Impact Pricing Blog

Pricing begins with understanding why buyers choose to make a purchase in the first place. Buyers don’t purchase AI because it is powerful or innovative. They buy because it solves a problem they care deeply about. We refer to this as a foundational problem: a problem so significant that buyers are willing to pay for a solution.

Market segmentation is the practice of grouping buyers by their foundational problems. In AI, this step is critical. Since the same AI technology can solve many different problems, segmentation provides the clarity needed to decide which problems you are solving, for whom, and at what value.

Segmentation and the Value Architecture

In our value architecture, segmentation sits at the top because it determines the scope of the market and defines the foundational problem. Everything else flows from here:

• Packaging determines how the solution is structured for that segment.
• Pricing metrics determine how buyers will pay for that solution.
• Positioning and communication determine how we describe the value.

Each of these decisions are best made for a market segment.  Without segmentation, companies risk trying to sell a capability rather than a solution. A platform without segmentation may look impressive but leaves the burden on buyers to discover use cases. Solutions, by contrast, are intentionally packaged for a segment’s foundational problem.

Examples of Segmentation in AI

AI is incredibly flexible, but segmentation shows how the same underlying technology can become different solutions when aimed at different foundational problems.

Nuance (Microsoft) — Speech Recognition and Natural Language Processing

• Healthcare segment: Physicians’ foundational problem is spending too much time on documentation, which limits patient care and contributes to burnout. Nuance’s Dragon Medical One solves this by automating clinical note-taking, increasing productivity and freeing up physician time.
  
• Financial services segment: Banks’ foundational problem is fraud risk and poor customer experience in authentication. Nuance applies voice biometrics for secure, seamless logins that improve trust and reduce fraud.
  

Palantir — AI-Driven Data Integration and Analysis Platform

• Defense segment: Intelligence agencies’ foundational problem is analyzing vast, fragmented datasets to identify threats in time to act. Palantir packages its platform into mission-planning and threat-detection solutions.
  
• Healthcare segment: Hospitals and public health agencies’ foundational problem is operational planning under uncertainty. Palantir provides tools for capacity management, resource allocation, and pandemic response.
  

Cresta AI — Real-Time Conversation Intelligence

• Customer support segment: Call centers’ foundational problem is high costs and long handle times. Cresta applies AI to reduce average handle time and improve customer satisfaction. Notice that this single market segment spans industries such as e-commerce, financial services, and telecom.
  
• Sales segment: Sales teams’ foundational problem is low conversion rates and inconsistent performance. Cresta provides real-time coaching during calls, lifting win rates and revenue.

Key Takeaway

Even when the underlying AI capability is identical, segmentation matters because buyers only pay for solutions to their foundational problems. Defining these problems clearly is the first step toward effective packaging, pricing, and positioning.

Platforms vs. Solutions in Segmentation

Platforms, like ChatGPT, often resist segmentation. They offer a wide range of capabilities that could be applied to countless problems. This flexibility is powerful, but it places the burden of solving valuable problems on the buyer.

Solutions, like Nuance’s Dragon Medical One or FinAI’s call resolution model, are inherently segmented. They succeed by targeting a specific foundational problem and creating a clear path for buyers to see the value in their solution.

The choice between platform and solution is not about technology, it’s about focus. A platform without clear segmentation risks being too general to monetize effectively, while solutions generate traction by aligning directly with a buyer’s “why.”

The Risk of Over-Segmentation

While segmentation is critical, there is a danger in slicing too narrowly. If the defined segment is too small or the foundational problem is not urgent enough, the company may build a precise solution for a market segment that can’t support a business.

This is especially relevant in AI, where the same core model can be aimed at dozens of different problems. Companies must balance the desire for focus with the need for sufficient market size.

Why Segmentation Matters Most

At its core, segmentation is about recognizing that the foundational problem is why buyers buy. Each segment represents a group of buyers united by the same underlying problem, whether that’s reducing physician documentation time, cutting call center costs, or securing sensitive transactions.

Defining segmentation sets the stage for all our other decisions: how we package, how we price, and how we communicate value. With clear segmentation, AI companies can move from selling a technology to selling a solution, and that’s when buyers truly see the worth.

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