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Sales Has Been Partying Like It’s 1999
Think back to the mid-to-late 1990s.
The status quo for the Internet marketer back then was one of guesswork.
Marketing analytics technology had not yet emerged in a meaningful way as a tool-of-the-trade, so marketers would create and run their online campaigns, and simply hope they worked.
Today, that would be unheard of. Any marketer operating without analytics, measurement, and technology would likely be out of a job soon.
Analytics and marketing technology have transformed the discipline of Internet marketing from mostly art to mostly science.
Marketers have the ability to easily measure what’s working (and what’s not) – continually optimizing every element of their campaigns for higher yield and returns.
In other words, Internet marketing has become one of the most scientific, optimized disciplines in the business world, thanks to technology.
Has the Sales Profession Been a “Mystical Art”?
The sales profession’s life in the 21st century has been a different story, so far.
Sales professionals, managers, and leaders have been operating with the same blindfold that plagued the Internet marketing profession before analytics arrived.
We rely on what we think works in sales.
Every passionate sales professional has their own theories, instincts, intuition, and anecdotal experiences.
But (until recently) there has been no technology, data, or analytics that has been able to measure what is actually generating positive outcomes from sales conversations.
Taking the Guesswork Out of Sales Conversation Effectiveness
Throughout this section of our website (and in other places across the Web), you’ll find the results we discovered from analyzing massive amounts of recorded B2B sales conversations using artificial intelligence, speech-to-text, transcription, and machine learning technology.
The goal is to use data to find out what’s actually leading to key outcomes on sales calls (and what’s not).
We’re searching for the conversational patterns, trends, and insights that are driving the most revenue, highest win-rates, and shortest sales cycles.
We’re also analyzing what impact buyer behavior has on those outcomes.
There are many “holy wars” in the sales world.
That is, sales professionals have widely differing opinions from one another regarding what works (and what doesn’t) during sales conversations.
And some of us are willing to get heated about those opinions!
So, while we at Gong believe sales will never be completely reduced to an exact science where every move is based on prescriptive data, the goal of this research is simply to introduce a level of science to something that has previously been purely an intuitive art form.
In other words, Internet marketing was purely an art form until analytics were introduced. Then it became part art, part science.
We aim to do the same with the B2B sales conversation (and possibly other types of business-level conversations).
Here’s how we go about it…
How We Analyze Sales Conversations, Step-By-Step
We continually analyze (anonymized) sales conversation data from our customer pool using the Gong conversation analytics and AI engine, BI tools, and CRM outcomes.
We’ve done this in several “cohorts,” and will continue in that manner to isolate segmented insights.
For example, in our first cohort, we analyzed 25,537 B2B sales conversations from 17 anonymous customer organizations using the AI engine.
That cohort surfaced five unmistakable trends.
In our second cohort, we analyzed 21,427 calls in a similar way, surfacing three categorized patterns.
Here’s how the analysis works:
- First, we analyze a distinct group – or cohort – of calls from a particular (anonymized) group of customers using our self-learning conversation analytics platform. Sometimes, we may analyze our entire pool of recorded calls. But for the sake of specificity, we mostly segment our analytics
- Each call in the analysis is usually recorded using Gong (or dialers and similar sales tools), speaker-separated, cleaned, and transcribed from speech-to-text
- Next, the calls are mapped to each one’s corresponding CRM record. This gives us the ability to analyze the set of calls against sales outcomes such as win-rates, revenue generated, and how long the sales cycle lasted
- Finally, we run Gong’s artificial intelligence engine through the massive set of call data. Conversation topics, key moments, and seller/buyer behaviors are automatically categorized using sophisticated deep learning algorithms
What We’ve Learned from Analyzing Sales Calls with AI
Following this method, we continue to unearth new discoveries, including
- The talk-to-listen ratio – According to the data, what is the optimal amount of time sales reps should speak vs. listen at each phase of the sales cycle?
- Competitor mentions – When potential customers ask about our competitors during sales conversations, how does that impact sales outcomes? Does that change depending on the phase of the sales cycle?
- Prospect timing signals – Do potential customers exhibit linguistic cues predictive of them either moving forward with the deal, or falling through? If so, what are they?
- Pricing discussion trends – When exactly do the highest performing B2B sales reps reveal pricing? What customer pricing questions indicate a buying signal?
And much, much more.
So now that you know how we go about surfacing these insights, let’s dive into them.
Take a look at the first research article here.
If you liked this article you may also be interested in:
Talk vs Listen Ratio – The Highest Converting Talk-to-Listen Ratio in Sales, Based on 25,537 Sales Calls