We Analyzed 25,537 Sales Calls. Here’s What We Learned.
“We need to figure out the ROI justification and which teams we’re going to roll this out to,” my prospect said with confidence. “Once we do that, I can’t think of anything stopping us from moving forward.”
Despite that seemingly strong buying signal, my heart sank.
I had just started my new position at Gong and I was asked to meet on-site with this potential customer to move them closer to signing a deal.
So why did my heart fall into my stomach instead of race with excitement upon hearing that?
Any other sales professional would have been excited to hear that phrase uttered from their prospect’s lips. It seemed like such a sure thing, on the surface.
But I had a piece of knowledge most sales leaders and professionals have never had…until now.
Two Counterintuitive Sales Forecasting Insights Revealed by Artificial Intelligence
By the end of this short post you’ll understand why the above story makes sense, despite its counterintuitive first impression.
You’ll learn why that above so-called “buying signal” actually has a negative correlation with win-rates and forecast accuracy.
You’ll also discover the other side of the same coin: how a single cautious, indecisive word uttered by your prospect can increase (yes, I said increase) forecast accuracy by 73%.
Let me explain.
Bringing Science to the B2B Sales Conversation
For the sake of context, we at Gong have built an automatic sales conference call recording platform with conversation analytics running on the back end using transcription, AI, and machine learning technologies.
We used our own platform to analyze 25,537 anonymized B2B sales calls from 17 customer organizations in search of data-driven sales conversation insights.
For the curious, here’s a quick summary of how we gathered this data:
- As mentioned, we analyzed 25,537 B2B sales calls from 17 customers. These customers were mainly mid-market, high growth SaaS companies. All of these calls were account executive conference calls conducted on platforms like GoToMeeting, Zoom, Webex, etc., rather than SDR or prospecting calls
- Each call recording was mapped to its corresponding CRM record. This allowed us to analyze calls against outcomes like win-rates, forecast accuracy, sales cycle length, and revenue produced
- As the calls were recorded, they were also speaker-separated, cleaned, and transcribed from speech-to-text
- Finally, we used Gong’s conversation analytics capabilities to analyze the calls and transcripts, auto-categorizing events within each call such as key moments, topics discussed, and seller/buyer behaviors
Among a handful of other insights, here are the two counterintuitive trends we discovered in terms of forecast accuracy signals.
Beware the Phrase “We need to figure out __________________”
Think back to the story I told at the beginning.
Remember when my “heart sank” despite what seemed like a strong buying signal?
Every B2B sales professional worth his or her salt will eventually ask the “timing question.”
“When do you estimate moving forward with this project?”
“What does your timeline look like for purchase?”
“How soon do you foresee getting this agreement executed?”
It turns out when your prospect responds to your timeline question with some variation of “We need to figure out ________________,” there is an unmistakable negative correlation of getting that deal closed within your estimated forecast.
Your odds of closing that deal on time drop significantly when your prospect utters those words (or some variation of them).
Counterintuitive, but true.
Now you understand why my heart sank in the story at the beginning of this post
“Probably” Justifies “Happy Ears”
On the other side of the same coin, we also discovered a response to the timeline question that has a strong positive forecasting correlation.
The word “probably.”
In other words, when you as a sales professional ask for timeline and the prospect cautiously responds with the word “probably __________________________,” you have a much higher likelihood of closing the deal within the estimated forecast.
Again, counterintuitive but true.
After all, the word “probably” isn’t exactly strong, decisive, confident language.
Only in hindsight does it make sense: the prospect is likely responding cautiously because of how seriously they are considering the purchase.
They don’t want to get backed into a corner by a pushy sales rep with happy ears, so they verbally distance themselves.
A Sales Conversation Insight Generation Machine
We’ve only revealed the tip of the iceberg.
25,537 analyzed sales calls reveals many more insights than just the two we covered here, as I’m sure you can imagine.
There are many more AI-driven sales conversation revelations we discovered such as:
- “Talk-to-listen” ratio trends against win-rates
- How often pricing is discussed in winning sales conversations
- The exact time window top performing AEs discuss pricing
- A specific type of language that increases sales win-rates 32% on average (hint: it’s not “assumptive language”
Go here to get the report covering the full range of insights we discovered in this analysis.
Keep in mind: there are many more to come over the next months and quarters. Be sure to keep tabs on us by visiting Gong.io. And if you’re a B2B sales leader with an account executive team of at least 10 and you’re interested in seeing what Gong is all about, go here to request a Gong demo.
“Data is the new oil,” has become somewhat of a trope in the tech community: a quippy statement to illustrate the vast amount of data in the universe…
Sales recruiting is broken, but there is hope in finding your perfect VP of Sales. Just follow this guide.
In just five years, she’s helped grow Stripe’s sales team to about 200 folks in the U.S. and 500 globally—that’s bigger than the entire company was when she first came on board.