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Whatever You Do, Be Aware of These 21 Biases!
DDVC #56: Where venture capital and data intersect. Every week.
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Cognitive biases in the VC investment process
VC has long been the lifeblood of budding startups, offering them the financial springboard they need to bring their innovative ideas to fruition. Yet, beneath the surface of multi-million-dollar funding rounds and high-profile success stories, lies a nuanced landscape of decision-making, influenced by a range of overt and subtle cognitive biases.
These biases can shape the fate of young companies, often determining which ventures are given a chance to soar and which are left grounded.
Talent is distributed equally but capital and opportunity are not.
VCs are the gatekeepers for capital and opportunity, thus understanding the underlying decision-making process and potential flaws is key. In this article, I will share what a typical VC investment process looks like and provide an overview of the potential biases involved.
After reading it, you as an investor should be able to better recognize your own and your team’s biases, to make better, more objective decisions. As a founder and operator, you should gain more clarity on the VC investment process and how to leverage the VC biases to optimize the chances for an investment in your company.
Snapshot of the VC investment process
Depending on the target stage of an investor, investment processes vary. The earlier (later) they invest in the development cycle of a company, the leaner (more extensive) the process. For typical early-stage investors at Seed to Series A stage, a process looks something like the following:
Sourcing: Historically mostly inbound, meaning founders need to reach out to the investors either via email, LinkedIn, website, or any other channel. In light of the rising competition among VCs and with the help of data-driven approaches and AI, VCs gradually increase the relative share of outbound activities, meaning that VCs reach out to the founders directly, oftentimes even before they’ve officially announced their new venture.
Screening: For most inbound requests, VCs will ask for a blurb and/or a pitch deck to check the fit with their internal investment criteria such as location, stage, industry, business model, or even whether they have a competing investment already. Outbound dialoges require less information as VCs have oftentimes done their homework on the person and the business already, knowing exactly why they want to talk to an entrepreneur.
Intro call/meeting: Depending on the internal structures of a VC firm, either the lead partner for a specific vertical/technology of the respective company and/or a more junior investment professional who works closely with the check writer takes the first call. It’s important for founders to note that within larger VC firms, investment professionals oftentimes act as the gatekeepers to the investment committee. They can be your greatest cheerleader but also your biggest blocker, thus it’s super critical to treat them with the respective care and not tell them “I only want to talk to the Partner”.
Deal flow call #1: Following a short debrief within the “deal team” (= lead partner for a specific vertical and investment professionals supporting on the deal; typically 2-3 people, can also include who support on the groundwork DD), the most promising opportunities get presented to the broader partnership and investment team. This deal flow call typically runs on a weekly basis to benchmark the most interesting opportunities in the funnel, collect questions, and decide on go vs no-go.
Due Diligence: While some basic DD can start before the intro call already, post deal flow call is typically where the wheel starts to spin, kicking off deep dives on product, competitive landscape, metrics, user/customer references, hiring, and a lot more. This process is oftentimes a ping-pong between the deal team and the founders on the other end, bouncing back questions, findings, and ideas. Of course, this is the best experience as a trial period to see how a mutual collaboration may look like.
Deal flow call #2: Once all work got done and major questions got answered, the results will be presented to the broader investment team in the second deal flow call. This is where most teams then decide to invite the founders to an investment committee or discontinue the process. Rarely they decide to do even more work.
Investment Committee: While in the past these meetings were on a recurring basis like every Monday or so to benchmark as many opportunities as possible, COVID and remote work broke many of these rigid processes into more opportunistic scheduling, meaning that few firms today still have these IC days but rather adapt to the individual fundraising process dynamics. ICs consist of voting IC members (typically General Partners and Partners) and the deal team. Depending on firm culture and size of the Partner group, more or less investment professionals from the broader team join this meeting. There are different formats of debriefing and voting, but for us at Earlybird every team member has a voice, we collect these unbiased perspectives via a survey, then look at the results together and discuss perspectives bottom-up, starting with the most junior professional in the room.
Term sheet & confirmatory DD: Following the debrief comes the term sheet negotiation and the stage where confirmatory due diligence (legal, tech, financial, etc.) kick off in parallel streams.
That’s what a typical Seed to Series A stage investment process looks like. Screening, Deal Flow calls, Due Diligence, and most obviously Investment Commitees can all be understood as a fork in your decision tree: go vs no-go. As different individuals are involved throughout thes process, the underlying discussions are prone to a variety of biases.
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Why do we even have cognitive biases?
Humans have cognitive biases due to a variety of evolutionary, psychological, and social factors. These biases often serve as mental shortcuts or "heuristics" that help individuals make decisions quickly, particularly under conditions of uncertainty or limited information.
Although cognitive biases can lead to perceptual distortion, inaccurate judgment, illogical interpretation, or what is broadly seen as irrationality, they have also been evolutionarily advantageous in certain contexts. Here's a bit more detail:
Survival: Early humans needed to make quick decisions to survive in often hostile environments. Quick, heuristic decision-making (like assuming that rustling in the bushes is a threat) can be more advantageous for survival than a slow, analytical approach.
Energy Efficiency: The brain is an energy-intensive organ. Simplifying complex decision-making processes by using cognitive shortcuts helps to conserve cognitive resources and energy.
Information Overload: In the modern world, humans are bombarded with more information than they can process. Biases help simplify the information and make decision-making manageable.
Memory and Recall: Cognitive biases may impact how we remember and recall information, often prioritizing emotionally charged or personally significant events over objectively important information.
Confidence and Control: Biases like the illusion of control or overconfidence bias allow individuals to navigate through life with a necessary degree of confidence, even if it's sometimes misplaced.
Social Cohesion: Some biases, like in-group bias, might have developed as mechanisms to promote social cohesion and cooperation within groups, which is crucial for societal development and personal well-being.
Social Influence: People often conform to societal norms and values, which can create and reinforce biases. The desire to belong and be accepted by a social group can override the objective assessment of information.
Decision-making Under Uncertainty: In environments with variable resources and unpredictable outcomes - such in the world of startup investing - biases like loss aversion might develop as a way to mitigate potential risks and navigate through economic decisions.
Resource Allocation: Mental shortcuts can help individuals make quicker decisions about how to allocate resources, even if those decisions are not always optimally rational.
Limited Cognitive Capacity: The human brain has limited cognitive processing capacity. Thus, cognitive biases act as filters that allow people to make decisions without analyzing every piece of available data.
Simplification: Biases often help in simplifying complex problems and decisions, enabling people to act despite the complexity and uncertainty of the world.
Despite these functional aspects, it's essential to note that cognitive biases can also lead to systematic errors, poor decision-making, and perpetuation of stereotypes and prejudices. In modern, complex societies, the downsides of these biases are often more apparent, and thus understanding and mitigating them is crucial.
While pattern matching can lead to great outcomes for some investors, it prevents majority to see beyond their own sample of success cases and thus miss out on future opportunities. Therefore, creating awareness for your own and your team’s underlying biases is key.
Overview of biases throughout the investment process
Having spent the past 6+ years on data-driven approaches to VC, and particularly decision science, I summarize the different cognitive biases I’ve observed throughout my time as a researcher and investor below.
1) Anchoring Bias
Definition: You rely heavily on the first piece of information you receive.
Scenario: A VC might use the initial valuation of a startup as a reference point in future fundraising rounds, potentially overlooking current metrics and performance.
2) Authority Bias
Definition: You trust opinions from perceived authorities more.
Scenario: If a well-known and successful investor backs a startup, other VCs might also invest without conducting thorough due diligence, trusting the initial investor's judgment.
3) Availability Heuristic
Definition: You judge things based on info readily available or easily recalled.
Scenario: Investors might prioritize investing in sectors or startups they’ve recently encountered or had success with, neglecting potentially promising opportunities in unfamiliar domains.
4) Bandwagon Effect
Definition: You tend to follow popular opinions or trends.
Scenario: (insert hot technology - AI, blockchain,..) has been attracting many investors, even those traditionally non-aligned with tech investments.
5) Confirmation Bias
Definition: You favour information that confirms your existing beliefs.
Scenario: An investor might place more emphasis on information or data that supports their belief in a startup’s potential, while neglecting negative indicators.
6) Dunning-Kruger Effect
Definition: You overestimate your ability when you know little about something.
Scenario: VCs who had early successes might overestimate their expertise and make risky investments thinking they can't fail.
7) Endowment Effect
Definition: You value things more when you own them.
Scenario: Investors may overvalue their portfolio companies simply because they have invested in them, potentially leading to improper resource allocation.
8) False Consensus Effect
Definition: You overestimate how much others agree with you.
Scenario: A VC might assume that other investors perceive the value and potential of a particular startup in the same optimistic manner they do.
9) FOMO (Fear Of Missing Out)
Definition: You feel like the train is about to leave without you and you rush against your rationale to jump on.
Scenario: Heard about this hot new startup that every investor is chasing? What are you waiting for - reach out and get in front of the train!
Definition: Your desire for harmony outweighs rational decision-making, causing you to conform to the group's decision rather than presenting contrarian viewpoints.
Scenario: Investment teams may agree to fund a startup without sufficient scrutiny because no one wants to be the dissenting voice or appear non-cooperative.
11) Halo Effect
Definition: You judge a person's character from an overall positive impression.
Scenario: Investing in a founder with a successful track record without thoroughly evaluating their new venture’s viability.
12) Home Bias
Definition: You prefer to invest in familiar geographies or industries and miss out on unfamiliar opportunities.
Scenario: Overlooking or undervaluing foreign startups due to a preference for investing in local companies, which are perceived as less risky or more understandable.
13) Illusory Correlation
Definition: You mistakenly believe two unrelated things are connected.
Scenario: Believing that founders who graduated from elite universities always create successful startups and subsequently favoring them during investment decisions.
14) Negativity Bias
Definition: You focus more on negative events than positive ones.
Scenario: Avoiding investments in certain sectors or founders due to past failures or negative experiences, despite their present merits.
15) Outcome Bias
Definition: You judge a decision by its outcome, not the decision-making process.
Scenario: Associating a successful exit with smart investment decision-making, even if the success was due to external factors or luck.
16) Recency Effect
Definition: You give more weight to the latest information or experiences.
Scenario: Placing undue emphasis on a recent market trend or startup success story while making investment decisions.
17) Self-Serving Bias
Definition: You credit your successes to yourself and blame your failures on others.
Scenario: Attributing successful investments to one’s skill and failed ones to market conditions or startup teams.
18) Similarity Bias
Definition: You have a tendency to appreciate people who are similar to yourself.
Scenario: Preferring to invest in founders or startups that share similarities with the investor, like alma mater, which may limit the diversity of the investment portfolio.
19) Spotlight Effect
Definition: You think others notice your mistakes or appearance more than they do.
Scenario: Overestimating the impact of a failed investment, thinking that others in the industry are judging or paying more attention than they actually are.
20) Sunk Cost Fallacy
Definition: You stick with something not working due to what you've already put into it.
Scenario: Continuing to invest in a faltering startup due to the significant resources already invested.
21) Survivorship Biases
Definition: You focus only on the survivors and tend to forget about the ones who died along the way.
Scenario: Focusing on successful startups in a specific sector and ignoring failed ones, which may lead to overestimating the sector’s overall potential.
Surely there exist many more biases, but the above 21 are the most important ones I’ve personally observed throughout the past years. Whatever you do, be aware of them. Next episode will dive into another form of biases: biases and training data for automated screening models.
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If you have any suggestions, want me to feature an article, research, your tech stack or list a job, hit me up! I would love to include it in my next edition😎