How sales teams can use generative AI

How can sales teams use generative AI?

Generative AI's sales use cases fall within three overarching categories: content generation, data analysis and task automation.

1. Content generation

Write personalized emails. Sales teams can use generative AI tools to quickly draft personalized emails for inbound and outbound leads. These tools can draw on CRM data, such as customers' names, their past interactions and product demos they've watched, to craft an email that addresses their needs and catches their eye.

These tools can also pull data from sources like LinkedIn or company websites to find items like contacts or previous employers that sales reps have in common with outbound leads. AI tools can then use that information to generate emails so reps can build an initial rapport with cold leads.

Sales reps typically interact with these tools from a chat window in their email systems' interface. The tools can suggest responses to each email and let users input custom prompts, such as "write a cold sales email to John Smith about our new product." The tool then generates a draft which users can edit.

Design sales proposal slides. Sales teams can use generative AI tools to create visual artifacts, such as presentation slides, in addition to text. A well-designed presentation can win over customers and prospects, but not all sales reps have an eye for design.

Generative AI presentation tools let sales reps use natural language to design slide decks and other forms of AI art. For example, a sales rep could prompt the tool and write: "Create a sales proposal for a B2B tech company." The tool could then generate a deck of slides that includes key sales proposal elements, such as an "about us" slide, pie charts and user quotes. The sales rep could then fill in the slide deck with their unique company information.

Improve sales chatbots. Generative AI's advanced natural language processing capabilities can enhance customer-facing chatbots. For years, organizations have deployed sales chatbots across their company websites to engage customers, answer product questions and collect information. However, these tools can feel overly scripted and robotic.

Generative AI's ability to understand complex queries and generate humanlike responses can make these chatbots more helpful and engaging than past generations of chatbots.

2. Data analysis

Coach sales reps in real time. Generative AI tools can analyze sales interactions, such as email, live chat and video conferencing conversations, in real time and coach sales reps along the way. To enable sales coaching, organizations must customize their AI tools. They can collect and feed large amounts of past sales interaction data into the tool to help it recognize company terminology and elements of successful and unsuccessful sales interactions.

A customer might ask a sales rep, "What makes your product different from competitors?" The tool can then quickly generate a cue card to replicate how sales reps successfully answered this question in the past. The sales rep could then choose to use the suggestion or not.

Generative AI can also help sales reps identify unsuccessful behaviors that cost them valuable leads. For example, a tool could analyze a sales rep's interaction history to learn their deals often fall through when they try to set up a meeting too early in the relationship. The sales rep could then work on building a rapport before trying to sell.

Lead scoring. Sales teams can have hundreds or thousands of leads, depending on their organizations' size. Generative AI tools can quickly analyze massive amounts of customer data to enhance lead scoring efforts and help sales reps know which prospects to prioritize.

To accurately score leads, these tools can analyze the following types of customer data:

• Website behavior.
• Demographics, such as age and gender.
• Firmographics, such as employer size and industry.
• Job title.
• Purchase history.
• Social media engagement.

Generative AI tools can synthesize all this data to attribute a score to each lead. They can also process information in real time, so scores change regularly as new data comes in.

Improve forecasting. All departments within an organization rely on sales forecasts -- whether monthly, quarterly or annually -- for resource allocation. Generative AI's ability to analyze large amounts of unstructured data, such as sales interactions, can improve the accuracy of these forecasts.

If a sales forecast predicts an organization will bring in $50 million more in revenue than the previous year, it might decide to increase technology and hiring budgets for production and marketing teams. Yet, if the forecast is wrong, organizations might need to lay off employees, cut budgets and halt production. Generative AI tools can analyze information in CRM systems, along with data about the economy and competitors' pricing, to predict future revenue more quickly and accurately than a team of humans.

3. Task automation

Add contacts to a CRM system. Sales reps spend a lot of time adding contact information to CRM systems -- especially those in large enterprises with complex sales processes. Generative AI can speed up this process, as it lets sales reps use natural language prompts to input data, as opposed to manually filling out fields. For example, a sales rep could type, "add [email protected] and follow up next week." The tool could then automatically input the contact email and action item into the CRM contact list.

Research leads. Generative AI tools can help sales reps research leads directly from their CRM systems. For example, a sales rep at a Houston-based healthcare SaaS company could ask the tool for a list of all large hospitals and healthcare providers in the Houston area. The tool could then offer up a list that includes company descriptions, addresses, key contacts and an option to automatically add the information to the CRM system.

Summarize recorded interactions. Generative AI can also summarize interactions from calls, emails and video chats, so sales reps can look back on important details. These tools can automatically add interaction highlights as notes in the CRM system to save sales reps time.

Risk of generative AI for sales

Despite generative AI's benefits, the technology isn't a panacea. Organizations should understand the following risks before they invest in generative AI:

• Data protection. To use generative AI most effectively, sales teams need to train a tool on their company data, which includes personally identifiable information, such as customer names, email addresses and credit card information. Organizations must implement security measures, such as encryption and access controls, to protect this information and achieve compliance with data privacy regulations.
• Lack of empathy. Although generative AI can write personalized emails at scale, these messages might lack the personal touch and empathy of a salesperson. Sales reps should use the tool to create first drafts and then edit them manually.
• False information. If generative AI tools don't have enough information to answer a question, they sometimes generate false answers, which can negatively affect CX. For example, a chatbot could give customers incorrect pricing information for a product. To increase accuracy, organizations should extensively train these tools on their knowledge bases, then regularly test and monitor output quality.

Organizations that decide to use generative AI have different implementation options. If they need a lot of flexibility and have an internal team of AI developers and experts, they can build their own tool. However, this option can take considerable time and effort.

Alternatively, sales leaders can purchase a generative AI tool and train it on their company data. Standalone tools can integrate with an organization's existing CRM or email system, whereas other generative AI tools come as features within larger CRM platforms.