Tag Archives: Incentives

Getting investors to act fast

When fundraising, the best way for an entrepreneur to get investors to act fast to complete an investment (and to get a healthy valuation) is to create demand for the company from competing investors.

However, this isn’t always possible. Especially in markets where capital is scarce, even very promising companies might not have many investors at the table.

When this is the case, some entrepreneurs resort to fabricating demand that doesn’t exist. They claim that investors who aren’t interested actually are, or they exaggerate the interest level of investors who have expressed initial interest. This often backfires because investors talk to each other.

Another approach is to set an arbitrary deadline. This doesn’t work because it doesn’t tell the investor what they have to gain from investing early and the investor knows that they’re the only party at the table. If the deadline were to pass it would simply be extended. In other words the deadline isn’t credible.

Rather than fabricate demand that doesn’t exist or set an arbitrary deadline, a better approach to get investors to cross the finish line is to show them the growth opportunities that the company will miss out on or have to delay due to the lack of funding. This also means that the investor who’s evaluating an investment in the company will miss out on them.

This includes highlighting the great team members that the company isn’t able to hire, demonstrating the foregone revenue or cost savings potential from not making a particular capex investment, and quantifying the opportunity cost of not conducting a specific marketing campaign, all due to delayed funding.

The reason why this works is two-fold. First off, unlike fabricated demand that doesn’t actually exist, it’s truthful. And second, unlike an arbitrary and uncredible deadline, it shows the investor what they have to gain from investing early.

Complex and simple systems

Simple systems have clear links between inputs and outputs. If certain knowable conditions are met, there’s a well-defined function that maps the inputs to the outputs.

Complex systems are different. They feature multiple actors that offer up a probability distribution of inputs which interact in context-specific functions with partially knowable forms and indeterminate weights. The outputs which are produced as a result are therefore impossible to predict with full accuracy. Our best bet is to develop increasingly educated guesses.

As a result of the different nature of simple and complex systems, the approach necessary to succeed when working with each is different. In particular, working with simple systems requires knowledge of the facts.

In addition to knowledge of the facts, working with complex systems requires an understanding of the incentives of different actors, an iterative approach to testing these incentives and how their interactions produce outputs, and a readiness to revise the form and weights of the context-specific functions you develop as you learn from experience.

Since simple systems are easier to solve, many people solve them. So you’re likely to get immediate positive feedback from someone after solving a simple system.

Since complex systems are harder to solve (in fact you can’t fully solve them and have to be content with getting gradually closer to solving them), there are fewer people solving them. This means that there are fewer people there to give you immediate positive feedback on your progress.

Making progress towards solving complex systems also takes more time, so the frequency of feedback is lower than that which you get when solving simple systems.

However, the intrinsic and extrinsic rewards from making progress in understanding how complex systems work are much greater than the rewards from actually solving simple systems.