Total Monetization: The Impact of AI on Software Pricing and Packaging
AI, particularly generative AI, is having an outsized impact on everything from art to green tech, and dominates most tech conversations lately (well, over the last 18+ months anyway). The one area that I don’t hear enough about is how technology providers, particularly SaaS and cloud providers, are scrambling to figure out new pricing and packaging models that will capture the value the new solutions bring while ensuring that margins are not disrupted (or maybe I should say destroyed) with the high costs of operating and scaling AI offerings, particularly generative AI. By offering advanced AI functionalities, companies can differentiate their products in crowded markets. This differentiation can be a key driver in packaging design, helping companies to highlight unique features that justify premium pricing or attract a specific segment of customers.
The integration of AI technology may involve significant upfront investments in research and development, AI training, and computational resources as well. These costs can influence the pricing strategy, potentially leading to higher prices to offset the initial and ongoing expenses associated with maintaining state-of-the-art AI capabilities. For many providers (with some notable exceptions we’ll look at later in the post) the current subscription pricing model is not sufficient enough to capture the new innovation, recoup the increasing costs and at the same time maintain the simplicity, predictability and transparency that customers insist on.
This past week I attended Zuora’s Subscribed Live Bay Area 2024 in Berkeley, CA. Subscription management platform provider Zuora has long been a thought leader in the “subscription economy” led by founder and CEO Tien Tzuo. It’s probably not surprising that the agenda for the event was taken over with discussions on how AI is changing current pricing and packaging strategies, and how Zuora has changed to help vendors enable new, more appropriate mixed monetization models. They introduced a new way to think about your pricing and packaging strategies, something they call “total monetization”. You may think that this is another marketing term, but the more I think about it, I believe it might be the best way to think about the overall strategy. While there’s some inherent complexity in these new models, taking a comprehensive approach to pricing and packaging could help capture the value of the new innovations, recoup the increasing costs and at the same time maintain some predictability and transparency for customers.
(Note: this isn’t intended to be a review of the event or Zuora’s capabilities, which have been enhanced greatly lately through some interesting acquisitions as well as continued organic innovations. I did publish a look at their acquisition of Togai here, which is relevant to this discussion).
Embedding Generative AI into Enterprise SaaS
Embedding generative AI into enterprise SaaS applications can have several impacts on pricing and packaging strategies. Here are some key considerations:
Tiered Pricing Models
Companies might introduce tiered pricing structures to differentiate between levels of AI functionality. For instance, basic packages might include limited AI capabilities, while premium tiers could offer more advanced AI features like deeper analytics or more sophisticated automation. This allows businesses to cater to a wider range of customers with varying needs and budgets.
Targeting Different Usage Levels:
Free/Freemium Tier: This tier offers a limited set of generative AI features, often with restrictions on usage (e.g., number of outputs, basic functionalities). It serves as a trial to attract users and showcase the technology's potential. And of course it supports a product led growth strategy (PLG).
Paid Tiers: Higher tiers offer increased access to generative AI capabilities. This could include more outputs, advanced features (like content quality control, specific content formats), or priority processing.
Balancing Value and Cost:
Tiered Pricing Based on Usage: SaaS vendors might base pricing on the amount of generative AI features a user consumes. This could be per-generated image, word count for written content, or number of code lines. This aligns cost with the value derived from the AI features.
Tiered Pricing Based on Features: Alternatively, pricing could be based on the specific generative AI features unlocked in each tier. For example, a tier might offer basic text generation, while another unlocks more complex features like image generation or code writing.
Focus on User Segmentation:
Catering to Different User Needs: SaaS vendors can tailor their tiers to distinct user segments. A basic tier might target individual users or small businesses, while higher tiers with advanced features cater to larger enterprises with more complex needs.
The market and models are still evolving, so pricing models will likely adapt and change. However, tiered structures offer a flexible way for SaaS vendors to capture value from generative AI while catering to a diverse user base.
Additional Considerations:
Transparency is key: Clearly communicate the limitations and functionalities of each tier to avoid user frustration.
Focus on value delivered: Ensure each tier offers a clear value proposition that justifies its cost.
Usage-Based / Consumption-based Pricing
Generative AI can lead to usage-based pricing models where customers pay according to the volume of AI interactions or the computational resources consumed. For example, a SaaS application could charge based on the number of AI-generated reports, queries processed, or the complexity of the data analysis performed.
Key Characteristics
Pay-As-You-Go: Customers are billed based on their actual usage of the service. This can be measured in various units such as API calls, data storage, or number of transactions.
Flexibility: This model allows customers to start with minimal usage and scale up as needed, making it attractive for businesses that want to avoid high upfront costs.
Alignment with Value: Pricing is directly tied to the value customers derive from the product, which can lead to higher customer satisfaction and retention.
Scalability: It supports business growth by allowing customers to increase their usage without renegotiating contracts.
Implementation Details: The specific metrics used to measure usage can vary. For instance, some models might charge per gigabyte of data used, while others might charge per API call or per user action.
Hybrid Models: Some companies combine usage-based pricing with traditional subscription models to offer more predictable revenue streams while still capturing the benefits of usage-based billing.
This model offers flexibility, aligns costs with value, and supports scalability, making it a popular choice for modern SaaS and cloud services.
Value-Based Pricing
With the integration of generative AI, SaaS products can deliver enhanced capabilities like predictive analytics, personalized content, and automation of complex tasks. This added functionality can increase the perceived value of the software, allowing companies to adopt value-based pricing models where prices are set based on the perceived worth to the customer rather than solely on costs.
Regulatory and Ethical Considerations
Depending on the industry and location, there might be regulatory requirements regarding the use of AI, which could impact how features are packaged and priced. Additionally, ethical considerations about AI use (e.g., transparency, fairness, privacy) could influence customer trust and willingness to pay for AI-enhanced services.
Scalability and Flexibility
Generative AI can enhance the scalability of SaaS applications, allowing them to serve a broader range of customer needs and adapt to changing conditions. This flexibility can be reflected in the packaging, offering customers customizable options that scale with their growth.
Overall, the inclusion of generative AI in enterprise SaaS applications can lead to innovative pricing and packaging strategies that reflect the added value, manage cost recovery, encourage adoption, and maintain competitive advantage. Focusing on total monetization as a strategy can lead to more effective and customer friendly models.
Is AI Table Stakes?
While a somewhat contentious concept, some Enterprise SaaS application vendors believe that generative AI is becoming table stakes and that it should just be included in the base subscription rather than some new pricing and packaging models. This belief can be attributed to several strategic and market considerations:
Competitive Differentiation: In a highly competitive market, providing generative AI capabilities as part of the standard offerings can differentiate a vendor’s product from others that might charge extra for similar features. This could make the product more attractive to potential customers who see more value in a comprehensive, all-in-one package.
Market Expectation and Standardization: As generative AI technologies become more pervasive and integral to various business processes, they may start to be viewed not as optional extras but as essential components of enterprise software. Vendors might anticipate that customers will expect these capabilities to be standard, much like how security features or mobile support have evolved from add-ons to baseline expectations.
Simplified Pricing and Sales Process: Integrating generative AI into the base subscription simplifies the pricing structure and makes the purchasing process easier for customers, who might be deterred by complex pricing schemes or the perception of being "nickel and dimed." A simple, transparent pricing model could lead to quicker sales cycles and higher customer satisfaction.
Increased Adoption and Usage: By including generative AI features in the base package, vendors encourage widespread use of these features among their customer base, which can drive broader adoption and deeper integration of their platform into the customer’s daily operations. This can lead to higher customer retention and potentially more upsell opportunities as the value delivered through the platform increases.
Driving Innovation: Encouraging the use of AI within their platforms, vendors can collect more data on usage patterns, which can be invaluable for training AI models and improving the product. This iterative cycle of usage and improvement can accelerate innovation and maintain the vendor's leadership in the market.
Regulatory and Ethical Alignment: As discussions around AI ethics and regulation continue to evolve, including AI capabilities as part of the standard offering might also help vendors ensure compliance and maintain ethical standards across their customer base, rather than having disparate levels of compliance depending on who opts in or can afford additional features.
By viewing generative AI as a standard component rather than an optional add-on, SaaS vendors might accelerate adapting to a future where AI is seen as an indispensable part of enterprise software, much like data analytics or user experience enhancements are today. I only know of two examples of leading enterprise providers that are going to market in this way, Oracle and Zoho, adding in many new AI and generative AI capabilities without increasing subscription prices or adding in usage-based models. There is something unique about those two vendors though, they both “own” their entire stack including compute and infrastructure components. That changes the economic model around generative AI / AI quite a bit, enabling them to scale with less margin impact. The market is evolving quickly and will continue to change over time, perhaps others will join in this approach. Time will tell.
