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Generative AI for Sales in 2024

Sales involves extensive interactions and transactions, generating substantial amounts of data such as text from email threads, audio from phone calls, and video from face-to-face encounters. These unstructured data types, which constitute around 90% of the business data, are precisely what generative AI models are built to handle (see Figure 1). The dynamic and adaptive nature of sales presents numerous opportunities for generative AI for sales to analyze, understand, connect, and personalize.

Figure 1. Structured data vs unstructured data

Source: M Files

Generative AI tools are becoming a part of sales technology. In this article, we will explain top use cases of generative AI for sales that must be known by sales professionals. Additionally, we will provide some of the latest real implementations of generative AI in sales technology.

10 Use Cases of Generative AI for Sales

1- Content creation for sales materials

AI-generated sales content can support various content marketing efforts, including SEO-optimized blog posts, case studies, whitepapers, and social media updates. By creating content that is high-quality and targeted at scale, sales teams can effectively attract, engage, and nurture potential customers throughout the sales funnel. 

Generative AI tools like ChatGPT can be used to create sales enablement materials such as:

  • Sales scripts
  • Brochures
  • Sales playbooks

2- CRM system integration

CRM systems typically contain a wealth of information in the form of customer interactions, support tickets, social media comments, and email communications. Much of this data is unstructured, making it difficult for traditional analytics tools to extract valuable insights.

By employing natural language processing (NLP) and machine learning algorithms, generative AI can analyze this unstructured data to uncover trends, sentiment, and customer needs. For instance, analyzing unstructured data can reveal common customer pain points, enabling businesses to address these concerns proactively and improve customer satisfaction (Figure 2).

Figure 2. AI powered CRM systems

In March 20223, the CRM market leader Salesforce introduced Einstein GPT, the first-ever generative AI CRM solution, providing AI-generated content throughout every interaction in sales, service, marketing, and more.

3- Customer segmentation

Generative AI can process large amounts of customer data to identify patterns and classify customers into distinct segments based on their behaviors, preferences, and needs. By understanding these customer segments, sales teams can develop tailored strategies, messaging, and offers that resonate with each group, improving the overall effectiveness of sales efforts.

For more on customer segmentation, check our article on the types, steps and benefits of customer segmentation.

4- Lead generation

Generative AI can help identify potential leads by analyzing customer data, online behavior, social media activity, and other sources to pinpoint potential customers who are most likely to be interested in your product or service.

For learning other lead generation techniques, check our article on lead generation technologies.

5- Personalized sales emails

Generative AI can craft personalized email and messaging templates by analyzing customers’ previous interactions, preferences, and interests. By using natural language processing (NLP) and natural language generation (NLG) technologies, AI can create engaging and relevant content that resonates with prospects, resulting in higher open and response rates, as well as improved conversion rates.

Microsoft’s Viva Sales, which is a sales application equipped with advanced tech solutions, incorporates the large language model GPT into its CRM system to automate some of the sales tasks such as responding to customer emails.

Figure 3. Email generation by AI in Viva Sales

Source: Microsoft

A few weeks later, Salesforce followed by launching Einstein GPT.1 Additionally, Outreach company announced its Smart Email Assist, an AI-powered automatic email generator for sales teams.2

6- Product recommendations

Generative AI algorithms can analyze a wealth of customer data to generate personalized product recommendations, such as:

  • Purchase history
  • Browsing behavior
  • Stated preferences 

By offering the right products at the right time, sales teams can increase the likelihood of closing deals and boosting average transaction value.

7- Sales analytics

Generative AI can significantly enhance sales analytics by providing valuable insights for data-driven decision making and strategy optimization. It can analyze sales data to identify patterns, trends, and key performance indicators, as well as predict future sales performance using machine learning algorithms. 

Additionally, AI can offer prescriptive analytics, providing actionable recommendations to improve sales outcomes. Real-time analytics, sentiment analysis, pipeline analysis, and sales rep performance analysis are other areas where generative AI can benefit sales teams.

8- Sales automation

One of the biggest benefits of generative AI in sales is its ability to automate repetitive tasks. For example, salespeople often spend a significant amount of time creating proposals, email templates, and other sales materials. Generative AI can quickly and efficiently generate these materials, freeing up valuable time for salespeople to focus on other aspects of their job, such as building relationships with clients.

9- Sales training

Generative AI can create customized training materials, simulations, and role-play scenarios that address the unique challenges and requirements of each sales team in sales processes. By offering tailored learning experiences, AI-driven sales training can help sales representatives refine their skills, build product knowledge, and improve their ability to address customer objections, leading to better performance and higher success rates in a sales process.

10- Sentiment analysis

Generative AI can analyze customer feedback, reviews, and social media comments to gauge sentiment and identify potential issues or opportunities. This information can help sales teams address customer concerns, capitalize on positive feedback, and tailor their sales messages to resonate with their target audience.

Figure 4. High-level overview of a sentiment classification approach

Source: “The Impact of Synthetic Text Generation for Sentiment Analysis Using GAN-based Models”

For more on this, check out our article on the use of ChatGPT in sentiment analysis.

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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