B2B pricing in the brave new world
AI made a seismic shift. This shift calls for new pricing models that can keep pace with the change and competition. Pricing, more than just a number, is pivotal in shaping a company’s trajectory - its success or failure hinges on getting this right.
We are going to explore the need for the new pricing model, evaluate current ones, and suggest new pricing models to adapt. It helps me sort out my thoughts around this topic and I hope it will do the same for you.
Brave new world
The new world is shaped by two pivotal shifts (the Internet and AI). For the past three decades, The hallmark of successful companies has been their ability to leverage the Internet’s economic change: the effective zero cost of distribution and transaction. Ben Thompson has repeatedly emphasized this point [1]:
“...the Internet has completely transformed business by making both distribution and transaction costs effectively free. In turn, this has completely changed the calculus when it comes to adding new customers: specifically, it is now possible to build businesses where every incremental customer has both zero marginal costs and zero opportunity costs….’’
This paradigm shift favored broad, scalable products. Consider Slack: its ability to seamlessly integrate into various workflows across multiple industries not only enhances its utility but cements its role as foundational to business operations, exploiting the shift by the Internet.
GenAI: redefine productivity
AI, particularly GenAI, is reshaping the landscape by significantly lowering the cost of productivity, particularly in fields requiring extensive knowledge and implementation work. AI introduces a new economic reality: tasks, simple or complex, are now orders of magnitude cheaper to perform with AI than with human labor. This shift in cost dynamics is not merely about making things cheaper - it’s about redefining the economic viability of a vast range of tasks, enabling more work to be pursued that were previously cost-prohibitive.
Sarah Tavel highlights this shift in AI startups from selling software to selling work. She uses EvenUp as a case study [2]:
“...Take EvenUp as an example (who I have no doubt will dominate their vertical). If you are a personal injury lawyer, a work product you create on behalf of a plaintiff is called a demand package. …If you were still in the mindset of selling software, you could imagine a software offering for personal injury law firms, sold on a per-seat basis, …. Instead, EvenUp had the foresight to sell the work product itself: the demand package…”
In the AI-driven landscape, the most successful products offer complete jobs-to-be-done from start to finish. They do more than merely perform tasks; they integrate into how businesses operate, becoming indispensable. This transformation towards a results-oriented business model is compelling companies to rethink and adapt their pricing strategies to better align with the demands.
How does current pricing models stack up
As the business landscape is reshaped by the Internet’s zero distribution cost and AI’s dramatic reduction in productivity costs, traditional pricing models are facing new challenges and opportunities. Let’s break down how each model stacks up.
Per-Seat Pricing
What it is: Charges are based on the number of users within an organization, similar to renting a tool like a carpet steamer from HomeDepot. It encourages frequent use as there are typically no usage caps.
Example: Github Copilot, at $10 per user per month [3], offering unlimited code completions to boost developer productivity.
Leverage: Simplicity and scalability make this model a fit for businesses looking to expand their user base quickly. It’s plug-and-play style, aligning well with software tools that scale.
Drag: The one-size-fits-all approach struggles as user demands evolve. A heavy user pays the same as an infrequent one, which can misalign costs with actual value received. And as AI drives more bespoke usage patterns, this model may fail to capture nuanced values, limiting both customer and potential revenue optimization.
Usage-based Pricing
What it is: This model charges based on measurable actions, like the volume of data processed or transactions completed.
Example:
Output: Zapier charges based on how many times an automation is executed.[4]
Input: LindyAI bills based on token consumed. [5]
How it would fare in the new world: Usage-based pricing promotes transparency but focuses quantity over quality. As demand shifts towards more value-driven outcomes, this model needs to evolve to stay relevant.
Leverage: This model excels in transparency and fairness, making it appealing in data-driven environments where every action can be measured. It aligns cost directly with use, ideal for customers who prefer a pay-as-you-go approach.
Drag: The model’s focus on quantity, like data processed or transactions completed, overlooks the quality of outcomes. In an AI-driven world where customer demands increasingly prioritize effectiveness and tailored results, this approach is failing to align with those expectations.
Outcome-based Pricing
What it is: Instead of charging by volume, this model charges based on the results achieved, linking costs directly to customer success.
Example: Consider Intercom’s chatbot, Fin is an example of this. Intercom charges 99 cents per successful resolution [6], assuming the responsibility for both the iterations and effectiveness of each outcome.
Leverage: Outcome-based pricing is aligned with the AI + Internet era, focusing on results. It inherently aligns provider incentives with customer success.
Drag: This model places burdens onto providers to not only deliver but also to precisely measure success. The risk of shifts significantly from customer to provider. This can be challenging in complex or subjective fields where defining and agreeing on success criteria is difficult.
Capacity-based Pricing
What it is: The capacity model charges based on the amount of time allocated per month, letting users utilize the service within that time as they see fit.
Example: WithDouble, a EA(executive assistant) platform allows users to select plans based on the amount of time they want to allocate to them [7]. Within that time, their EA can perform any request tasks. Additional time can be purchased if needed.
Leverage: Capacity-based pricing offers flexibility and aligns well with variable workloads, making it well-suited for the new era. This model adapts to a wide range of tasks without the need for frequent plan adjustments.
Drag: The flexibility is a double-edged sword. Unused capacity can feel like waste, turning away customers looking for outcome-driven value. Scaling this model is tricky, sudden spikes in demand can overextend resources, compromising service quality as providers scramble to adjust.
Navigating the future with a hybrid pricing model
We're rethinking pricing with a model as flexible and capable as your best employee. It starts straightforward: pay for outcomes, like a commission. Once we demonstrate value, we introduce knowledge management as a subscription service. This isn't merely an added expense; it's a strategic investment. It gathers and manages your unique insights, addressing current challenges and guiding future decisions, turning raw data into actionable decision data. This model ensures that every dollar spent enhances your company's ability to act decisively and strategically.
How to implement
Outcome First: Initially, focus is on delivering concrete results. Customers only pay for the outcomes. This setup reduces initial risk for customers.
Knowledge Subscription Second: After proving our value and establishing trust, we roll out a knowledge subscription model. This setup not only offers predictable billing and consistent access to our services but also the active management of the contextual knowledge.
As we deliver projects using the outcome model, our service captures and catalogs the tribal knowledge traditionally held by human workers. Consider a software company where the crucial know-how, from coding standards to debugging tricks, lives mainly in the heads of a few senior developers. This approach guarantees smooth transitions when key staff leave or new employees join, preserving and enhancing operational continuity. Think of it as having a team that not only works on your projects but also safeguards and leverages your unique business insights.
Why this model works
For customers: They benefit from a pricing model that mimics the best aspects of hiring a versatile and reliable employee. The outcome-based model reduces risk, while the subsequent knowledge subscription ensures operational stability, while also safeguarding and capitalizing on the accumulated expertise.
For providers: This approach smooths customer onboarding with a focus on quality and successful outcomes. The transition to the knowledge subscription phase offers stable revenue and fosters deep, ongoing customer relationships built on a thorough understanding of their business needs and challenges.
Why this model won’t works: The biggest issue with this model is the complexity of measuring success. When you base pricing on outcomes, you’re inherently tied to being able to define and measure those outcomes clearly and convincingly. This becomes a real challenge when results aren’t cut and dry, which is often the case in more nuanced or creative fields.
What’s old is new
This pricing model may seem familiar because it’s inspired by the proven practices of consulting firms and design studios, where fees directly reflect the quality and scope of the work delivered. Retainers further ensure ongoing resource allocation and knowledge accumulation, effectively linking price to both immediate value and sustained partnership.
Wrapping up: pricing for the future
The right pricing model is just as crucial as the product itself in building a successful business. As we move forward, I’m excited to continue to battle test our pricing model. At Isoform, we’re starting with integration engineering to validate our hybrid pricing model. Interested in our journey or want to learn more? Reach out to me (bo at isoform.ai) or follow our updates at isoform.ai.
Links:
[1]: https://stratechery.com/2015/beyond-disruption/
[2]: https://www.sarahtavel.com/p/ai-startups-sell-work-not-software
[3]: https://github.com/features/copilot/
[4]: https://zapier.com/pricing
[5]: https://www.lindy.ai/pricing