The path to generative pricing
Steven Forth is a Managing Partner at Ibbaka. See his Skill Profile on Ibbaka Talio.
Generative pricing is a new approach to pricing that is fit for second generation generative AI applications. These applications are built on Large Language Models, us LangChain or an equivalent to sequence actions, and tend to have conversational interfaces.
Ibbaka has published four key blog posts to help you get oriented and begin to execute with generative pricing.
Generative Pricing (where we introduce the approach)
Pricing approaches for generative AI applications (where the alternatives are considered)
The path to generative pricing (where we introduced the five step approach to generative pricing)
Steps on the path to generative pricing (where we expand on the five steps)
Key characteristics of second generation generative AI applications
First generation generative AI applications were mostly extensions to existing applications. They made incremental improvements to existing value drivers but did not really change the game. There were some interesting pricing innovations. Intercom took a big step towards outcome-based pricing, see AI applications edge toward outcome based pricing. And Box also showed some creativity in designing flexible pricing for its AI. But in general, applications were routine and pricing mundane.
SaaS growth expert Kyle Poyar called this out, noting “Lack of pricing innovation presents an opportunity for the second wave of AI apps.”
We. are now seeing a second wave of generative AI apps that are much more disruptive. Some characteristics of these applications are deep integration, enhanced capabilities (enabled by approaches such as ReAct, or Reason Action, and LangChain) and the emergence of AI based strategic data platforms.
Deep Integration: Unlike first-generation AI systems, which often involve bolted-on solutions, second-generation AI applications are built on comprehensive LLMs and neural networks tailored for specific domains. This allows for more seamless and efficient integration into existing workflows and systems.
Enhanced Capabilities: These applications go beyond basic automation and content generation, offering advanced functionalities such as skills inference, job-to-candidate matching, internal mobility, and pay equity analysis in HR.
Strategic Data Platforms: Second-generation AI platforms are designed to act as strategic data platforms that can complement or even replace traditional transaction systems. They provide deeper insights and more accurate predictions by leveraging vast amounts of data.
Some examples of second generation generative AI applications are given below.
Allium: https://www.allium.so
AISDR: https://aisdr.com
Induced: https://www.induced.ai
Klarity: klarity.ai
Manaflow AI Operations: https://manaflow.ai
Narrative: narrative.io
Orby AI Automation: https://www.orby.ai
Respell: https://www.respell.ai
Sensible: sensible.so
Synthetic Users: https://www.syntheticusers.com/
Here is what Perplexity thinks these companies have in common.
Focus on Automation and AI
Hyperautomation
No-Code/Low-Code Platforms
Customer-Centric Solutions
Data-Driven Insights
Scalability and Flexibility
The need for generative pricing
Ibbaka believes that generative AI will need a new approach to pricing, generative pricing. This pricing will need to be responsive, explainable and value based.
Responsive pricing is pricing that changes to reflect rapidly adapting applications, that change configuration in response to real time user needs. Application configuration will not be static but will change in response to the user’s needs and the operating environment. Static pricing will not work.
Is this dynamic pricing? Not as currently understood. In dynamic pricing demand and market data is used to set the price based on an estimate of willingness to pay. Responsive pricing is different. It responds to changing product configurations and is based on sharing value and not on willingness to pay.
Explainable One of the criticisms of dynamic pricing has been a lack of transparency. This is a criticism of AI generally. In generative pricing the price changes with the configuration and the configuration changes in response to the user’s needs and environment. For buyers to accept this approach they will need to be able to understand and agree to any price/configuration changes. Pricing transparency will be critical to acceptance.
Value Based What will the configuration/price be baed on? Value. Specifically the economic value being provided. Generative pricing will be built on value models. These models will need to be a lot more sophisticated than what is typical today. ROI calculators locked up in spreadsheet will not do the job. These value models will themselves be made and updated using generative AI. They will be connected to configuration management applications, also built on generative AI, and both the value model and configuration manager will be connected to the customer facing application and plugged into many different data sources.
Steps to generative pricing
Over the next five years most B2B software will be built on generative AI platforms. This is where we are going, but most of us will not jump to that in one great leap. We need a path where we can begin my taking small steps. What can companies do today to prepare for generative pricing and get some early benefits?
Build a value model
Manage functionality using feature flags
Map features to value
Design pricing that lets you change pricing as you switch features on and off
Connect pricing to configuration management
Build a value model
You won’t get far without a formal value model. And these value models have many uses beyond setting you up for generative pricing.
A good value model can contribute to many different business processes. According to Perpleixty these include …
Pricing Strategy Development
Value Communication
Customer Segmentation
Product Development and Management
Financial Metrics and Performance Measurement
Competitive Analysis
Manage functionality using feature flags
Feature flags, also known as feature toggles, are a powerful technique in software development that allow developers to enable or disable features in an application without deploying new code.
Feature flags enable targeted releases, where features are activated for specific user requirements. This allows for personalized user experiences and controlled feature rollouts. Exactly the direction that generative AI applications are headed.
Use of feature flags also helps development and product management teams understand and communicate what is a feature in the application, something that is not always clear.
Map features to value
Once you have a list of features, that you can toggle on and off, and a set of value drivers (value models are built from value drivers), you can map features to value drivers using a DSM or Design Structure Matrix. Understanding the relationships between features and value is the key to value based configuration.
Design pricing that lets you change pricing as you switch features on and off
Now that you have mapped feature to value you can design a pricing model where price changes depending on what features are included. The change in price is based on the change in value which reflects the features included. Once you can do this you are more than halfway to generative pricing.
Connect pricing to configuration management
The final step is to connect the value based pricing to configuration management. There are many configuration management applications available, from Chef for those in the Ruby on Rails world to Microsoft Intune for those in the Microsoft world. Configuration management is a hot and rapidly evolving software category, one that will be critical to generative AI applications and generative pricing.
Take baby steps to generative pricing
Generative pricing will be the best practice for pricing B2B SaaS over the next decade. Even small steps, like having a value model and using feature flags can have a big impact on your business. Start small, gather data, validate in the field and iterate. The value based mindset is a change for many organizations, where sales is often reacting to competitor moves rather than shaping value for the customer and customer success is focussed on soft metrics like NPS (Net Promoter Score). Moving to a value mindset will transform your business.
A value mindset involves a strategic focus on understanding and delivering measurable value to customers, which enhances customer satisfaction, retention, and overall business growth. This approach requires a deep understanding of customer needs, effective communication of value propositions, proactive customer success strategies, and value-based pricing models.