandora what sort of music we like and don’t like by giving songs a thumbs-up or a thumbs-down. Over time, it will deliver a very accurate result…but it doesn’t start that way," Walker explained.
Teaching a machine to understand a nuanced concept like advocacy takes time. The first step our speech analytics team takes is to just put pen to paper and think about what the concept might sound like. As Luciano explained, “We try to place ourselves in the rep and customers' shoes and think through the way something like advocacy might be expressed.”This sort of brainstorming leads to commonly understood utterances like:
So, the team built a machine learning script that captured these common phrases. There were also some things that were close enough that the team decided to expand the script to include them in the training set. For instance “Let me check what I can do for you” represents the same idea as the original training set so they teach the machine to include these in the future.
Over and over they will test their scripts against larger and larger call sets. Our current advocacy category has been tested against more than 200 million minutes of customer conversations. Along the way, the team encountered many less common (but nonetheless important to include utterances) like:
The latest iteration of our advocacy category encompasses 130 relevant phrases which are captured through 28 discrete machine learning scripts—each phrase itself can appear in multiple different usages, so the machine is capturing thousands of different advocacy-related utterances.With one large insurance company, we found that using advocacy language decreases the likelihood that a customer will recontact the company within seven days by roughly five percent—a huge reduction for an organization that handles millions of customer calls in a given year. And for one home services provider we work with, we found that advocacy had a massive lift on sales conversion: when reps used advocacy language, it increased sales conversion by more than 22 percent.This analysis helped us to prove out that all advocacy language is not created equal. For example, in a sales interaction, it’s much better to demonstrate “declarative advocacy” (“I have the perfect package for you”) but such a confident, declarative approach doesn’t work well in service calls because it sends the customer the message that there’s only one possible course of action. The better approach is for reps to demonstrate “flexible advocacy” (“I have a few ideas for how to fix this…let’s try this one first.”).
One of the things that we often find at Tethr is that negative hits can be as instructive as positive hits when testing a new category. In this case, we found a recurring theme when we audited the negative hits—something that ended up producing an entirely new concept that actually has more bearing in service interactions than even the original concept of advocacy. It’s advocacy’s evil twin: powerless to help.“Powerless to help”—when reps hide behind policy—is the opposite of demonstrating advocacy, and its impact on the customer experience is nothing short of disastrous. The insurance company I discussed earlier (the one which predicted a five percent reduction in the probability of a recontact when advocacy language is used), we found a six percent increase in the probability of recontact when powerless-to-help language is used. For another large insurer, we were able to link up customer call data with completed survey responses and saw that when reps used powerless-to-help language, it resulted in a 27 percent increase in the likelihood the customer would give the call a “high effort” score. And for a credit union we work with, we saw that powerless-to-help language drove a six percent decrease in the probability that a customer would give the credit union a high NPS score.It turns out that in the English language, at least, there are a lot more ways to shirk responsibility than to take responsibility. Specifically, we identified 317 relevant phrases in total which we’ve coded into 26 different machine learning scripts. Some of the common ones you might have guessed yourself:
But we also turned up some less common phrases that show up, like:
Of course, these concepts of advocacy and powerless to help are just two examples of a whole battery of categories that together fall under the heading of effort-reduction language techniques. Other examples include setting expectations, positive and negative language and acknowledgment.Contact us at Tethr for more information about how we’re translating effort-reduction concepts into human language using the power of machine learning.
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