SaaS solutions firm Gong last week released the findings of a study on sales phone conversations, including an analysis of gender speaking patterns.
Women delivered slightly longer sales monologues, averaging about 9-12 seconds longer than those their male counterparts gave, but men generally spoke faster, the study found.
Men tended to speak at a faster pace, averaging about 2.88 words per second compared to the average 2.79 seconds for women, the study found. Men also paused longer — averaging about 1.5 seconds compared to women’s 1.3 seconds.
Women had a higher degree of success in progressing deals, with a 54 percent probability of reaching the next milestone, while men registered a 49 percent probability, the data indicated.
Devil in the Details
To carry out the research, Gong used artificial intelligence and machine learning technology that was developed to recognize sales conversation patterns.
The technology is designed to help companies increase sales win rates, decrease the length of the sales cycle and drive more revenue, the company said.
Using AI to examine speech patterns used during a sales call can reduce the guesswork involved in improving sales conversations, according to Gong.
Its cloud-based AI software is designed in particular to improve the performance and productivity of B2B sales teams by tracking details in sales conversations — but this is just part of what Gong can provide to sales teams.
Gong’s AI package combines natural language processing tools with machine learning to analyze, categorize and even quantify sales conservations that can be used to determine what is working — and more importantly, what isn’t.
Always Be Closing
The use of machine learning and AI can catch subtle details, providing benefits that go beyond human critiques.
“You can gather a lots of data from monitoring these calls,” said Greg Sterling, vice president of strategy & insights at the Local Search Association.
“Machines can identify the patterns and trends, and for this reason it can be an important tool to identify what is working and what isn’t working,” he told CRM Buyer.
Of course, the golden rule in sales is one made famous in Glengarry Glen Ross — “ABC,” as in “always be closing.”
“Successful salespeople optimize on closing, and regardless of approach they compensate to ensure that closing rate,” said Rob Enderle, principal analyst at the Enderle Group.
However, the result is an optimized process based on the peculiarities of the individual salesperson, and capturing one aspect of a successful salesperson’s process and applying it to another may not be so easy, he told CRM Buyer.
“The change won’t match their personality,” Enderle pointed out, so “the end result, even after time, may be lower performance than it was before.”
Where this technology could help might be with those just entering the sales game.
Salespeople who apply the deep learning to their own personalities should be able to approach the performance of successful salespeople more closely and far faster, suggested Enderle.
“It is generally far easier to optimize someone that is new and hasn’t developed layers of habits than it is to try to super-optimize someone that has already learned a complex set of behaviors that are now optimal for them,” he added.
As a result, “this data can be used to bring salespeople up quickly and make them very successful — but it could also damage existing successful salespeople, because it is likely to break their already validated process,” Enderle pointed out.
Such technology can benefit sales teams, but it is unlikely to replace the experience or talent of sales professionals — and companies that try to rely too much on the AI’s findings may not see the results they expect.
“The danger is that there can be an over reliance on machine learning to create the right formula for a sales call, which is really as much an art as it is science,” Local Search Association’s Sterling added.
“There are a lot of factors to consider, and a lot of intangibles in sales,” he said. “Machines are just a tool to help, but it is important to note that machines can’t craft the perfect pitch, and it would problematic to think they can.”