Pricing by the unit of value is a useful test because the same $500 can feel cheap or absurd depending on what moves. For an AI receptionist offer, I would track booked jobs recovered and gross margin after review time, not minutes or calls handled. Which billing unit best survives when usage rises but customer value does not?
I agree with the statement of a gap between Comprehensibility that is hidden behind Price sensitivity. I also fully agree with the point to slice by top-account gross margin, as in most cases dependency on top accounts creates marketing effects more than real profitability. As a reader thinking about B2B specifically, I recognise one gap between vendor and buyer: the price is negotiated on one set of parameters, usually technical, but then offered in a different way to end customers based on the value it creates for them. It is interesting then to see that the value is more beaten, as it is more debatable. The fact remains that in this context the purchase is a product that is then translated into the business value that end customers buy. This by default creates price inconsistencies and I would expect more alignment to happen.
Sure. An example from telecom. In 5G network slicing, an equipment vendor prices to a Telco on traditional cost components: software licences, professional services, training, support and maintenance, right-to-upgrade fees. These are the vendor's standard commercial parameters, auditable and easy to compare across suppliers.
The Telco then needs to sell network services to its own enterprise customers, but those customers do not buy licences or professional services. They buy service types: mobile broadband capacity, low-latency connectivity for industrial applications, fixed wireless access, IoT connectivity, transport connectivity. The Telco asks the vendor to translate its cost-component pricing into per-service-type pricing based on projected service volume. That translation is real work, and heavy in negotiations, especially when requirements or volumes change. That happens often. In dynamic services that AI supports, this translation will become not sustainable.
At first, I glanced past your questions but am glad I returned to think deeper. Q1 is particularly important because it shifts the focus to customer outcomes. The ATS example, in particular, digs deep into a customer outcome with a truly valuable Signal to Noise ratio.
I'm a little embarrassed how often I've gotten this wrong in my career. Even as a consultant selling hours, I never understood why it was so difficult for clients to understand. But they obviously weren't able to cleanly translate my time into their business outcomes.
Pricing by the unit of value is a useful test because the same $500 can feel cheap or absurd depending on what moves. For an AI receptionist offer, I would track booked jobs recovered and gross margin after review time, not minutes or calls handled. Which billing unit best survives when usage rises but customer value does not?
I agree with the statement of a gap between Comprehensibility that is hidden behind Price sensitivity. I also fully agree with the point to slice by top-account gross margin, as in most cases dependency on top accounts creates marketing effects more than real profitability. As a reader thinking about B2B specifically, I recognise one gap between vendor and buyer: the price is negotiated on one set of parameters, usually technical, but then offered in a different way to end customers based on the value it creates for them. It is interesting then to see that the value is more beaten, as it is more debatable. The fact remains that in this context the purchase is a product that is then translated into the business value that end customers buy. This by default creates price inconsistencies and I would expect more alignment to happen.
I think I follow: the buyer has different success/grading metrics than the end user, which also must be satisfied to compete a sale.
Do you have an example that illustrates it?
Sure. An example from telecom. In 5G network slicing, an equipment vendor prices to a Telco on traditional cost components: software licences, professional services, training, support and maintenance, right-to-upgrade fees. These are the vendor's standard commercial parameters, auditable and easy to compare across suppliers.
The Telco then needs to sell network services to its own enterprise customers, but those customers do not buy licences or professional services. They buy service types: mobile broadband capacity, low-latency connectivity for industrial applications, fixed wireless access, IoT connectivity, transport connectivity. The Telco asks the vendor to translate its cost-component pricing into per-service-type pricing based on projected service volume. That translation is real work, and heavy in negotiations, especially when requirements or volumes change. That happens often. In dynamic services that AI supports, this translation will become not sustainable.
At first, I glanced past your questions but am glad I returned to think deeper. Q1 is particularly important because it shifts the focus to customer outcomes. The ATS example, in particular, digs deep into a customer outcome with a truly valuable Signal to Noise ratio.
I'm a little embarrassed how often I've gotten this wrong in my career. Even as a consultant selling hours, I never understood why it was so difficult for clients to understand. But they obviously weren't able to cleanly translate my time into their business outcomes.