Market, patent, and commercial research for wrist-worn fall detection

This is an independent sample created from ZKK's own internal hardware program. It is not client work, and no client was involved. The public research in it is real and sourced. The model projections are illustrative, based on stated assumptions, and are not forecasts.

We built it to show the format and method of a Comprehensive engagement combined with Patent Landscape Research, applied to wrist-worn fall detection. You can read the sample report (PDF). Other samples are listed on the Work page.

The work combined several strands, each producing its own section of the deliverable. In general terms:

• Market sizing builds a total, serviceable, and obtainable market from documented inputs and assumptions. The method is explained in our guide to TAM, SAM, and SOM for medical devices.
• Competitor research identifies companies and products in the field and compares positioning and offerings using public sources.
• Buyer research looks at who purchases or influences the purchase, and what they care about.
• Patent research organizes publicly available patent information around the technology, with a patent list, a comparison, and a documented search approach.
• Regulatory and reimbursement research gathers public information on regulatory context, coding, coverage, and payment.
• Supply-chain research looks at components, suppliers, and related considerations.
• Pricing research examines how comparable products are priced.
• Channel research covers the routes by which a product reaches buyers.

The strands feed one another. Pricing and channel findings, for example, shape the inputs to the commercial model.

The deliverable is a source-supported report with a companion Excel model. The model is editable, so a team can change inputs and rerun the numbers.

The model separates sourced inputs from commercial drivers that have not been measured, so a reader can see which numbers come from public evidence and which are assumptions. It makes the base, downside, and upside cases explicit.

That separation is the point of the format. A projection is only as strong as its assumptions, and a reader should be able to see them, question them, and replace them with their own data as it arrives.