Maximizing Returns through Data-Driven Pricing Strategies
Pricing optimization has the highest ROI of all the revenue growth value drivers I work with. Higher than growing market share, customer expansion, product expansion, market expansion and customer retention. Why? Because of the “I” part. Price optimization does not require an investment in product development or customer acquisition. All it requires is figuring out the price metric within a business model. Just a number. But because it’s a number that often drops out of a black box it’s a number that tests the confidence of many a management team who fear losing customers and new sales if they get it wrong (too high). This is justified because however that price is derived, it’s perched atop a wobbly house of cards of secondary data.
Some companies use cost-plus pricing and others use competitive-based pricing, but the gold standard is value-based pricing. Value. Since price is exchanged for value, to get the price right we need to understand how much value the product produces… which is easier said than done. Today, understanding what exactly the customer values and how much they value it is a qualitative research project at best. Surveys are deployed, focus groups are assembled and discussions are had to hear how much the customer is willing to pay and which features are most important. Management believing in new pricing requires a leap of faith because no matter how clever or complex, that derivation is balanced upon a few assumptions based on secondary data. Whew. Kind of prehistoric, isn’t it?
Fast forward a couple million years and we now have virtual products – native and virtualized. Software and tech products that use APIs to gather the primary data needed to calculate produced value and smart products that use sensors to gather the primary data needed to calculate produced value. By using revenue produced as a proxy for value produced (in B2B) we can measure it directly. This begins by understanding the KPIs in your customer’s business model and ends by determining the proportionate role your product plays in making the customer money. Choosing which APIs to link to or which sensors to install is based on the KPIs we need to measure.
Let’s look at two simplified examples to make this more concrete: one in graphic design software and the other in mining. The KPI for graphic design software, like AI generated art, is the number of images produced. The KPI for mining extraction machinery, like a bucket wheel excavator, is metric tonnes of material extracted. These KPIs sit within the customers’ business models and their corresponding data, number of images and weight, can be collected by API calls or readily available sensors. To complete our model, we determine the product’s attribution to value through desktop work.
In this conceptual overview I only touch on price but in the future I’ll discuss how to analytically define packaging - the other key factor in capturing the full value a product produces. And this line of thinking naturally leads us into business model design, but that’s a broad topic that I’ll also cover at another time.
The pricing optimization approach I describe here is based on pure customer data – clean and unbiased. By using APIs or sensors to understand how customers make money with their software or physical products, pricing becomes an analytical exercise that can stand up to the scrutiny of any skeptical CFO. This is true value pricing.
Bruce Sinclair is Managing Partner of Digital Operating Partners, a smart tech value creation firm
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