Machine Learning Can Turn Your Sales Team into Closers

A couple years ago, I attended a conference where machine learning was spoken about in connection with two fields: community policing and healthcare. Professionals from both industries discussed how combining massive amounts of known data with AI allowed them to draw correlations they wouldn’t have otherwise expected. Based on the unexpected correlations, doctors and police were able to deploy preemptive measures in patient diagnosis and proactive policing, respectively.

This made me think of all the massive amounts of data we have at our disposal in the B2B sales process—data including customer demographics, customer behavior, sales rep activity, and sales outcomes. How are we using machine learning to draw correlations that we wouldn’t have otherwise drawn? And how are we using those correlations to better inform our sales processes and eventually close sales?

Data and Analytics, Meet Machine Learning

In recent years, data and analytics have provided flint and steel to sales and marketing teams—functional and impressive for starting fires, until you discover the lighter of predictive analytics. Machine learning, meanwhile, is a flamethrower.

While time-tested functionalities like customer segmentation can be useful to sales teams, creating segmentation requires a person to outline specific parameters around segmentation categories, score accounts, create the ideal customer profile, and so on. Similarly, predictive analytics requires analysts to preselect a limited number of variables, and even after that, an analyst can only run so many correlations.

Machine learning does not have the same limitations. Machine learning can literally run thousands of correlations between massive amounts of data. It goes one step further in drawing correlations that sales analysts and sales professionals wouldn’t necessarily identify. While data and analysis answer the questions of “what” and “who,” machine learning answers the questions of “what if” and, more importantly, “what now.”

Improve Sales Rep Efficiency

Machine learning allows sales teams to be more efficient, more effective, and better targeted. Utilizing machine learning can drastically reduce the amount of time that sales reps spend on presales tasks like prospecting and communicating. Not only does machine learning help reps prioritize prospects by scoring categories like contactability and likelihood-to-close, but the functionality can also help teams identify the best communication channels to reach prospects and suggest messages to best communicate with them.

Increasing rep efficiency and using machine learning also allows sales managers to become better coaches. By analyzing phone calls and emails and flagging phrases and terms that led to past closed deals, reps no longer have to use their judgment to determine what works best. Machine learning can even be utilized before hiring reps to determine what type of candidate will likely be a good sales rep down the road.

Machine Learning Is for Closers

With all of these newly discovered correlations and efficiencies—like the best time to call a certain prospect, the appropriate channel to reach them on, and the right things to say—machine learning has set the stage to shorten sales cycles, allowing reps to close more deals. Over the next year, my business intelligence team is focused on testing AI in the following areas that we typically used predictive analytics for:

While I don’t think machine learning will ever replace a human salesperson, I do think we’re in an exciting time for sales and marketing. We have the opportunity to draw correlations that we could have never drawn before, and these connections can better inform the entire sales process, ease a lot of headache for the customer, and ultimately, lead to more closed deals.

Vaughn Aust is executive vice president of marketing and product for MarketStar.

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