Last updated: AI in B2B Sales: Transform your sales for better outcomes with practical use cases and strategies

AI in B2B Sales: Transform your sales for better outcomes with practical use cases and strategies

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B2B sales in many ways is an intricate dance between buyer and seller. Both need to move together with matching speed and intent. Unlike most partner dances however, the buyer not only dictates direction and speed but also follows their own unique path.

Conversely, B2B sales also is largely a numbers game. The number of sales actions and the quality of each interaction correlate to the amount of won business. These two ways of thinking about B2B selling reinforce the complexity sellers face and illustrate why organizations run into productivity thresholds. AI in B2B sales holds the key to breaking through these limitations.

Data is at the heart of every organization: Customer records, buying trends, interaction data, inventory, and more. Within this business data is the key to reducing sales complexity.

Artificial intelligence for B2B sales, when implemented correctly, uses all of this critical data to surface insights and trends that can guide and improve sales outcomes.

But for sales AI to be effective, it not only needs to be connected, it needs to be embedded into critical sales processes and workspaces sellers use daily.

INTELLIGENT SELLING.  MAXIMIZED REVENUE. Intelligent, guided sales experiences help sellers WIN MORE BUSINESS, FASTER. And the best news is that smarter selling is just a few clicks away.

Breaking down the types of AI

There are numerous intelligent technologies in the market today, but we can broadly group these into three general varieties of AI:

  1. Predictive AI is exactly what it sounds like. It uses historic data, current data, and third-party data sources to provide predictions and deep insights on what is expected to occur in the future.
  2. Generative AI takes things one step further by using data to create or summarize.
  3. Conversational AI transforms how we interact with technology by using machine learning and natural language processing (NLP) to better derive our intent and context.

Each type of AI has a core purpose. When B2B sales organizations are able to bring them together to drive sales, they hold the key to success.

Examples of predictive AI in B2B sales

Predictive AI in B2B sales helps sellers and sales managers understand the state of business and where to focus.

Lead scoring is a great example and uses current and historical data to provide an indicator of lead strength or health. At a glance, sales representatives have insight into their lead and clear visual cues act as an alert if the lead is moving in the wrong direction. These insights aren’t only informative, but guide actions and improve seller focus.

Intelligent forecasting is another great example and uses real-time data to provide a data-driven projection of business that’s expected to close within a designated time frame. As the quarter progresses, sales managers need to know if they are going to hit their number. If the projected data doesn’t align with a seller’s manual roll-up, sales managers are able to take proactive action to investigate or get involved before the deal slips.

Predictive AI in B2B sales is a critical component of intelligent selling, and when paired with generative AI, can take B2B sales to the next level.

Generative AI streamlines selling

Each B2B buyer is distinct and has specific needs. Understanding this complexity is where high-performing sellers come into play, but time and focus are always factors. Generative AI for B2B sales has the ability to help remove complexity and lighten a seller’s workload.

Because B2B sales cycles are often complex and normally have several stakeholders, each with distinct concerns and desires, things tend to move and change quickly. Here’s where Generative AI for sales shines, by using the latest data to collate, generate, and provide customer summaries, deal highlights, key talking points, and similar information directly to the seller. These insights are contextual and directly related to the workspace the seller is using, so they always have the latest information to guide their actions.

But generative AI can also take on part of a seller’s workload by using data to generate contextual call scripts or craft customer communication. This generated content then acts as a first draft, allowing the seller to fine-tune with their expertise or even re-generate to adjust for preferences like length and tone.

Generative AI operates as a teammate and can dramatically increase how quickly a seller can communicate with customers.

Using conversational AI for better sales outcomes

Conversational AI in B2B sales pulls everything together and allows users to better interact with technology to improve outcomes. It uses deep learning and large language models to engage, draw insights, and provide information from natural speech or text.

This could be a digital sales assistant that sellers can chat with to answer questions or locate critical information. It also can be a customer-facing chatbot that captures customer intent from a sales call transcript or converts a chat inquiry directly into a sales lead.

Conversational AI enhances sales processes because it helps a user naturally interact with technology as they would a person, which is often more intuitive and easier.

When sales organizations harness these three types of AI with a holistic approach, they can make their sales processes much more efficient and opportunities less complicated. By uniting predictive, generative, and conversational AI at data, process, and user levels, sales organizations can maximize their B2B AI sales initiatives.

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Frequently asked questions (FAQs):

Generative AI focuses on creating new content, such as images, videos, music, or text, using models like Generative Adversarial Networks (GANs). It is widely used in creative fields and content creation to produce novel outputs that mimic human-like patterns. Predictive AI analyzes historical data to forecast future events or trends. It employs techniques such as regression analysis and neural networks and is commonly applied in finance, healthcare, and marketing for data-driven decision-making.

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