Markets and Revenue Models: “Stuff” vs. “Machines”
Isolde’s Siiquent targets hospitals and diagnostic labs constrained by reimbursement systems and lower budgets. Her razor-blade model sells consumables (“stuff”) at a low price but profits from per-test pricing. It turns out that this is ideal for cost-sensitive, high-volume users. These users have benefited from being able to actually make money by purchasing from Siiquent, as Siiquent charges slightly less than the reimbursement value.
Meanwhile, Emanuel’s Teomik serves research institutions and universities seeking prestige, not cost efficiency. His model sells patented equipment (“machines”), and it earns high margins on sophisticated instruments that institutions/universities don’t mind paying extra for because they are unique and patented.
Both models also involve offering high-quality customer service and maintenance, which serves as a relationship builder, not a revenue stream.
Basically, each model fits its market’s purchasing logic: recurring usage vs. up-front investment.
One vs. Two Models
Pros for the merger: A single model offers clarity of vision, scalability, and simpler metrics, which is Peter’s rationale. It’s also more cost efficient for the parent company, as you can probably cut down on redundant functions and synergistic processes.
Perils of the merger: However, a single strategy risks rigidity and ignoring how two distinct markets buy. Putting them together places you at risk of forcibly jamming together two puzzle pieces that just don’t fit — hospitals/labs with high purchase volume and cost constraints have very different needs and values from prestigious universities who don’t mind paying more for the right specific instruments. By unifying the models, you risk alienating one or even both markets. Pleasing all = pleasing none.
A flexible mix fosters responsiveness but invites internal chaos and moral hazard, as seen when pay-per-test customers overused materials. The case captures a central trade-off.
Mediating the Merger
As PM, I’d scaffold a fair, data-driven process rather than force consensus, using negotiation principles.
- Before talking specifics, align on interests, not positions. On paper, the two companies and the two audiences they serve might seem to want different things, but beneath the superficial opposing positions (stuff vs. machines), there are probably aligned interests with synergies.
- Also before talking specifics of the merger, have Isolde and Emanuel brainstorm and then agree on a set of fair, objective criteria by which to make the decision of which business model to use. Also have them define shared success metrics (profit, loyalty). That way, when you make a decision based on objective criteria that make sense to both parties, you remove the human ego / emotional conflict side of things between Isolde and Emanuel. You can always go back to the objective criteria to keep things fair.
- Map how each model affects customers and teams.
- Base decisions in data: consider A/B testing the two models, see what works better.
