Integrating machine learning into your sales process via AI-powered sales tools can help you improve rep productivity, make better decisions, and cultivate a data-driven sales culture.
Machine learning’s ability to quickly turn vast amounts of data into relevant insights has various sales applications, including audience segmentation, lead scoring, prospect engagement, sales forecasting, and so much more.
Today you’ll learn seven ways today’s sales teams are leveraging this technology. Hopefully this will help you think of ways it can improve your sales process and boost revenue.
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How does machine learning in sales work?
You can think of machine learning (ML) as teaching a computer to learn from experience. Instead of giving it a set of strict rules to follow, you show it lots of examples.
The computer then figures out patterns on its own. It’s able to make decisions and predictions based on the data it has “studied.”
For example, a sales rep might want to teach a machine learning model to recognize the best time to send cold emails to leads living in NYC.
They’d feed it tons of data around cold emails, the time they were sent, and their respective open rates. Over time, the model would be able to tell the seller the best day and time to send a cold email to someone in NYC. That’s a pretty helpful predictive tool for cold outreach.
Fortunately, you don’t have to actually train a model to unlock the predictive and analytic power of machine learning for your sales team. There are pre-built AI sales tools out there with ML-powered features.
Let’s go over some ways B2B sales teams are using these tools.
7 ways teams use machine learning in sales to engage prospects & customers
From finding patterns in the behavior of your customers to spotting gaps in your sales team’s skill set, machine learning can enhance your B2B sales team’s performance in a variety of ways.
Let’s check out some of the most impactful use-cases of machine learning in sales today.
Better understand your customers
Humans are decent at recognizing patterns.
Give a sales rep 100 cold calls and they may start to notice which openers pique the lead’s interest and which ones result in “not interested.”
But machine learning models are much better at it.
They have the benefit of being able to ingest and analyze an ocean of data — for example, all the cold emails your team has ever sent — in a short amount of time.
Plus, they always apply the same level of attention to each data point, unlike a human who might be too drained to think about why that last call went awry.
This ability to spot patterns in your customers’ behavior enables you to form a deeper understanding of how they think and make purchasing decisions.
To illustrate, an ML-powered analysis tool might identify that customers with specific characteristics respond better to sales emails than LinkedIn messages, allowing for more targeted outreach approaches.
This is why sales tracking software, tools that make it easy to analyze the relationship between your sales actions and the customer’s behavior in order to find ways to improve performance, is so critical.
Reach out to leads at the right time
Aware that decision-makers are busy people, sales teams are using machine learning to help them identify the best times to send their email sequences to leads and prospects.
This simple ML application can boost reply rates dramatically, because an email sent at the wrong time can get lost in the inbox forever.
An example is Mixmax’s AI Smart Send feature, which uses AI to predict the time when your recipient is most likely to respond.
You can see in the image below it recommends certain times to the sender:
AI Smart Send email scheduler
To discover these times, the algorithm studies data on when your prospects usually check their inbox as well as how they interact with your team’s emails.
Over time, as it consumes more data about your outreach, its predictive abilities only improve, and as a result, so do your email conversion rates.
Prioritize your prospects
Accurately estimating a deal’s quality is important for sales time management.
That’s why sales teams often use machine learning tools trained on prospect engagement data to predict each prospect’s likelihood of closing.
Many of these tools, like Mixmax does with its contact engagement scoring, will automatically assign leads scores based on information like how many emails they’ve opened or replied to.
Mixmax Engagement Score
When reps log into their dashboard, they can see which prospects are warmest.
This insight enables them to focus their limited amount of time on the people most likely to buy from them and hit the lower-quality leads if they have free time at the end of the day.
Provide sales reps with data-driven next steps
As a sales rep, sometimes you don’t know what action is most likely to move the deal forward.
Even with a well-defined sales process, there are so many choices.
Should you connect with another stakeholder? Send over some content? Ask for a check-in call to go over their concerns?
Machine learning can use past sales activity data to determine the best next step for engaging the prospect.
For example, Salesforce’s Einstein AI will recommend a step-by-step closing process that’s tailored to your process and prospect:
Or, take Mixmax’s Smart Triggers.
These AI-driven rules and triggers can automatically adjust sales workflows based on recipient behavior and engagement data.
Mixmax Smart Triggers
That way, sales reps always operate according to the deal’s unique circumstances rather than just following a predetermined, static sequence of sales actions.
This leads to more personalized engagement and higher win rates. Not to mention, reps spend less time agonizing over their next move and focus instead on execution.
Write stronger sales emails (faster)
Sales reps are leveraging generative AI to help them quickly generate, personalize, and polish their sales emails.
To illustrate, one of the biggest mistakes sales reps make in cold outreach is writing long-winded emails. No prospect has time for an essay. They want concise, clear messages.
In addition to auto-generating email copy, an AI writing tool like Mixmax’s AI Compose also gives reps the ability to automatically shorten their email prose with the click of a button, as shown below:
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Mixmax AI Compose
Or, they can adjust the tone to make it more friendly or professional. Before AI, this would’ve taken ten minutes at least. Now it’s done in seconds.
In a world where content is king, every sales rep has to be a solid copywriter, capable of writing content for email and social that grabs the attention of their prospects and moves them emotionally.
Large language models like Chat-GPT, and the AI writing tools they support, make it easier for reps to write quality content at a fast pace. As a result, they spend less time crafting and more time shipping those cold emails and LinkedIn posts.
Speaking of generative AI, check out our Chat-GPT prompts for sales. Inside you’ll find prompts to streamline prospecting, speed up account research, and auto-generate cold email first drafts.
Improve sales forecasting
Machine learning is a fundamental technology of many predictive analytics applications, most notably, sales forecasting.
Sales forecasting tools rely on machine learning to analyze large amounts of historical sales data, economic indicators, seasonality, and other factors.
This allows them to accurately predict future sales, revenue, win probability, churn, and other metrics that help you make smarter sales decisions.
And it can do it a whole lot faster and with less errors than the average sales analyst with a spreadsheet.
Take a revenue forecasting tool like Aviso. Powered by AI, it boasts a 98% forecasting accuracy, and you can run forecasting reports from your mobile device in a matter of minutes.
By using machine learning to automate and improve sales forecasting, you can spend more of your time creating data-driven sales plans, which, by the way, you’ll be more likely to follow since there’s no doubt about the predictions that formed its basis.
Optimize sales performance
The best way to get better at something is to practice and get quick and accurate feedback about your weaknesses and strengths. Machine learning makes this possible.
To illustrate, let’s talk about how sales reps use conversational intelligence software to track and analyze their sales calls.
A tool like Gong will actually listen to the calls and, using historical call data from top performers on your team, make suggestions about how you can improve.
For example, Gong might’ve learned that discovery calls at your company perform best when they contain 6-8 questions for the prospect to answer.
Let’s say that on your recent call you only asked five questions. Using natural language processing, the software would notice this shortcoming and suggest that you ask 6-8 questions on your calls from now on.
Immediately, you’re aware of a way to get better results on discovery calls, something you might not have discovered on your own.
How Mixmax can help you improve sales engagement
Keeping prospects engaged requires you to predict their needs and identify the best times to reach out and messages to send.
Harnessing the power of machine learning, Mixmax helps sales teams do all of this. With tools like AI compose and an AI email scheduler, Mixmax will make your prospects feel like you read their minds, when in reality you just listened to the data.
