Sassy Pricing for SAAS
Updated: Sep 7, 2022
Just stumbled on an intriguing question posted on this Facebook group (Hebrew). This released an insight I would like to share.
How to Price Dynamically a SaaS Product?
The issue at hand: What is the right pricing model for a service catering to differently sized customers, where website hits are the driving factor, and utility to the customer remains opaque.
Patric Campbell describes the concept of dynamic pricing in this article, dwelling on the potential impact on public relations. (we do not go in this direction, for the domain we're addressing is b2b, rather than b2c).
Let us first revisit dynamic pricing, also pejoratively called "Price Discrimination".
In the diagram above, we can set a fix price P, and consumers would buy a certain quantity Q, generating revenue R=P*Q.
The diagram below demonstrates how n price points result in an aggregate revenue function ∑R=P1*Q1+P2*Q2+...Pn*Qn
Indeed, Campbell mentions a gradual pricing, where customer size matters, as in this example by Hubspot:
Campbell goes further to introduce a fixed part and a variable part, to reflect different account sizes, and maximize revenue.
Is it enough?
The question remains, how do we price, so that we do not lose revenue from large accounts, or deter smaller accounts from joining in.
A small detour
One efficient way for pricing is the ECV - Economic Customer Value - measuring the incremental utility a new service or product brings to customers. The problem in this discussion is, suppliers do not necessarily know what their customers' economic value is, as they chose to retain it opaque, and would not share usage figures.
In recent years, bidding mechanisms have proliferated in the programmatic advertising arena, where advertising networks allocate advertising assets dynamically, resulting from a self service bidding process.
In this paradigm, customers decide their price, and compete for a scarce resource - the advertising space available. The equilibrium reached optimizes both price and quantity for each customer, maximizing value for both buyers and sellers in that market.
How can my customers bid on my service?
So, can SaaS vendors resort to a bidding process, instead of guess pricing? I believe that when considering a volume business, however opaque the utility function is, we can.
First, let us consider the prerequisites:
We need to externalize the utility to the customer, and best of all, let them monitor it themselves.
We need (paradoxically) to make our service scarce, so that customers have to compete on it.
Bidding on time Domain Scarcity
In this post, I am proposing an artificial insertion of scarcity to the service offered. A prototype description would be:
Let the service operate as a TDM (Time Division Multiplexing) machine: For each user, divide the duration a service is rendered to subdivisions - such as seconds to the minute
Now set a base level when your service operates (perform the service on all website hits during only part of the time)
Now you have an A/B testing generator, where your customers can evaluate for themselves the difference between the time your service is ON, and when it is OFF
Build a bidding interface, where your customers can bid on additional seconds-to-the-minute service.
In this scheme, though, you do NOT offer a 100% service option. Not right away. Instead, your customers compete for those extra seconds, and augment the price offered, (resulting in time the service is consumed), in similar way to programmatic advertising.
Now you can build a double tiered pricing scheme, where customers pay a base price for part of the time they use your system, and bid dynamically for additional time - until an equilibrium is reached.
Some customers may pay only the base price (and obtain partial utility) while others would pay more, and obtain utility accordingly.
A few months into this (if you still have customers remaining, that is), you'll know what are the optimal price points, so that you may switch to an easier pricing scheme, or keep this elaborate infrastructure for maximized revenue.
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