What is Artificial Intelligence (AI) for Sales?

Using automatic technology to make the sales process easier and faster is what artificial intelligence (AI) for sales means. Several types of sales systems that use AI are available, such as:

• Chatbots are computer programs that can engage in conversation with customers and prospects, providing personalized answers to their questions and helping them through sales.

• Sales automation: systems that automatically take care of customer relationships and lead generation, like setting up appointments and suggesting goods and services based on customer’s preferences.

• Natural language processing (NLP): AI models that can understand and interpret human language, allowing customers to ask questions in natural language without typing commands.

• Predictive analytics is software that looks at customer data to find people likely to buy and guess future sales trends.
• Automated lead scoring: tools that determine how likely a prospect will become a customer based on information about their past actions and background.
• Marketing automation: AI models that help marketers run campaigns automatically and give customers content tailored to their needs.

Machine learning algorithms run most sales technology today. These algorithms let the software learn from sales data and make more accurate predictions.

Salesforce, a popular CRM system, was one of the first well-known ways that AI was used in sales. The company already had millions of users worldwide, making it the perfect candidate to train its software based on what those users said.

Early versions of Salesforce helped users by giving them helpful information, guessing how leads would act, suggesting the best next steps, and automating boring jobs like adding notes to the CRM.

Since the business started working on AI technology, it has helped pave the way for digital sales to become the norm.

Like words

• Conversational AI for sales: AI technology enables human-like conversations between businesses and customers using natural language processing.
• Generative AI for sales: the use of machine learning algorithms to generate personalized recommendations and insights based on historical data.
• Sales virtual assistant: a virtual assistant powered by AI technology that provides customer service, processes orders, and answers common questions.
• Virtual sales assistant: an alternative term for “sales virtual assistant.”

Why AI is good for sales

The use of AI in sales has grown by 76% between 2018 and 2022. Top-performing sales teams are 2.8 times more likely to use a sales stack that includes AI.

AI can help with sales in several ways, such as:

Correct attribution of sales

There is a lot of disagreement between the sales and marketing teams about which organization’s connections made a sale happen more.

It’s pretty simple to figure out who is responsible in smaller businesses. But as the sales cycle lasts, it gets harder to pinpoint a single cause for poor sales success.

A study from Rain Sales Training found that it takes sales reps eight interactions on average to get meetings (or other types of conversions). There are some B2B sales methods where it can take up to 20 touches to close a deal.

Giving credit where credit is due, machine learning models learn to better look at how each touchpoint affects the whole. Also, and this is more important, buyers know which sales techniques help them close a deal.

Lower rates of leaving

A high loss rate makes it hard for businesses to grow in a way that lasts, and sales reps don’t always have the information they need to spot customers who are about to leave.

AI-powered customer success models look at past customer behavior to determine which customers are likely to repeat their subscriptions or contracts and which ones need more attention. They do this by looking at how responsive, engaged, and consistent past customers who left were.

In the same way, customer segmentation models driven by AI help sales and marketing teams find patterns in how customers buy things that show they might leave.

SDRs can use this information to contact customers about leaving and offer discounts or other benefits to keep them.

More personalization and interaction with customers

Buyers want relationships that are tailored to them. Personalization isn’t just about the product for B2B sellers, which is to say, most salespeople. It’s also about how they treat the customer.

Only 22% of B2B buyers say that sellers are meeting their need for unique experiences, even though 73% of B2B buyers say they want the same things that B2C buyers get.

When SDRs use AI, they can get helpful information that helps them connect leads better. They can use this information about how the lead uses their website, their current solutions, and their digital interactions in the past to make content suggestions more relevant to their needs and tastes.

Before AI, sellers had to go through emails quickly, direct messages on social media, and CRM notes in their CRMs to prepare for their product demos and first calls.

When AI handles the data, all of these different sources of information come together to form a single source of truth.

Sales reps are more organized and get more done with a complete picture of their customers.

Help with sales calls in real-time.

As sales reps talk to prospects and show them products, AI gives them real-time feedback. In conversations, it notices things like how fast they talk, the tone of their voice, and the expressions on their faces. It then tells them how to change their approach, which helps salespeople get to know prospects better and build relationships with them.

It also helps sellers figure out how potential customers feel and what they say with their bodies. It also helps them focus on the most critical parts of the talk to better understand what the customer wants.

AI can also quickly write down records of sales calls. Salespeople can use these to learn and improve, and sales managers can use them to see how well their sales team is doing.

Accurate reports and predictions of sales

When sales managers make predictions, they must share them with the top leaders and use solid data to see if their sales team is on track. With just the click of a button, software that uses deep learning models to learn from past sales and customer data can make correct predictions and reports.

AI also makes regular internal reports automatically, so managers don’t have to put together charts by hand every week or month to check on how their teams are doing.

This way, AI can save reps and managers time that they would have spent manually putting together reports and making sales predictions while still making sure that its predictions are correct.

The executives and investors can make better choices about the company’s future as these projections move up the company’s hierarchy.

More time to work on sales tasks

Less than a third of the average rep’s time is spent on sales tasks, which clearly indicates why 79% of sales team members say they are disengaged.

Salesforce says that the three most common things people do to waste time are:

• Dealing with emails
• Writing down what you do
• Putting in sales information and notes

When AI drives sales processes, these jobs are pretty much eliminated.

Activities like keeping track of the sales flow, talking to customers, and following up can be done automatically. CRM automatically makes notes and stores them. Email autoresponders can handle the first contact from prospects, giving sales reps more time to work on more important chores.

Better scoring of leads

People who sell things want to talk to more customers, but not those who will waste their time. This is because poor sales waste time and energy that could be used to sell. They are just as bad (or worse) than having no leads.

When AI determines how likely a lead will convert, it automatically looks at many things that a person wouldn’t immediately notice. These include how they behave on particular website pages (like how long they scroll), their company size and industry, the number of times they visit your website, and other data points.

With these new insights, reps don’t have to think too hard about whether to follow up on each lead, which makes the lead score more accurate.

Faster ramp time and better sales training

Faster ramp time lets sales reps start making sales more quickly. Training for buyers is more than just a one-week program with shadowing to get them started in sales. For it to work, ongoing, personalized teaching for each representative must be ongoing.

But 26% of sellers say their sales training doesn’t work, and 84% of reps forget what they learned in just three months.

This hurts sales output because reps may encounter more problems during the sales process if they don’t learn or remember enough from coaching and training.

Salespeople can get better faster with the help of guided selling, lead scoring, real-time sales feedback, and AI-powered insights. These tools also show managers where their reps need more help.

Tips on How to Use AI in Sales

Sales operations are tasks and actions that help a sales team do its job. AI can help make things run more smoothly, reduce manual work, and give you helpful information to help you make better choices.

Among the sales operations tasks that AI drives are:

Trying to guess the sales

Previously, sales reports and predictions were primarily based on gut feelings. Most sales data is multivariate and stored in separate systems (e.g., CRM, marketing automation, e-commerce platform), so it was hard to predict how sales would do in the future.

AI centralizes this data in sales, which helps companies make accurate sales forecasts based on buyer behavior, market trends, historical data, and many other factors that a person could not consider.

Getting leads and prospecting

Even though AI robots and other conversational AI tools can’t generate all leads independently, they free salespeople to focus on more critical tasks, like answering frequently asked questions and engaging with leads on the first interaction.

Instead, people who use chatbots can use AI to write scripts that get better over time without any help from the user, just like a new employee.

There are specific signals that chatbots can pick up that mean they need to pass the talk on to a salesperson. The conversation log can be kept up-to-date automatically, so the person who takes over can see the whole chat history.

This can help find new leads and speed up the sales process. The chat logs can also teach robots how to be more valuable and efficient in the future.

Scores for Leads

AI models look at a lot of different kinds of data, such as:

• Demographics
• Financials
• Company industry, size, structure, and maturity
• Web visits
• Interactions with sales reps or chatbots

These tell sales reps how to get in touch with a lead and whether or not to promote it.

AI can also better identify leads than a person could by looking at customer reviews, emails, and other data. This saves time because sales reps don’t have to give scores by hand or deal with leads that aren’t approved.

Making sales possible

AI helps with sales enablement by giving sellers real-time feedback and picking out the best content so they can quickly reply to reps with targeted messages, helpful articles, and relevant information.

Let’s say a sales rep from a company that makes marketing management software is on the first call with a qualified e-commerce prospect to learn more about them.

AI watches what each person does on camera and listens to the whole talk. This information sends a message to the seller telling them in real time how to change their pitch, get people interested, or ask better questions.

During the call, the prospect said that “emailing too often” is their most significant problem with their present marketing efforts.

AI sees this immediately as a chance for the seller to focus on during their pitch. It tells the prospect about parts of the product that directly solve their problem and sends them an article from the company blog about “how to find the perfect email cadence.”

Optimization of Prices

Price optimization means finding the “sweet spot” between the price that makes you the most money and the price your customers think is fair.

Without an algorithmic method, optimizing prices is a lot of guesswork—a product has to go on sale at a specific price, which has to be changed over time as market conditions change.

AI learns from past data to guess how the market will react to changes and how they feel about the value of a product. This takes away some of the guessing.

Cross-selling and up-selling

It’s like Amazon’s recommendation algorithm regarding sales, but the seller is between the customer and the goods.

AI is used by some sales tools, like CPQ software, some CRMs, and sales intelligence platforms, to find ways that reps can give their current and future customers more value.

Reps can use AI-generated suggestions to help customers find related goods and services that may be useful based on where the customer is in the buyer journey.

Making things unique

Personalizing material has been a top priority for sales and marketing teams, but doing it on a large scale is challenging.

There are, however, clear benefits to a personalization plan that is carried out well. The Adobe Personalization at Scale study shows that 66% of the time, global decision-makers in charge of personalization programs report exceeding their revenue, customer experience, and conversion rate goals.

As customers move through the buying process, they expect more personalized information.

Personalization of content after a purchase is most important for B2C buyers; almost half of them expect it when they need help or interact with the company as a present customer.

B2B customers expect a lot more personalized content throughout the buying process. 66% of buyers expect customized content during decision-making, and 72% expect personalization after the purchase.

The marketing team’s job is determining what material buyers will be interested in and making it.

However, sales AI tools can help reps who work directly with customers find and send personalized content to prospects who are already in the sales process. This can be done in real-time.

Sales Automation and Getting More Done

Competitive wages, high turnover rates, and the fact that it’s hard to find and keep track of activities that bring in money make sales teams very expensive.

Since sellers spend most of their time doing things other than selling, a more productive sales team directly leads to more money in the bank.

AI makes sales more efficient in two ways: it boosts team productivity and automates jobs that sales reps would generally do repeatedly.

Here are some examples:

• Predictive analytics show the buying patterns of customers, which cuts down on the time needed for study.
• AI sales assistants answer customer questions and instantly qualify leads based on rules set by the sales team.
• Time-consuming chores like data entry and sales notes are now done automatically, which frees up reps’ schedules and ensures the work is done correctly.

The sales team is more productive and has more time to do what counts most: closing deals and helping customers.

Getting better results

When AI is used in sales, it helps SDRs, their bosses, and the company’s bottom line.

The seller can use AI to make more sales, get more accurate data, and be more effective in sales meetings. This is an instant benefit for the seller.

Sales managers use AI to make their teams stronger. They can see more quickly when an SDR isn’t doing their job and give them more focused training or coaching.

Managers can also find performance trends and use this information to help them decide on their plans.

Big companies use AI to monitor how their sales teams deal with customers and make changes to the sales plan as needed.

AI helps teams see how well they’re doing against their goals and can even tell you how customers feel, which can help you build better relationships with them.

Types of sales tools that use AI

Different types of sales AI are used by businesses for different tasks. They can use the following tools to build a full tech stack that uses AI.

Chatbots

Most of the time, chatbots are used for customer service and generating leads, but if a customer is ready to buy, they can also be used for direct sales.

Mainly, they get rid of the need for sales reps to deal with customers to do simple, repetitive jobs like

• Giving answers to questions that are often asked
• Giving basic details about the product
• Making suggestions that fit your needs
• Setting up research calls and product demos
• Finding information on the website, product catalog, or knowledge base.

Because they are software parts that work with the company website, they can respond to customers immediately, 24 hours a day. This gives sales teams more time to work on more complex tasks.

Chatbots are designed to work with e-commerce, CRM, and email marketing tools so that the sales process runs smoothly. They can also gather information about how customers use and feel about the product. This helps the marketing and sales teams do their jobs better.

Learning by Machine

When using AI in sales, machine learning algorithms are often taught by looking at old sales data. They look at past transactions, customer interactions, product details, and many other factors to find patterns and links.

After these algorithms process this information, they can predict future sales, find good leads, or suggest items to show customers. It means that machine learning algorithms get “smarter” every time the company uses them because they always learn as they are introduced to new data.

• Many CRM tools use machine learning to guess how customers will act and tell sales reps what to do.
• Tools that help sales reps make sales use it to find material that makes the sales pitch more personal.
• Email marketing tools use it to ensure that email subject lines, content, and rhythm are perfect.

Machine learning gives sales reps and managers information that they use to shape their strategies and make decisions based on data.

For example, a salesperson could use a machine learning tool to sort leads by how likely they are to become customers.

Language Processing for Natural Language

NLP examines text data from random sources, like chat logs, emails, social media posts, and product reviews, and figures out what it all means.

It finds and pulls out customer sentiment, purpose, and entity segmentation, including names, dates, and places.

This information helps sales teams determine what goods customers want, how prospects respond to their conversations, and how happy customers are with their products.

An NLP algorithm might look at customer emails and put them into groups based on whether they are positive or negative or if they talk about certain goods or features.

They could also use a Zoom plugin that NLP drives to listen to sales calls and look for patterns in customer conversations.

Visualization with AI

AI models help with the sales process in several ways:

• Reporting on sales. Visualizations powered by AI help sales reps quickly spot trends and see how their actions are doing.
• They are separating customers into groups. Sales teams can divide customers into groups and make material and offers more relevant to each group using AI.
• Suggestions for products. Visualizations powered by AI show sales teams the goods customers are most likely to buy so they can tailor their pitches and get more sales.
• They are predicting income. AI visualizations can make predictions based on past sales data, which helps salespeople plan their strategies.
• They are setting up the product. CPQ software with a 3D product configurator can help sellers of physical goods, like B2B makers, show buyers what their goods look like.

More advanced analytics

The future of making decisions based on data is augmented analytics. It uses NLP, machine learning, and text mining to improve the process of analyzing data.

It aims to make the experience better for users by eliminating the need for them to do things by hand. Platforms for augmented analytics take in vast amounts of data from many sources and examine it almost instantly.

The information these platforms give us is used to make predictive models and give salespeople suggestions they can follow.

What Will Come Next for AI in Sales?

AI is still very young. AI will be used increasingly in sales as it becomes more common.

Here are a few of the most exciting discoveries in sales AI:

• Personalization on a large scale. As creative AI improves, salespeople can enter more data and make more personalized products in less time.
• Prices that change. Dynamic pricing algorithms that AI drives can help sales teams change prices in real time based on what customers want and how the market is doing.
• Computers that talk. Chatbots are getting smarter and can now have more complicated conversations with customers and potential customers.
• Analytics in real time. Thanks to faster and more robust data analysis tools, sales teams can make decisions almost instantly.
• Less help from the seller. A Gartner study shows that 44% of millennial buyers don’t want to talk to a seller during the buying process. This is a significant need that many software companies are working hard to meet.
• The seller should offer more value. With robots, sellers can give their buyers more value, such as highly personalized material and proactive advice.