One of my favorite topics in enterprise software is pricing. Despite the work done in value-based pricing over the past 50 years, the vast majority of pricing exercises still start with either a very basic cost-plus or percentage-based ROI model. This assumption has a key issue: it assumes that your product is a commodity. To explain why and to explain how to take a more value-based approach, consider what a price is.
There are many ways to break down price, but the model AI uses to translate value into price comes from 3 basic components: Reference Price, Differentiated Value, and Price Positioning
Understanding the Reference Price
The Reference Price can be further broken down into Commoditzed Value, Commoditized Cost, and the Reference Price:
- Commoditized Value – the table stakes that you need simply to get in the door, consisting of the value that your product is expected to provide.
- Commoditized Cost – the time and money investment that the customer is expected to make.
- Reference Price – the expected price customers typically pay for the set of functionalities that define your market
That reference price typically is your Commoditized Value – Commoditized Cost.
And one of the key issues that commodity product creators ignore is that the Commoditized Cost may mean that your initial reference price is Zero or Negative because of the combination of switching costs and the lack of initial value provided when your solution is not adopted at scale. This is the starting point for figuring out how much the product costs.
This second piece is where companies need to start understanding how they provide additional value relative to the market. Is it faster? Does it reduce or reallocate headcount? Does it support new business that cannot be supported otherwise? And how much does the solution realistically contribute?
In this second piece, value is ultimately defined by the customer. If the vendor believes they contribute 50% to a new venture and every client believes the solution only contributes 10%, the real answer is that the differentiated value should be based on that 10% contribution.
In addition, I believe that John Gourville’s articulation of the 9x effect in his 2006 Harvard Business Review article “Buyers: Understanding the Psychology of New-Product Adoption” still holds true due to the inherent skepticism that consumers have to purchase a new product. This approach leads to an assumption for new products where vendors overvalue their product and consumers undervalue the product due to inherent risk avoidance, comfort with the status quo, and the inability to imagine how process change or automation could lead to greater success. The exact number, whether it be 9x, 5x, or 20x, is less important than the realization that companies need to be able to model something along the lines of a 400% – 2,000% ROI to win over a new customer who is conducting an unbiased replacement and is willing to consider new solutions with the number getting higher as the ROI is less tangible.
This is a key issue that AI has seen over and over with new solutions: the pricing simply does not match the perceived risk associated with not getting value. As solutions become more established and proven over time, the 9x effect starts to recede first to a 3x to 5x effect and then to the corporate expectation of return on capital or return on assets.
Based on the first two issues, companies have a better idea of where the price should be based on the customer’s initial expectations. But the third piece of the puzzle is the role that the price should play. Companies seeking high market share with the expectation of being able to create a subscription model or a highly-adopted transaction-based model may seek a freemium model where the initial purchase is irrelevant. Companies with strong marketing and outreach capabilities may seek parity with expected market expectations. Or companies can skim the top of the market by selling only to the highest value use cases and only selling to, say, companies that can realize millions of dollars in value through their purchase.
As companies consider this pricing, they should be aware of perhaps the most common pricing error: addition, not multiplication. To effectively price for market penetration, strong value communication, or as a premium product, the price should be calculated based on the differentiated value and inducement to purchase (to ease consumer-perceived risk) on a per-unit, per-order, per-month, or per-use margin with an understanding of whether the initial purchase is profitable or not and the time period needed to break even. For instance, AI has noted that companies shifting from a license model to a Software-as-a-Service model typically price their products so that the subscription will pay back more than the initial licensing model over a three-year period. Simply sticking a percentage on top of the cost ignores the important exercise of actually thinking through both the value of the solution and the extent to which your company’s view of value is matched by the customer.
So, what does this have to do with technology?
One of the key challenges in pricing theory has traditionally been the ability to actually execute on the strategy. Companies may know that they provide X value for their initial use, but provide 10X value when clients use the product to its fullest extent. But it can be difficult to deal with the combination of transactions, usage, duration, tiering, pooling, peak-time, overage, subscriptions, and event-based pricing options in creating an actionable pricing model.
In addition, companies also confuse their costs with their pricing model. Just because a company pays, say, Amazon Web Services on a per processor, per GB, or per hour basis does not mean that the customer wants a similar level of granularity in their own pricing. Typically, a tiered pricing model with some freemium or usage-based conversion capabilities and tweaks based on the positioning of the price will make more sense.
With the emergence of digital transformation, data monetization, the Internet of Things, and the Subscription Economy, companies must transform their pricing, billing, and revenue recognition models to match both customer preferences and a changing cost structure where the Cost of Goods Sold increasingly includes an ongoing service cost and components that may vary based on the performance obligations of the contract. Because of this, Amalgam Insights will be focused on the future of Subscription Billing, advanced revenue recognition, technology lifecycle management, and the Art of the Renewal to help companies in creating the back-end capabilities to support and optimize innovative business models.
After all, a good idea on its own is worthless in the business world. In business, good ideas must have a valid and executable business model. That is why price and billing are crucial to the future of digital transformation.
In future blogs, Amalgam Insights will explore why companies insist on the wrong pricing assumptions and roles for their products and the portfolio of subscription billing and revenue recognition solutions that can actually solve modern business model monetization challenges.