How an Investor’s Behavioral Traits Might Completely Derail Your Pitch – Part I

Many startup pitch meetings start out on a promising note, but things fall apart during the conversation between the startup and its prospective investor. Sometimes this could have been prevented if the startup team had studied a little bit of behavioral psychology beforehand.¹

Traditional finance theory tries to tell us how investors should behave, if they act as rational economic beings. Behavioral finance is based on the observed behavior of investors. Traditional finance is based on economic theory. Behavioral finance is based on psychology. Traditional finance assumes that investors make their decisions based on all available information, that investors are rational, and that markets are efficient.² Even if you think this is true in public markets, you have to agree that it is not the case in venture capital. It is especially true that the market for early stage venture investments is highly inefficient, prone to extreme uncertainty and severe information asymmetries. Behavioral finance makes none of the assumptions of traditional finance.

In this post, I will describe a few behavioral biases that an investor might exhibit during a pitch meeting with the founder of a startup.³ I will describe how each bias might be exhibited during a pitch meeting. I will also suggest how the entrepreneur might attempt to mitigate each bias. Failure to mitigate a behavioral bias could mean that the pitch gets derailed, and the entrepreneur fails to communicate effectively with the investor about the startup. I have developed the examples on the basis of meetings at which I have been on the side being pitched by entrepreneurs, after-the-fact reports about investor meetings that entrepreneurs I know have spoken with me about, and also from meetings at which I have been on the side pitching an innovation to potential business partners, and investors for a startup that I have been helping to incubate since January 2011.⁴

The behavioral biases that I will cover in this post are categorized as cognitive errors. A cognitive error or bias stems from the inherent shortcomings people face as they try to process information that is unfamiliar and complex. Cognitive errors are further categorized as information processing errors or belief perseverance errors. This post will focus on a few belief perseverance biases. Behavioral biases generally can be grouped as cognitive errors, emotional biases, memory errors or social biases.

As you read the rest of this post you will notice that the lines between these biases is somewhat blurry – what one person sees as indicative of one kind of error could be seen by someone else looking at the same information as indicative of another bias. That seems to be the nature of those behavioral biases I have studied – there’s a lot of interconnection and it is difficult to assign an observed behavior to a single bias or error. More likely, the observed behavior arises due to a combination of biases and errors. That is why I think preparation beforehand is key. The entrepreneur can try various approaches to mitigating the observed bias until one approach leads to a break-through that restores the flow of ideas and communication between the entrepreneur and the potential investor.

Cognitive Errors – Belief Perseverance:

1. Conservatism Bias: This occurs when a potential investor fails to revise his preconceived beliefs about your startup even when there is new evidence that suggests that his beliefs are incorrect.⁵
   • Case: Steve is 20 years old. He has quit college with two of his classmates to focus on building a startup – Disruptive Tech Startup (DTS). He meets with an early-stage venture capitalist to describe the work they have done so far and their vision for the future. Steve does not realize that the investor believes that he’s too inexperienced and too young to accomplish what he wants to accomplish with DTS. More specifically, the investor does not believe that Steve is experienced enough to steer DTS so that the investor realizes the minimum 10x return that the investor’s investment thesis requires. Steve thinks the meeting went well, but the investor later tells him that the fund has decided to pass on making an investment in DTS.
   • Mitigation: Steve should spend more time discussing his background, what he has done to learn how to run the startup, and how he will learn what he needs to learn in order to run the startup in the future. He should explicitly tackle the issue about his youth and relative lack of experience, and openly discuss steps he will take, or has already taken to ensure that his investors’ capital is not put at risk because of his youth and perceived inexperience. He should offer references who prospective investors might speak to about his leadership potential as it relates to managing a startup. He should not assume that the investor will conclude that he will continue to succeed in the future after seeing what he has accomplished at DTS so far.
2. Confirmation Bias: This occurs when the investor focuses on perceived negative aspects of your startup while ignoring and dismissing your attempts togive an explanation with evidence that will contradict that perception.
   • Case: Steve is pitching DTS to another early stage investor. She thinks that the technology they have developed is trivial after having listened to Steve for about 20 minutes, and she tells them as much. Steve gets frustrated because he feels she does not understand what DTS is about. The meeting is a disaster because she keeps focusing on the notion that “But isn’t this just a simple bot that scrapes the web for data? If I were a software developer I could do this with very little effort.”
   • Mitigation: This investor likely does not understand the full extent of the problem that DTS is solving.⁶ If Steve is stuck in “Demo Day Pitch” mode he likely has not considered that in a small meeting the dynamic is different. He should “put away the deck” and go into “professor mode” – in this mode he is educating the investor about the problem, about how DTS is solving that problem, and also about the opportunity that presents for potential investors in DTS, all in a conversational setting – like a professor teaching a student a new concept during office hours. He should expect to follow this up with further information for the investor to consider.⁷
3. Representativeness Bias:⁸ This occurs when the investor uses an if-then rule of thumb or mental shortcut toassess your startup because of the high levels of uncertainty associated with the decision the investor must make.
   • Case: Ademola is a Nigerian entrepreneur. He has been building an Internet software startup in Lagos for two years, African Technology Startup (ATS). He grew up in Nigeria and holds a master’s degree in computer engineering from the Obafemi Awolowo University of Science and Technology, one of Nigeria’s leading universities of technology. In order to grow ATS he has moved to New York and is raising capital from investors. ATS has customers from all over the world, and he believes ATS is solving a significant problem for them. Growth has been phenomenal. ATS has accomplished that growth on a shoe-string budget. Ademola has been building ATS with two other people, they are both co-founders of ATS as well. They will remain in Lagos to manage the technology and R&D efforts. Ademola is worried that many of the investors he will meet are ignorant about Africa. He is also worried that they may unconsciously harbor negative perceptions about ATS that they will not verbalize during a meeting.
   • Mitigation: Ademola has to work doubly hard to demonstrate his technical competence because the average early stage investor in the United States does not associate Africa with technical talent and competence. For example, investors might assume that ATS is relying on a contract software engineering consultant in Asia or Eastern Europe. If ATS is building its technology in-house, Ademola has to make that explicit. He has to talk about the technology in a way that demonstrates that he can fulfill the vision that he’s selling to his customers, and potential investors. He has to convince his audience that a software engineer trained in Nigeria can compete on the global stage. He has to remember that his accent could inhibit potential investors’ ability to understand what he is trying to communicate.⁹ Instead of hoping that they will ask him to clarify something, or explain something they do not understand he should practice speaking clearly and communicating effectively to people who have never encountered someone with his accent. He should avoid using colloquial terms and idioms that are used in Nigeria, but may not be commonly used elsewhere. Investors in NYC will not understand those terms. He should be friendly, but he should avoid the temptation to be unnaturally funny. His off-the-cuff attempts at humor could back-fire. The representativeness bias at play here could be “If ATS is an African startup then the probability that it is doing all this on its own is zero because all we ever see on TV about Africa is war, starvation, and political corruption and incompetence.” Ademola has to overcome that bias during his conversations.¹⁰

An investor’s cognitive biases play an important role in how that investor will hear and interpret the information that an entrepreneur is trying to convey. Time spent understanding this phenomenon and how to mitigate any possible negative effects of a prospective investors behavioral biases will invariably lead to more productive pitch meetings.

Wikipedia’s entry on cognitive biases is here. Wikipedia also has a much more extensive list of cognitive biases here. If you have the time, you should invest in a copy of Daniel Kahneman’s¹¹ Thinking, Fast and Slow.

1. Any errors in correctly attributing work to my sources and references is entirely my fault. I’ll make corrections if you point an error out to me. ?
2. The efficient-market hypothesis (EMH) states that financial markets are informationally efficient and that the price of a publicly traded stock incorporates all available information about that stock. ?
3. I am basing the outline of this post on portions of the CFA Institute’s 2013 Level III readings on Behavioral Finance. ?
4. I am not a psychologist, so my discussion of this topic will certainly fall far short of even very modest expectations. However, I hope that budding entrepreneurs find this discussion to be a good starting point for some independent work on this topic. ?
5. This can also be exhibited as a tendency to underestimate high-likelihood events and overestimate low likelihood events. ?
6. The unstated assumption here is that DTS is not solving a trivial problem. ?
7. See this post for an example. ?
8. The Wikipedia entry for the Representativeness Heuristic is here. You should read it if you are building a startup and will be raising capital from investors. ?
9. Y Combinator’s Paul Graham inadvertently got embroiled in a pseudo-controversy over this subject. You should read what he has to say, and also read what others have said. Paul’s post on the subject is here. One response is here. ?
10. This blog post at 59 Ways is a clear, but brief explanation of this bias. ?
11. If you purchase it through this link I will receive a small portion of the sales proceeds from Amazon to help me maintain this blog. ?