Home 3 Ways Deep Learning Can Help Your Sales Team Thrive

3 Ways Deep Learning Can Help Your Sales Team Thrive

Every salesperson wants an edge. Yet many are afraid of being edged out by AI, which makes them hesitant to embrace emerging tech. This is a big mistake, especially for high performers and superstar closers.

Machine learning, specifically deep learning, fills in gaps that human intuition never could. Put to use across a team of eager sales pros, its innate advantages add a layer of intelligence to any crew’s knowledge base.

But the only way to make deep learning an invisible member of your sales department is to get over the fear of a would-be takeover by robots.

AI as an Ally

Instead of viewing AI-related processes with disdain or distrust, salespeople should open their minds to how much of a game changer advancements like deep learning can be. Instead of replacing team members, deep learning frees up reps to focus on the tasks best suited to the human touch. As a tool, deep learning provides insights by spotting and naming patterns in millions of unstructured data points. Even the most brilliant salesperson could never achieve that kind of foresight.

An MIT-published study found that more than three-quarters of companies surveyed have adopted machine learning to propel their sales growth. Such progressive companies rely on deep learning to sift through rafts of data to uncover everything from the right time to contact a prospect to signals about which leads are most qualified. This level of discernment would have been impossible just a few years ago. Now, it’s not only achievable — it’s available to companies of all sizes.

AI in Action

Without a doubt, AI-driven deep learning can help your sales reps scale up operations and realize their true potential. Start your journey into deep learning by leveraging technology to accomplish three goals:

1. Prioritize your incoming prospects for more reliable lead qualification.

How much time do your salespeople spend chasing poorly qualified leads? Deep learning bridges gaps in your sales pipeline by determining who is most likely to convert to the next stage in the sales funnel. Prior to AI, marketing teams could only predict which leads were hottest based on a few factors. Today, deep learning programs can not only score leads more effectively, but they can continuously self-educate to enhance those predictions through constant data aggregation and analysis. As McKinsey research shows, the fastest-growing sales organizations are those that depend on such sophisticated analytics.

Excited at the prospect but worried about getting deep learning off the ground? There are plenty of systems on the market that can handle this type of deep learning, relieving you of the burden of creating one from the ground up. Kurt Schlimme, senior director and sales operations PMO at BroadSoft, says his team has been using Clari, a platform that uses automation and AI to increase a manager’s visibility into a salesperson’s pipeline. This changes the conversations between sales reps and team leaders from “Where are you with this lead?” to “How can I help you close the sale?”

2. Improve forecasting and customer management.

Give your salespeople a crystal ball — that is, the ability to intelligently forecast what will happen down the road by applying deep learning. Because deep learning “can make use of unstructured data across a variety of sources,” explains Greg McBeth, head of revenue at Node.io, “sales leaders can not only identify a good-fit potential customer, but also predict the possible deal size, deal cycles, and other insights.”

In addition to predicting prospects’ future moves, deep learning can offer suggestions for smarter management of current customers based on historic account details. Understanding how best to nurture a client reduces the risk of attrition and boosts the likelihood of upsells, cross-sells, and other revenue-producing opportunities. By removing the fallibility of human hunches, deep learning’s constantly evolving neural networks allow for more dependable sales outcomes.

3. Discover opportunities for personalized selling.

Although clients may not react logically in all situations, they do react predictably if you drill down far enough into their past behaviors. Consequently, the secret to anticipating customer needs and moves is to capitalize on deep learning insights. Salesforce research tells us that 62 percent of outstanding sellers expect to use AI-guided selling themselves. They also anticipate that their competition will embrace deep learning for similar reasons, such as closing more deals and keeping client engagement high.

Allowing a computer program to prioritize opportunities and make recommendations enables salespeople to act more and ponder less. For instance, Crystal uses AI to provide sales teams with personality profiles of everyone they meet. Then, Crystal leverages that data to supply email templates based on each lead’s personality. A tool like this can help your sales reps personalize their communication with customers while saving them time, as they’re no longer starting each email from square one. It also takes away much of the guesswork that comes with sales, improving confidence across the board.

Does the idea of increasing your sales team’s efficiency sound appealing? It’s highly doable. Even your team’s technophobes can learn to embrace deep learning. And once they realize its benefits, they’ll wonder how they ever got along without this high-impact edge.

About ReadWrite’s Editorial Process

The ReadWrite Editorial policy involves closely monitoring the tech industry for major developments, new product launches, AI breakthroughs, video game releases and other newsworthy events. Editors assign relevant stories to staff writers or freelance contributors with expertise in each particular topic area. Before publication, articles go through a rigorous round of editing for accuracy, clarity, and to ensure adherence to ReadWrite's style guidelines.

Brad Anderson
Former editor

Brad is the former editor who oversaw contributed content at ReadWrite.com. He previously worked as an editor at PayPal and Crunchbase.

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