The Role of AI and Machine Learning in Sales in 2024

Sales professionals are constantly expanding their arsenal of sales software as new technologies come onto the scene. Over the years we’ve adapted countless new platforms to make our jobs easier and help our businesses maintain their competitive edge.

Platforms like sales CRM systems, door-to-door sales software, sales force automation, and virtual selling, and mobile sales apps are mainstream now—as we continue shifting our sales strategies to meet the demands of a digital marketplace. But until recently, technology was only good for performing physical or computing tasks. Humans still did all the thinking. Now, artificial intelligence has changed all that, and its benefits are spreading across industries.

Naturally, AI for sales and marketing is changing the way we sell things. We even wrote a whole, free ebook about it.

In this article, we’ll discuss the different roles of AI for sales reps, and explore its current capabilities and where it's headed. We’ll also provide some tips for using AI for sales efficiency.

Let’s start with the basics. Artificial Intelligence—AI—is computerized technology designed to perform cognitive tasks as well as (or even better than) their human counterparts. Basically, AI technology is designed to make tasks easier by delegating some of the thinking to computers.

Turns out computers are pretty good at it. AI is currently being used for everything from environmental conservation to education to home appliances—and, of course, sales.

What is the role of AI in sales?

For profit-driven businesses, artificial intelligence and sales means greater ability to process mountains of eye-opening data in real-time, and then use that data to gain deeper insights into customer behavior and buying habits. It also means less reliance on human personnel, which can be hard to retain in a competitive job market.

Some thought processes are still better left for human brains, such as reading body language, interpreting tone of voice, and navigating complex decision-making. But there are certain things that technology can process much faster and more accurately—like purchasing history, social media and email engagement, website visits, market trends, and more.

The role of AI in sales is to use data analysis algorithms to handle the cognitive work that takes too long or is too data-heavy for people to handle on their own. Computers are masters at spotting patterns hidden in large volumes of data, so it makes sense to delegate some of the analytical thinking to software.

But some sales teams are still hesitant to adapt AI—and that hesitancy could come back to bite them later down the road.

It’s no secret that computers are better at automatically organizing and processing large amounts of information. But this isn’t the only way technology makes sales work easier. Artificial intelligence has advanced to the point where it can also recognize where change is needed and initiate those changes without human intervention. The ability for AI technology to improve on its own over time is called machine learning.

Machine learning in sales: an example

Traditionally, automated sales technology operated by performing its duties based on the rules set for them by humans. For instance, you could set an automation rule to send a personalized welcome email to every lead who fills in one of your web forms. This hands-free approach saves time and ensures that there’s no lag in engagement with a potential buyer.

With machine learning, however, the benefit of sales automation is pushed even further. JPMorgan used AI machine learning as a marketing tool to improve their email outreach efforts. Without human intervention, the AI technology analyzed the results from their email campaigns and then used that data to create new email copy that would get even more click-through engagement.

At its peak, they saw a 450% increase in email click-through rates. Not only did their technology automatically send those emails, machine learning allowed them to analyze the feedback and improve its output to get even better results.

Different data types require different kinds of AI tools. Here are three types of AI that sales teams are currently using across industries. Perhaps your organization has already started working with a program that uses one of these AI technologies.

1. Natural language processing. NLP is designed to understand and respond to human speech—written and verbal— in a way that sounds human. Using computational linguistics, NLP not only strives to understand the language being used, but to interpret the intentions and sentiment behind the words. You’ve probably interacted with an NLP tool, as this AI technology is already being used to innovate digital assistants, speech-to-text dictation programs, and customer service chatbots.
2. AI analytics. This AI tool takes a deep dive into your raw data to look for and interpret patterns. AI Analytics tools can detect anomalies and alert you to them in real-time, saving data analysts hours upon hours of work. Businesses typically handle a lot of data and use it for different purposes, so we’ll do a closer look at the various kinds of AI Analytics in the next section.
3. Smart process automation. SPA is robotic automation plus machine learning. During an automated process, SPA recognizes when it needs human intervention in order to take the next step forward. It loops its human counterparts into the process, then uses those human-made decisions to predict solutions for similar circumstances in the future. As you’ve noticed, SPA isn’t a completely human-free tool—but it still counts as AI because its machine learning properties make it more efficient over time.

AI tools come in all varieties, serving their own unique function for streamlining the sales process. Depending on your business needs and goals, you’ll require a specific kind of AI system to help you automate portions of your sales process in a way that’s cost-effective—without damaging your customer relationships.

Conversational AI

Conversational AI for sales uses NLP to receive and analyze input from customers through a text or voice interface. Then, they deliver a natural and instant response. Basically, conversational AI for sales is any program that lets customers interact with your company in a way that feels human—even when half of the conversation is being handled by a computer program.

These tools—unlike people—are available 24/7 to keep leads and customers engaged. They also don’t get frustrated or tired from having to interact with needy or pushy contacts.

Using NLP and machine learning, conversational AI tools handle many of the tasks that would traditionally require the human capacity to receive and interpret information based on text or speech. These tasks include but aren’t limited to

• Nurturing leads by suggesting products
• Fielding commonly asked questions
• Onboarding new customers
• Reminding customers of items left in their carts
• Tracking shipments and providing up-to-the-moment delivery updates
• Perform lead scoring based on customer interactions over messaging and phone calls

Conversational AI for sales teams means that customer care and engagement don’t have to come to a grinding halt the moment your team goes home for the day. Customers can reach out and engage whenever it suits them best, while still getting the answers they need to nurture them further through the funnel. Plus with multiple language options, you can offer immediate sales assistance to a wider audience.

Conversational AI for sales examples:

• Chatbots. Many businesses use chatbots to assist website visitors and answer the most commonly asked questions, saving sales reps from having to answer the same questions over and over again. Plus, chatbots can point visitors to relevant published content, increasing engagement and nurturing leads along your funnel. You can also use chatbots to generate leads and schedule sales meetings.
• Voice assistants. If you optimize your company website for voice search, conversational AI for sales and marketing can include voice assistants like Alexa (Amazon), Siri (Apple), and Google Assistant can bring leads directly to you when they pick up on keywords that are relevant to your product or service. Integrated voice assistant technology is also useful for setting reminders and performing quick contact searches.

Predictive sales AI

Businesses use AI analytics tools for predicting future sales with greater accuracy. Right now, forecasts are often based on gut instinct or incomplete data—both of which pose a pretty hefty risk. But predictive AI for sales uses the power of algorithms to analyze mountains of information about buying signals and historical sales numbers. Then, predictive sales AI uses this information to build models that help you make better informed plans for future investments and supply demands.

Artificial intelligence for sales leads

AI-powered lead generation cuts down on the time it takes to find the sales leads who matter most. Using what you already know about your ideal customers, AI lead gen tools help you weed out the time-wasters and put a spotlight on the highest value prospects.

AI lead generation instantly sifts through key data points about potential leads, including industry, job titles, demographics, networks, and market trends. Then, it shows you the leads who are most likely to buy, increasing your chances of conversion. Along the way, it also gathers and analyzes your customer data so it constantly improves the results it puts in front of you.

Selling is a cycle that involves passing potential customers from marketing to sales. And the handoff between the two is a gray area that looks different in every business.

The goal of using AI is the same in sales as it is in marketing—to reduce the manpower hours needed to get the job done without sacrificing the personalized touch that customers appreciate. That’s a pretty big task, but AI is currently doing just that in sales and marketing departments across industries.

AI and marketing

The goal of marketers isn’t only to make people aware of their product, it’s to provide an entire content-driven experience that

• Establishes brand credibility to potential customers.
• Provides additional value and builds trust.
• Educates potential customers about the product or service.

There’s a lot of content that can fall under those three umbrellas, which can add up to a lot of data for analyzing. AI helps marketers measure the success of their campaigns by analyzing data like email open and click-through rates, and then suggesting and implementing tactics for better approaches. AI in marketing is all about recognizing patterns and gaining more engagement by appealing to trends in real-time.

Artificial intelligence in sales

On the sales side, AI is all about speeding up the sales cycle and sales tracking and making room for more productive interactions. Contrary to what some people think, Artificial Intelligence isn’t replacing human salespeople anytime soon. Many sales processes still require a human element to seal the deal—and that human element will perform much better when it’s freed from the repetitive administrative tasks that AI can take on.

By handing the more data-driven tasks over to AI components, human salespeople have more time and energy to develop and reap the rewards of their individual selling skills and techniques.

So far we’ve covered the basics of AI for sales. But as technology keeps advancing, businesses will only find even more uses for artificial intelligence. Here are some of the other ways businesses are currently using AI to cut down on repetitive tasks and make their workdays more productive.

Monitoring sales calls

Conversation AI technology acts as another ear listening to sales calls. It can produce real-time transcripts for easy data entry, and monitor details that salespeople don’t have the bandwidth to process in real time. The data gathered from these interactions is also useful for creating coaching materials for training new salespeople.

Example of using AI for sales calls

Company A wants to know more about their direct competitor, Company B. Apparently, sales reps have reported that customers are mentioning Company B in their sales calls, citing that their offering is somehow superior to Company A’s. Naturally, Company A wants to know what’s up.

Company A uses conversation AI to monitor sales calls between customers and sales reps, programming the system to recognize Company B’s name and information. Whenever a lead mentions the competitor’s name or related keywords, the conversation AI technology recognizes and stores those revealing moments of the discussion, which Company A examines later to learn more about Company B.

Imagine trying to do this same bit of research without artificial intelligence. If you were a sales manager at Company A, would you ask your sales team to take physical notes of every sales call and write down every detail their customers say about Company B? Or would you record every sales call and then have someone listen to those hours of conversation, or dig through pages of transcripts for mentions of Company B?

You could. But you can already feel the headache, can’t you?

That’s the beauty of artificial intelligence—computers don't get headaches, no matter how tedious the work is.

Predicting future sales

AI allows businesses to process enormous amounts of information in seconds, including up-to-the-minute trends and past sales data. It’s like sending a bloodhound out to sniff through all of your data—new and old—to locate details that would take a person days to find. Then, like a detective, it pieces its findings together to predict how well you’ll perform in the future.

By analyzing past outcomes and the activities that lead up to them, AI for sales forecasting lets companies make more data-driven decisions, saving them from relying on gut instinct alone. Plus, it frees sales reps from the tedious tasks themselves, leaving more room in their schedules for building valuable customer relationships.

AI for sales prospecting

Prospecting for leads can be an enormous time drain, which is why AI prospecting is such an attractive idea. Artificial intelligence reads behavioral and purchasing patterns to help salespeople identify the best potential buyers without having to sift through mounds of data themselves. That's what sales prospecting tools are all about.

AI for sales prospecting is all about finding the patterns in customer behavior, and then shining a light on the prospects with the most potential. This gives sales reps more time to spend on actually making the sales, and also lets businesses expand their pool of potential buyers.

The technology behind artificial intelligence is only going to get more impressive as time goes on. For now, here are some of the cutting edge ways that AI is revolutionizing sales and marketing processes in business:

• Sentiment analysis is AI that can actually detect human emotion based on language use and other clues. NLP and machine learning allow sentiment analysis software to monitor areas where customers are talking about you, such as on social media posts, public forums, in market research findings, and in customer service interactions. By assigning a positive, negative, or neutral tag to what people are saying about your company, you can collect a sector of data that reveals areas where you could improve services. Think of it like a spy who can listen in on every public conversation about your business, categorize those discussions as good, bad, and neutral, and then hand all of that information to your doorstep.
• Recommendation AI systems—like the ones that suggest new movies or shows on your streaming service—are getting even better at guessing what we’ll want next. These are incredible AI tools that never stop learning what makes customers tick. They also help you put those recommendations on customers’ radar in a way that will resonate with them. The best social selling software uses recommendation to say, “See? I know what you like!” You can build up brand loyalty simply by having a computer that has the time and bandwidth to be obsessed with knowing what your customers enjoy—and are willing to pay for.
• Lead scoring is a tricky process that is made much easier by using AI. Rather than basing its scoring criteria off generic models, it closely examines and learns from the criteria that’s specific to your unique business model and sales process. Then, as time goes on, it only gets better at determining which leads are most worth pursuing, and which ones will require more effort.