Very frequently, we’re asked about how to best use conversational analytics data (“is there an ideal number of questions to ask on a call?”)

The reality is that conversational styles of reps are too diverse for conversational metrics to be used in absolute terms.

However, they can be extremely effective in looking for detecting directional changes in your sales motion over time.

Here is a live case study of the conversational metrics of our CEO when he was working on taking a new product to market.

Background

In April 2020, our CEO took a small group of engineers inside the company to start work on our Sales Process Optimization platform.  This was a brand new product — not something that would be a natural upsell into our existing customer base — so it was decided that he would lead sales on the product to accelerate the feedback loop into product/engineering.

The data below covers his conversational analytics and deal progression metrics, all generated by Truly’s platform and all pulled directly from Salesforce reporting.

 

Talk Time % By Rep

The chart above shows how our CEO’s % talktime on sales calls changed over the past year.  It suggests that there were three distinct phases/shifts in his selling motion during that time period.  Here’s how he described these shifts:

  • Period 1 (Pitching): here we see that % talktime on the call gradually increase from 40% to 60%.  At the start of the period, he is still trying to figure out how to best position the product and so he really focuses on getting the customer to talk as much as possible for learning.  As time progresses, he starts to develop some pitch collateral to guide the discussion more, so his talktime organically starts to go up in each call.

  • Period 2 (Deep Discovery): in this period, % talktime collapses dramatically.  This is because in Period 1, the team had found that the offering wasn’t converting at the level that they wanted to see, so they made a number of changes to the product and offer.  In Period 2, the CEO goes back into heavy discovery mode and tries to ask open ended questions that get the prospect talking as much as possible to maximize learning.  This suppresses the CEO’s talktime.

  • Period 3 (Targeted Pitching): it turns out the new product mix is working far better than before, so the CEO goes back to the regular pitching motion.  Talktime % skyrockets, going above the levels in Period 1.  Why? When we asked our CEO this question, he guessed it was because there was there was more clarity in the offer being sent out in SDR Emails.  This meant customers were coming to the meeting with more context and more targeted questions, which led him to naturally spend more time talking about the solution instead of having to probe with deep discovery.

But wait a minute… can we prove the CEO’s hypothesis?  Is there a way to see how the customer’s behavior is changing on calls?  We’re glad you asked!

 

# Questions Asked By Customer

Conversational Analytics

As you can see, right when the selling motion changed in period 3 , the # questions per customer skyrocketed by almost 5X, which supports his first claim.

But our CEO has a second claim, which is that his pitch is much more targeted than it was before.  How do we know that this is true and he’s not just information dumping on the customer this whole time?

Fortunately, there’s another graph for that.

 

#Questions Asked Per Rep/Customer By Month

As you can see, our CEO is asking a Whopping 6X more questions on each call than before, which supports his claim that his pitches are far more targeted.

Now, you may be wondering…

How is it that there are 6X as many questions being asked, yet talktime is at an all-time high?

To answer this, we went to the call recordings themselves to see how the anatomy of the conversation changed between Periods 1, 2 and 3.  Here’s what we found:

  • Our CEO has a better understanding of what the value prop of the product is and how to sell it.

  • As a result, he is asking much more targeted questions at the start of each call (eg: instead of asking ‘what does your sales stack look like?’, they are asking ‘how do you track emails into Salesforce?’)

  • This is allowing them to customize the pitch very early on in the conversation, and then launch into visual examples of different capabilities.

     

So, what’s the impact?

Analytics are cool, but unless we can tie it to a concrete outcome, they’re worthless.  A key measure we look at here at Truly (and a metric in our AppExchange package) is Pipeline Momentum – the # of stages deals progress forward or backward within a given timeframe.

The chart below seems to support the CEO’s story.  In Period 1, we see deals progressing at a specific level.  In Period 2, our pipeline momentum collapses as we change focus from selling to discovery.

And most importantly, in Period 3, we see a lift of 10% in our Pipeline Momentum

Conclusion

As the makers of this software, we originally thought that conversational analytics would be a vanity metric because there is simply so much variability in the conversational styles of different people.  However, when controlling for a single sales rep, we see that it is an incredibly powerful tool for directionally understanding whether there is a shift in a rep’s execution cadence.