This week’s reading describes the challenge of Peter Noll, chief of Scherr Diagnostic Divisions, in finding an optimized single revenue model for the merge of Siiquent and Teomik. Separately, each business had its own version of a revenue model, with Siiquent profiting on consumables and Teomik selling high-margin machines (with a very flexible model that was responsive to customer feedback). Peter was interested in answering the question of whether profit should come from the machines or the consumables through a rigid revenue model. However, the leaders of the two companies were united in their opinion on a “non-model” (or rather, an “ever-changing mix of models”) model, driven by flexibility and responsiveness to the dynamic market and the changes demanded as it goes (as described, instead of focusing on “stuff or machines,” focusing on “customers, competitors, and employees”).
Siiquent’s primary market of consumables is hospitals and large diagnostic labs- the company was able to provide consumables for a bit less than the fixed reimbursements received from insurance, and thus was able to generate revenue for their market. Additionally, Siiquent’s customer service is exceptional and responsive to consumer feedback, leading to loyalty and trust from market users. Meanwhile, Teomik’s primary market are “big funders of genetic research,” such as research institutions and universities that are interested, and more importantly, able to, purchase high-margin machines.
A more rigid revenue model has the advantage of greater strategic focus—there is a clear path forward and thus greater clarity in “the next move” on long-term goals. A rigid model also promotes efficiency, as the internal operations of what needs to be done are known and unchanging. However, this rigidity lacks responsiveness to what sounds like an incredibly dynamic market, which would beg the question: what happens if you choose the wrong rigid model? What if what is deemed the most optimized rigid model one year is no longer as effective the next as the market demands change?
Meanwhile, a flexible approach addresses these questions by changing alongside the dynamic market. In addition, this promotes innovation by keeping options open and finding new ways to respond to demands. However, the drawback of this method can be a lack of clarity on future directions, and so I can imagine it would be harder to create and plan around long-term goals like you would in a rigid model.
I would scaffold my discussion by first establishing what the goals and intentions are for the company. Next, define the key issues to address in what has versus has not worked in their previous models, and the pros and cons of different types of models. From this discussion, we would identify our shared values on what it is we want to prioritize. Next, I would facilitate brainstorming to generate potential hybrid models or transitional approaches with scenario planning that maps how we estimate different models would perform under various conditions. Then, small-scale experimentation to gather real-world feedback, along with emphasizing iterating in the future.
