Sales in the age of LLMs
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Sales in the age of LLMs

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After writing about the potential use cases of customer support in the age of LLMs, I want to write about the application of AI in sales.
The advent of Large Language Models (LLMs) is reshaping the landscape of sales, offering unprecedented tools for efficiency and personalization. While AI presents exciting opportunities, its true power lies in augmenting rather than replacing human salespeople. In this article, I argue that the most effective sales strategies in the age of LLMs will combine AI's data-processing capabilities with the irreplaceable human elements of empathy, relationship-building, and nuanced understanding of the customer. Let’s dive in!

My Experience

Throughout my entire career as a product person, I have not directly worked in sales. However, I have worked closely with business heads, founders, and sales professionals in the field at B2B startups. For example, I worked closely with the founders and the DevRel Lead at a funded startup, where we were selling access to a large developer audience to dev tools companies by way of hackathons or sponsorships. Before that, I worked directly with the founder of an MIT Labs-funded startup that was selling AI-driven HR Tech and pivoted to Customer Support quality SaaS tools to SMBs and midmarket companies.
Most B2B product companies, even if their growth is product-led, like Notion, Coda, and others — should have a sales-assist approach to them. Because eventually, you will need sales if you have to go upmarket from SMBs to midmarket to enterprise.

What is good sales?

Good sales at its core involves understanding the customer’s needs, challenges, and goals and then pitching the product as a solution to those problems. This is also known as consultative sales. And what aids this is the fact of connecting with another person and building a relationship among shared goals and interests.
Good sales is not about manipulation or pressure tactics. Instead, it's about aligning the interests of the seller and the buyer. When done right, both parties benefit: the customer gets a solution that improves their business or life, and the salesperson (and their company) gains a satisfied customer and revenue.
I think AI can help tremendously in these two aspects.

The Sales Funnel

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Let’s discuss the application of AI by discussing it at each stage of the sales process into different parts of a funnel — prospecting, qualifying, proposal, and closing.
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Before diving into the funnel stages, it's crucial to define the Ideal Customer Profile (ICP) according to the product vision and features. It serves as the foundation for all subsequent sales activities, ensuring that efforts throughout the funnel are targeted toward the most promising and valuable potential.

Ideal Customer Profile

Creating an ICP involves identifying specific attributes that define the most valuable customers. These typically include:
  • Demographics: Industry, company size, location.
  • Technographics: Technologies the company uses which may complement or compete with your product.
  • Behavioral Characteristics: Their buying patterns, brand loyalty, and product usage.
  • Psychographics: The company’s values, culture, and priorities.
  • Budgetary Constraints: Their spending power and willingness to invest in solutions like yours.
An example of an ICP could be:
A SaaS company that offers project management tools might define its ICP as mid-sized tech companies with 100-500 employees, based in North America, that use agile methodologies and have a high adoption rate of cloud technologies. These companies are likely to value collaborative, scalable tools with robust integration capabilities.
Where AI can help:
You could give details of your current set of customers, customers that have churned, and let it define an ICP for you.
And like with customer support, you could use a combination of prompt engineering, RAG, and function calling to support different workflows.

Prospecting

Once you have defined your ICP, the sales team enters the stage of prospecting.
Depending on the GTM motion you employ, if the outreach is completely outbound, you identify key stakeholders and prospects at your ICP using platforms like LinkedIn, proprietary systems, or your existing network through alumni, investors, and other founders.
Or if the outreach contains inbound leads through webinars, demos, contact forms, freemium, or trials of the product, you can also reach out to prospects that way.
Where AI can help:
Create content to generate inbound lead gen through blogs, videos, social media posts, etc.

Qualifying

After sending the outreach and tracking the response, organize a discovery call once you get a response.
AI can enhance this process by using live transcription of recorded calls (with disclosure) to transcribe and prompt salespeople with relevant questions, responses, or information about features, integrations, unique selling points, or similar customers. It can cater to objection handling and be tailored to a specific company or industry.
Qualifying leads with the BANT (Budget, Authority, Need, Timeline) criteria is another process that can be done with an AI assist.
This could be done with just formulas and rules, but AI can contain the subjective part of it to ensure that it's not only numbers and can give a score.
That’s the part about calls. For email communications, AI can help with the following:
From email responses into a CRM to make it searchable and easier to analyze. Responses from calls can be categorized into reasons for accepting or rejecting the product, providing valuable insights for product, engineering, and support teams.
This approach can help normalize the quality of sales calls, making average performers significantly better while slightly improving top performers.

Proposal

Once you reach the proposal stage, it’s important to create the proposal keeping in mind the exact points mentioned in the sales discussions.
AI can help with this by auto-creating drafts of proposals from selected points from sales discussions.
I remember a friend was working at an Ed-tech company where they were creating sales proposals and there was a quality check team to ensure that there were no mistakes.
The AI can go through the proposal and see if there is something missed from the conversations or something from the calls or comments that is incorrect in the final proposal and flag it to the proposal creator.

Closing

One thing I have used AI for is to create simulations of the satisfaction levels of different stakeholders while doing PM work.
This is not too dissimilar from change management efforts while doing sales. You can estimate how convinced one part is vs the other.
And we also know that decisions are not always made by one person, but by a decision-making unit (DMU) that consists of a few important stakeholders. E.g. while buying a SaaS tool for Sales operations, the VP of Sales, CFO, and IT Lead will form a decision-making unit to make the final decision on features, pricing, and integration.
You can add stakeholders, their latest comments, your sentiment, and get the AI to give a score and what should be the next steps.
 
Other use cases can apply across stages include:
  • Insights on the pipeline to meet revenue targets
  • Combination of code and insights generator
  • Sales forecasting
  • Account Management
But we’ll cover them in a separate article.

Final Thoughts

AI is revolutionizing sales by enhancing efficiency and providing valuable insights across the entire funnel. However, it's crucial to remember that AI should augment, not replace, human salespeople. The core of sales remains building relationships and understanding customer needs.
While AI can handle data-intensive tasks and provide real-time assistance, successful implementation requires balancing technological capabilities with the human touch. Organizations must also navigate challenges like data privacy and the ethical use of AI.
As AI evolves, we can expect more sophisticated applications in sales. However, the fundamental principles of good sales – understanding customer needs and providing value – will remain constant. The future belongs to those who can effectively combine AI's power with strong interpersonal skills.