AI for Sales

What Is Artificial Intelligence (AI) for Sales?

Artificial intelligence (AI) for sales describes using automated technology to simplify and streamline the sales process. There are several different kinds of AI-enabled sales technology, including:

• Chatbots: Computer programs that can engage in conversation with customers and prospects, providing them with personalized answers to their questions and helping them through the sales process.
• Sales automation: Automated systems that manage customer relationships and lead generation activities, such as scheduling appointments and providing personalized recommendations for products and services.
• Natural language processing (NLP): AI models that can understand and interpret human language, allowing customers to ask questions in natural language without typing out commands.
• Predictive analytics: Software that analyzes customer data to identify likely buyers and make predictions about future sales trends.
• Automated lead scoring: Tools that assess prospects’ likelihood of becoming a customer based on their behavior and profile information.
• Marketing automation: AI models that help marketers automate their campaigns and deliver personalized content to customers.

Most sales technology today is powered by machine learning algorithms, which enable the software to learn from sales data and make more accurate predictions.

Salesforce, the popular CRM system, was one of the first notable applications of AI in sales. Since the company already had a global user base of millions, it was uniquely positioned to train its software based on its user inputs.

The early Salesforce models helped users by delivering relevant insights, predictions on lead behavior, recommendations on next-best actions, and automating repetitive tasks like adding notes to the CRM.

Since the company began developing AI technology, it (among many others) began to pave the way for digital sales transformation.

Synonyms

• Conversational AI for sales: AI technology that 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.”

The Benefits of AI For Sales

From 2018 to 2022, AI adoption in sales has increased by 76%, with high-performing sales teams 2.8 times more likely to use an AI-integrated sales stack.

There are several benefits of AI for sales, including:

1. More accurate sales attribution

One of the biggest points of contention between sales and marketing teams is which organization’s touchpoints had a greater impact on a sale.

In smaller organizations, it’s fairly easy to determine who is responsible. But as the sales cycle becomes longer, sales performance becomes increasingly difficult to attribute to any one source.

According to research from Rain Sales Training, it takes an average of eight touchpoints for sales reps to land meetings (or other forms of conversion). In some B2B sales processes, it can take upwards of 20 touchpoints to close a sale.

Machine learning models learn to analyze the impact of each touchpoint more effectively, giving credit where credit is due. And more importantly, sellers are more aware of which sales strategies actually improve the chances of closing a deal.

2. Lower churn rates

A high churn rate holds companies back from sustainable growth, and often sales reps don’t have the data they need to spot customers at risk of churning.

Based on historical customer responsiveness, engagement, and consistencies among past customers that churned, AI-powered customer success models provide insights on which customers are likely to renew their subscriptions or contracts, as well as any that need extra attention.

Likewise, AI-powered customer segmentation models help sales and marketing teams discover patterns in customer buying behavior that indicate churn risk.

Using this data, SDRs can reach out to at-risk customers and offer discounts or other incentives to keep them from leaving.

3. Higher degree of customer engagement and personalization

Buyers want personalized interactions. For B2B sellers (i.e., the majority of sales reps in this context), personalization isn’t just about the product—it’s about how the customer is treated.

73% of B2B buyers say they want personalized experiences like those B2C customers receive, but only 22% say that sellers are meeting that need.

Enlisting the help of AI means SDRs can access valuable insights that enhance their lead engagement. They can use this information the lead’s website use patterns, current solutions they use, and past digital interactions to personalize content recommendations based on their preferences and needs.

Before AI, sellers needed to frantically sift through emails, social media DMs, and CRM notes to prepare for their product demos and intro calls.

With artificial intelligence handling the data, these data points are brought to a single source of truth.

With a 360-degree view of their customers, sales reps are more organized and productive.

4. Real-time feedback on sales calls

AI in sales gives reps real-time feedback during discovery calls and product demos. It picks up on small conversational nuances like their talking speed, tone of voice, and facial expressions, and provides feedback on how to adjust their approach, helping reps become better at building relationships with prospects.

It also helps sellers understand their potential customers’ sentiment and body language, and guides their focus to the most important parts of the conversation to generate a more accurate picture of customer requirements.

AI also converts sales calls into written transcripts in seconds. Individual reps can review these to learn and find improvement, and sales leaders can use them to measure the overall performance of their sales team.

5. Accurate sales forecasts and reports

Sales managers need to report projections to executive leadership and use reliable data points to determine whether their sales team is on track. With software that uses deep learning models based on historical sales and customer data, accurate forecasts and reports can be generated at the click of a button.

AI also automates the creation of regular internal reports so that managers can check in on team performance without having to manually compile spreadsheets every week or month.

In this way, AI can save reps and managers time that would otherwise be spent on manual report consolidation and sales forecasting processes, while ensuring the accuracy of its projections.

As these projections move their way up the rungs of the company hierarchy, executive leadership and investors can make better decisions about the future of the company.

6. More time for sales activities

The average rep spends less than one-third of their time on sales activities—a clear indicator as to why 79% of sales team members report disengagement.

According to Salesforce, the three most common time-wasting activities are:

• Managing emails
• Logging activities
• Inputting notes and sales data

AI-driven sales processes practically eliminate these tasks.

Logging activities like sales pipeline movement, customer interactions, and follow-ups can be automated. Notes are created and stored automatically in CRM. And email autoresponders can handle the first line of engagement from prospects, freeing reps to focus on more important tasks.

7. Better lead scoring

Sellers want more customer interactions, but not ones that will waste their time. Since they take away valuable time and energy that could be otherwise spent selling, unqualified sales leads are just as bad (or worse) than no leads at all.

When determining a lead’s likelihood to convert, AI automatically accounts for numerous factors that human judgment does not immediately notice, including their behavior on specific pages of your website (such as how long they spend scrolling), their company size and industry, website visits, and other data points.

These insights make lead scoring more accurate and eliminate the need for reps to think too hard about whether to pursue each lead.

8. Improved sales training and faster ramp time

Faster ramp time means reps can start selling faster. For sellers, training is more than just a one-week sales onboarding program with shadowing. It needs to be an ongoing and personalized coaching experience catered to each individual representative.

But 26% of sellers find their sales training to be ineffective and 84% of reps forget their sales training within just three months.

This adversely impacts sales productivity as reps may face more obstacles during the sales process when they do not learn or retain enough from training and coaching.

Guided selling, lead scoring, real-time sales feedback, and AI-enabled insights help sellers improve faster and show sales managers where their reps need extra help.

Ways to Use AI For Sales Operations

Sales operations comprises the various activities and processes that support a sales team. AI can help streamline operations, reduce manual efforts, and provide valuable insights to make smarter decisions.

AI-driven sales ops activities include:

Sales Forecasting

Historically, sales reports and projections were largely based on intuition. Since most sales data is multivariate and siloed in different systems (e.g., CRM, marketing automation, ecommerce platform), it was difficult to accurately predict future sales performance.

AI in sales centralizes this information and helps companies make accurate sales projections based on buyer behavior, market trends, historical data, and countless metrics a human couldn’t possibly account for.

Lead Generation and Prospecting

AI chatbots and other conversational AI tools can’t take over the entire lead generation process, but they do relieve salespeople of frequently asked questions, initial lead engagement, and website engagements that won’t lead to anything.

Instead, chatbot users can develop scripts using AI that improve over time without any intervention, just like a new employee.

Chatbots are capable of identifying specific signals that indicate the need to pass the conversation over to a sales representative. The conversation log can be updated automatically, so the representative taking over has access to the entire chat history.

This can help with lead identification and speed up the sales process while the chatlogs can be utilized to train more efficient and effective chatbots in the future.

Lead Scoring

AI models evaluate multiple data points including:

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

They use these to tell sales reps whether or not to prioritize a lead and how to engage them.

AI can also evaluate customer feedback, emails, and other data points to qualify leads more accurately than a human ever could. This helps save time by eliminating the need for reps to manually assign scores or deal with unqualified leads.

Sales Enablement

AI plays a unique role in sales enablement—through real-time feedback and content curation, it helps sellers respond to reps with targeted messaging, helpful articles, and relevant information at lightning speed.

Suppose a sales rep working for a marketing automation software company is on an initial discovery call with a qualified prospect in the ecommerce industry.

AI listens to the whole conversation and watches each member’s on-camera movements. With this data, it messages the seller with real-time coaching on how to adjust their pitch, pique interest, or ask more suitable questions.

During the call, the prospect mentions that their biggest internal problem with their current marketing engagement is “emailing too frequently.”

AI immediately identifies this as a potential opportunity for the seller to focus on during their pitch. It highlights elements of the product that directly address the issue and sends the prospect an article from the company blog discussing “how to find the perfect email cadence.”

Price Optimization

Price optimization describes the process of finding the “sweet spot” between profit margin per sale and what the customer perceives as a fair price.

Optimizing prices without an algorithmic approach entails lots of guesswork—a product must hit the market at a specific price, which must be adjusted over time to reflect changing market conditions.

AI learns from historical data to predict the market’s reaction to changes and explain how they feel about the product’s value, removing some guesswork from the process.

Upselling and Cross-Selling

In sales, AI recommendation engines work similar to Amazon’s recommendation algorithm, except the seller is the intermediary between the customer and the products.

Some sales tools, including CPQ software, some CRMs, and sales intelligence platforms, use AI to uncover ways reps can offer additional value to their existing and potential customers.

Depending on where the customer is in the buyer journey, reps can use AI-generated recommendations to suggest related products and services that may benefit them.

Personalization

Sales and marketing teams have prioritized personalizing content, but personalization at scale presents significant challenges.

Still, there are clear benefits to a well-executed personalization strategy. Adobe’s Personalization at Scale report shows that global decision-makers in charge of personalization programs report exceeding revenue, customer experience, and conversion rate targets 66% of the time.

Customers expect personalized content to a greater extent as they progress through the purchase journey.

For B2C buyers, post-purchase content personalization is most important, with almost half expecting personalized content when getting help or engaging with the company as a current customer.

B2B customers have tremendously higher expectations for personalized content throughout the purchase journey, including 66% of all buyers, who expect personalized content throughout the buying decision process and 72% who expect personalization after.

Understanding what content will connect with buyers and creating it are jobs for the marketing team.

But sales AI tools can help customer-facing reps find and send out personalized content in real-time to prospects who are already in the sales funnel.

Sales Automation and Productivity

Competitive salaries, statistically high turnover, and the complexity of identifying and tracking revenue-generating activities make sales teams incredibly expensive.

Since sellers spend the majority of their time on non-selling activities, a more productive sales force directly correlates to increased profitability.

AI drives sales efficiency in two ways: improving team productivity and automating repetitive tasks reps would otherwise do.

Here are a few examples:

• Predictive analytics highlight customer buying patterns, cutting down research time.
• AI sales assistants provide answers to customer questions and automatically qualify leads based on criteria set by the sales team.
• Time-consuming tasks such as data entry and sales notes are now automatic, opening up reps’ schedules and ensuring accuracy.

The end result is a more productive sales team that has time to focus on what really matters: closing deals and helping customers.

Performance Improvement

AI in sales improves performance for SDRs, their managers, and the company’s bottom line.

The most immediate benefit falls onto the seller, who can use AI to sell more, have more accurate data, and be more effective during sales engagements.

Sales managers employ AI to build better teams. When an SDR is underperforming, they can spot them more quickly and provide targeted training or coaching.

Managers also identify trends in performance and can incorporate this data into their strategy.

At a macro level, companies use AI to monitor how sales teams interact with customers and change the sales playbook as needed.

AI helps measure team performance against goals and even provides insights on customer sentiment that can be used to develop better relationships.

Types of AI-Powered Sales Tools

Organizations use different kinds of sales AI for different functions. To build a complete AI-enabled tech stack, they can employ the following tools.

Chatbots

Chatbots are generally used for customer service and lead generation, but they can also serve as a mechanism for direct selling if a customer is ready to make a purchase decision.

Mainly, they eliminate the need for sales reps to engage with customers for simple, repetitive tasks, such as:

• Answering frequently asked questions
• Providing basic product information
• Giving personalized recommendations
• Booking product demos and discovery calls
• Navigating the website, product catalog, or knowledge base to find content

Since they are software components that integrate with the company website, they deliver immediate responses to customers 24 hours per day while freeing up valuable time for sales teams to focus on more complex tasks.

Chatbots are meant to integrate with CRM, email marketing, and ecommerce platforms to create a seamless sales funnel. They can also collect and analyze data about how customers use and feel about the product, which sales and marketing teams use to improve their efforts.

Machine Learning

In the context of AI in sales, machine learning algorithms are often trained on historical sales data. They learn from past transactions, customer interactions, product information, and many other variables to understand patterns and correlations.

Once these algorithms digest this data, they can forecast future sales, identify promising leads, or suggest products to show customers. Machine learning algorithms continuously learn as they are exposed to new data, meaning they get “smarter” every time the company uses them.

• CRM platforms often use machine learning to predict customer behavior and recommend actions to sales reps.
• Sales enablement tools use it to find content that helps reps sell on a personal level.
• Email marketing platforms use it to optimize email cadences, subject lines, and content.

Sales reps and managers use the insights generated by machine learning to inform their strategies and make data-driven decisions.

For instance, a sales rep might use a machine learning-powered tool to prioritize leads based on their predicted conversion likelihood.

Natural Language Processing (NLP)

NLP analyzes text data from unstructured sources (such as customer emails, chat logs, social media comments, and product reviews) contextually.

It identifies and extracts customer sentiment and intent, as well as entity segmentation, including names, dates, and locations.

Sales teams can use this data to see which products customers are interested in buying, what kind of response they got from prospects during conversations, or how satisfied customers are with their product.

For instance, an NLP algorithm might analyze customer emails and categorize them based on whether they contain positive or negative sentiment, or whether they mention certain products or features.

Alternatively, they might use an NLP-powered Zoom plugin to search through sales calls and identify trends in customer conversations.

AI Visualization

AI visualizations serve several purposes in the sales process:

• Sales reporting. AI-powered visualizations allow sales reps to quickly identify trends and check the performance of their activities.
• Customer segmentation. By leveraging AI, sales teams can segment customers and tailor content and offers to different groups of buyers.
• Product recommendations. AI-powered visualizations surface products that customers are most likely to buy, allowing sales teams to personalize their offers and maximize conversions.
• Revenue forecasting. AI-enabled visualizations can generate forecasts based on past sales data, allowing sales reps to plan their strategies accordingly.
• Product configuration. Physical products vendors (such as B2B manufacturers) can help buyers visualize their products using a CPQ software with a 3D product configurator.

Augmented Analytics

Augmented analytics is the future of data-driven decision-making. It combines NLP, machine learning, and text mining to enhance data analysis processes.

It aims to improve user experience by removing manual efforts from the process. Augmented analytics platforms ingest vast amounts of data from multiple sources and analyze it in near real-time.

The insights generated by these platforms are used to create predictive models and generate actionable recommendations for sales reps.

What’s Next in AI For Sales 

AI is still in its infancy. As AI technology matures, it will become more powerful and pervasive in sales.

Briefly, here are some of the most promising developments in sales AI:

• Personalization at scale. As generative AI improves, salespeople’s ability to input data and create personalize offerings in seconds will increase.
• Dynamic pricing. AI-powered dynamic pricing algorithms can help sales teams adjust prices in real-time, based on customer preferences and market conditions.
• Chatbots. Chatbots are becoming increasingly sophisticated and can handle more complex conversations with prospects and customers.
• Real-time analytics. Data analysis solutions will become faster and more powerful, allowing sales teams to make decisions in near real-time.
• Less seller intervention. According to Gartner research, 44% of millennial buyers don’t want to interact with a seller at all during the buying process—a significant customer need that many software companies are rushing to meet.
• More value from the seller. With robots on their side, sellers can offer their buyers increasing levels of value, from providing hyper-personalized content to offering proactive advice.

People Also Ask