What does generative AI think about generative pricing?
Steven Forth is CEO of Ibbaka. See his Skill Profile on Ibbaka Talio.
In our post Will generative AI require new approaches to pricing? we concluded that the answer is Yes and that this approach could be characterized as generative pricing.
There are emergent properties in second generation generative AI applications. These include
Dynamic configuration (like Totogi)
Conversational interfaces (the chatbot takes over the UI which is where Copilot is headed)
Synthetic data (like Synthetic Users)
Real time integration (through RAGs and seen in applications like those coming from Qlik)
Pricing for generative applications will need to support these emergent properties. I think dynamic configuration will be the biggest challenge, it will require a completely new way of thinking about configure - price - quote.
Generative pricing will need to be built on generative AIs and other more mathematically savvy AIs (Wolfram Alpha has some relevant capabilities). In this spirit, we began by asking Perplexity some questions about generative pricing.
If you have access to Perplexity you can follow the whole thread here.
Prompt: What is generative pricing?
Generative pricing is a concept that involves using generative AI to create new pricing models and strategies. According to the article from Ibbaka, generative AI can analyze vast amounts of data and generate new insights that can be used to develop innovative pricing approaches. This method leverages the capabilities of AI to understand complex patterns and relationships within the data, which traditional pricing models might miss.The article discusses how generative AI can help businesses move beyond conventional pricing strategies by considering a wider range of factors and scenarios. This includes dynamic pricing, personalized pricing, and value-based pricing, all of which can be optimized using AI-generated insights. The goal is to create pricing models that are more responsive to market conditions, customer behavior, and competitive dynamics, ultimately leading to more effective and profitable pricing strategies.In summary, generative pricing uses the power of generative AI to develop new and more sophisticated pricing models that can adapt to the complexities of modern markets. This approach aims to enhance the accuracy and effectiveness of pricing decisions by leveraging advanced data analysis and pattern recognition capabilities of AI.
Follow Up: How does generative pricing differ from traditional pricing models?
Generative pricing, driven by generative AI, represents a significant departure from traditional pricing models. Here are the key differences:
Data Utilization and Analysis
Traditional Pricing Models: These often rely on historical data and predefined rules to set prices. Techniques such as cost-plus pricing, competitive pricing, and value-based pricing are common. These models are generally static and may not adapt quickly to changing market conditions.
Generative Pricing: Utilizes generative AI to analyze vast amounts of data, including real-time market trends, customer behavior, and competitor pricing. Generative AI can create new pricing strategies by identifying complex patterns and relationships within the data that traditional models might miss. This leads to more dynamic and adaptive pricing strategies.
Flexibility and Adaptability
Traditional Pricing Models: Typically static and less responsive to real-time changes. Adjustments are often made periodically based on market analysis and competitive benchmarking.
Generative Pricing: Highly dynamic and can adjust prices in real-time based on continuous data input. This adaptability allows businesses to respond swiftly to market fluctuations, customer preferences, and competitive actions, optimizing revenue and profitability.
Personalization
Traditional Pricing Models: Generally apply uniform pricing strategies across customer segments, with limited personalization.
Generative Pricing: Enables personalized pricing by tailoring prices to individual customers based on their perceived value of the product, engagement levels, and purchasing behavior. This can enhance customer satisfaction and loyalty by offering prices that reflect individual value perceptions.
Technological Integration
Traditional Pricing Models: Often rely on simpler algorithms and manual adjustments. The integration of advanced technologies is limited.
Generative Pricing: Leverages advanced AI techniques such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to enhance data diversity and accuracy. These technologies enable the generation of synthetic data points, improving demand forecasting and pricing precision.
Strategic Approaches
Traditional Pricing Models: Use established methods like cost-plus, competitive, and value-based pricing. These methods are well-understood but may not fully capture the complexities of modern markets.
Generative Pricing: Incorporates innovative strategies such as token-based pricing, per-word pricing, and add-on feature pricing. These strategies are particularly suited for AI-driven products and services, reflecting the unique value and usage patterns of generative AI applications.
Examples and Applications
Traditional Pricing Models: Common in industries with stable demand and well-defined cost structures, such as manufacturing and retail.
Generative Pricing: Particularly effective in dynamic and data-rich environments like e-commerce, SaaS, and digital content creation. For instance, dynamic pricing in e-commerce adjusts prices based on real-time demand and competitor actions, while SaaS companies might use hybrid models combining subscription and usage-based pricing to reflect the value delivered by generative AI features.
In summary, generative pricing leverages the advanced capabilities of generative AI to create more dynamic, personalized, and adaptive pricing strategies compared to traditional models. This approach not only enhances revenue optimization but also aligns pricing more closely with customer value and market conditions.
Follow Up: What are the key benefits of generative pricing over traditional pricing models?
Generative pricing, driven by generative AI, offers several key benefits over traditional pricing models. These benefits stem from the advanced capabilities of generative AI to analyze vast amounts of data, identify complex patterns, and adapt to dynamic market conditions. Here are the primary advantages:
1. Enhanced Data Utilization and Analysis
Generative pricing leverages generative AI to process and analyze extensive datasets, including real-time market trends, customer behavior, and competitor pricing. This allows for more accurate and comprehensive insights compared to traditional models, which often rely on historical data and simpler algorithms.
2. Dynamic and Adaptive Pricing
Generative pricing is highly dynamic, enabling real-time adjustments based on continuous data input. This adaptability allows businesses to respond swiftly to market fluctuations, customer preferences, and competitive actions, optimizing revenue and profitability. Traditional pricing models, in contrast, are typically static and less responsive to real-time changes.
3. Personalization
Generative pricing enables personalized pricing by tailoring prices to individual customers based on their perceived value of the product, engagement levels, and purchasing behavior. This can enhance customer satisfaction and loyalty by offering prices that reflect individual value perceptions. Traditional models generally apply uniform pricing strategies across customer segments, with limited personalization.
4. Technological Integration
Generative pricing utilizes advanced AI techniques such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to enhance data diversity and accuracy. These technologies enable the generation of synthetic data points, improving demand forecasting and pricing precision. Traditional models often rely on simpler algorithms and manual adjustments.
5. Innovative Pricing Strategies
Generative pricing incorporates innovative strategies such as token-based pricing, per-word pricing, and add-on feature pricing. These strategies are particularly suited for AI-driven products and services, reflecting the unique value and usage patterns of generative AI applications. Traditional models typically use established methods like cost-plus, competitive, and value-based pricing, which may not fully capture the complexities of modern markets.
6. Improved Demand Forecasting
Generative AI enhances the accuracy and reliability of demand forecasting by using data augmentation techniques. This leads to more effective pricing strategies and improved business outcomes. Traditional models may not be as precise in forecasting demand due to their reliance on historical data and simpler analytical methods.
7. Cost Optimization
Generative AI can optimize costs by automating and streamlining various pricing-related tasks. This includes reducing the time and resources needed for data analysis, model training, and price adjustments. Traditional models often involve more manual processes, which can be time-consuming and less efficient.
8. Competitive Advantage
By leveraging generative AI, businesses can gain a competitive edge through more sophisticated and responsive pricing strategies. This can lead to increased market share, higher customer retention, and improved profitability. Traditional models may not provide the same level of competitive advantage due to their limitations in data analysis and adaptability.In summary, generative pricing offers significant benefits over traditional pricing models by utilizing advanced AI capabilities to enhance data analysis, personalization, adaptability, and cost optimization. These advantages enable businesses to develop more effective and competitive pricing strategies in today's dynamic market environment.
Conclusion
Generative pricing is an emergent approach to pricing that will grow along with second generation applications. If you are developing such an application, consider building generative pricing alongside it. This is something Ibbaka can help you with.
