The artificial intelligence arms race is in full swing. Software as a service (SaaS) providers in every niche are thinking about launching AI offers.
Generally speaking, it’s best to be early in the world of SaaS, even if the released product has a few imperfections. However, AI as a service (AIaaS) is largely uncharted territory, and many companies are still trying to figure out how to monetize this emerging technology.
Billing customers annually in advance is an appealing option when dealing with a known commodity. But all the mystery around the costs of AI makes the annual billing model impractical.
Instead, SaaS companies should make consumption-based pricing the center of their go-to-market (GTM) strategies. Consumption pricing is the fairest option for end users and the most pragmatic for vendors who can’t predict their cost of goods sold (COGS) with any level of reliability. Let’s take a closer look at why.
For years, SaaS companies reaped the benefits of lightweight pricing models that were extremely easy to manipulate. The four basic levers in the standard SaaS pricing model are:
Most SaaS companies simply manipulated one of these levers to achieve their desired margins. Alternatively, they could make incremental changes to multiple variables to reduce the perceived burden of price increases while maintaining stable cash flow.
Although a consumption-based model makes the most sense for AI monetization, achieving profitability with this method is more involved than simply mixing and matching these pricing levers. Instead, AIaaS providers will need to implement strategies and technology that enable them to accurately meter usage, quantify value, and accurately price, bill, and recognize revenue.
Many vendors struggle to monetize based on the actual value delivered to customers by a particular AI service. For example, some customers may use the tool or product multiple times daily, while others use it a few times a week, making a per-seat pricing model ineffective at delivering on a clear exchange of value with the customer.
Businesses that traditionally charge on a per-user or per-seat basis will potentially hemorrhage profits if they apply this rudimentary system to AI.
On the other hand, a consumption-based model will allow you to serve both types of customers at a fair price. The flexibility of consumption means it can scale costs according to usage frequency and enables vendors to quickly adapt to changing back-end costs.
Recent Subscribed Institute and Boston Consulting Group (BCG) joint research indicates that consumption-based pricing models are also becoming more valuable and relevant due to the generative AI boom—customers considering gen AI solutions report preferring usage-based pricing.
And when it comes to data, AI and consumption make a great pairing. Successful implementation of a consumption-based pricing model requires clean, accurate, auditable, and defensible data. If your product team is launching an AI product, chances are they’re already collecting, measuring, and translating the very data you’ll need for a consumption model rollout.
The consumption model is far from perfect. Familiarizing yourself with the advantages and disadvantages of this payment model will help you determine how to work it into your GTM strategy.
The benefits of a consumption-based pricing model are clear:
However, there are some common challenges associated with consumption-based pricing:
Despite these concerns, consumption-based billing is still clearly the most pragmatic solution for AI monetization.
Launching an AI product and monetizing it requires a cohesive GTM strategy. While your strategy must align with your organization’s overarching needs and goals, there are some essential truths you need to keep on your radar when building your GTM plan.
Knowing your customer is job one for a modern business. If you don’t have a fundamental understanding of your customer and their needs, your GTM strategy will fall flat.
Get more data-driven and obtain granular insights into your customers. If you already have solid customer data, build on that network of information to learn even more about your target audience. The more you know about your customers, the more precisely you can price and package your AI offerings.
You also need to know what sort of value your product is delivering for your customers. Consider how artificial intelligence fits into your value proposition. If adding AI to your offer doesn’t magnify the value you provide, it may not be the right move for your business, even if it’s the popular thing to do.
SaaS companies everywhere are clamping down on spending and consolidating technology. You need to engage in continuous value realization to ensure your new AI offering is not part of these cuts.
As a GTM organization, and particularly with a consumption model, you must constantly be justifying why your client should keep using your product or service by demonstrating the ROI it provides.
To achieve this, you have to zero in on the consumption value metric that makes the most sense for your service and customer. And, as we’re seeing in so many arenas today, AI is forcing companies to reconsider how they approach this challenge.
For instance, generative AI services may be metered based on “number of interactions.” But to improve the value delivered to the customer, a better value metric may be “number of successful interactions.”
Integrating AI into your product offer requires introspection. While you may not always like the answer, be willing to think outside the box and question your beliefs, especially when it comes to your pricing model.
Focus on cultivating a flexible monetization strategy that includes multiple levers. A multi-attribute consumption model gives you the freedom to adjust pricing and packaging as you learn more about how your customers are using your product.
For instance, it often makes sense to onboard customers with a simple pay-as-you-go model, but adding a level of commitment in exchange for a better price will increase the value for the customer and predictability for the business.
Creating a more nimble consumption pricing model requires customizable mediation and metering technology. To monetize AI, you need a flexible solution that reduces financial complexity, gives customers real-time visibility into their usage, and allows product managers complete pricing flexibility.
With flexible, scalable pricing model management solutions, you can lay the foundation for successful AIaaS monetization.
Explore the strategies from successful consumption businesses and delve into the ways in which they are addressing potential drawbacks in this future of consumption webinar.