It’s widely known that more than 90% of start-ups fail. The most common reasons that tech start-ups fail at the post-seed / series A stage relate to inability to validate the technology and failure to achieve product-market fit.
But let’s dive deeper into the specific factors that contribute to these types of failures.
Technology Validation
Technology validation refers to the process by which a technology’s potential use and benefits are tested and demonstrated in the real-world. But when does a technology fail / not validated?
- Technical difficulties in integration with existing technological systems.
- Example: complex hardware-software technologies such as robotic medical systems and semiconductor technologies where compatibility with legacy systems is crucial.
- A technology fails completely at the testing level
- Example: technologies requiring significant clinical testing before wide use. Prominent cases include pharmaceuticals or interventional / invasive medical technologies where extensive clinical studies are needed to prove both their safety and efficacy.
Product-market fit
Product-market fit refers to the extent to which a product has proved its value to a number of customers with positive feedback. But being commercially successful depends on:
- Need: the technology should solve a huge industry problem (must have as opposed to nice to have).
- Scalability: for wide market adoption there should be least or no need of processing / customization of the technology (either by the start-up or more importantly by the customer) to arrive at the final product. This should apply not just to ‘early adopters’ but to each potential future customer. If integration of the technology with the customers’ systems / processes is necessary, such integration should be seamless and easy.
- Value Chain: the product should ideally be positioned to the end of the value chain i.e. the product / technology should be sold to the end-user and not, for instance, to the supplier of the end-user.
- Sales cycle: the product should be of high value thus the customers’ decision to buy it and adopt it is fast. This is crucial, especially at the scale-up phase.
- Unit economics: the pricing model should be optimal both for the start-up and its customers. For example, a SaaS pricing model makes sense if the product is continuously improved and new features are added, thus enabling the start-up to upsell. Pricing should be studied carefully and be benchmarked against existing solutions. If the product is premium this should be reflected on the price. If the product is on par with existing ones, pricing should be in-line or cheaper. Under any scenario, both incremental and total revenue generated should exceed fixed and variable cost.
Overall, tech start-ups fail because the product / technology cannot be validated or it does not succeed commercially.
Biostrategy Analytics 