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Are You Guilty of These 3 Biases That Kill Most Startups?

Founders succumb to three common cognitive biases that sink most startups.

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We make over 35,000 decisions every day — from subconscious ones such as deciding the side of the bed to wake up from, to more strategic ones such as when to pivot our startup.

Unfortunately, many of these decisions end up being flawed. 74 percent of startups go bust because they decided to scale a little too early. When nine out of every ten startups fail, one wonders why founders end up making bad decisions so often. As an entrepreneur, this gives me the shivers.

According to Tom Eisenmann, professor at the Harvard Business School (HBS), there are recurring patterns that explain why many startups come to nothing. After studying hundreds of founders and investors, he shares the findings in his book, Why Startups Fail.

Let’s look at three of these failure patterns that are most common in early-stage startups. We’ll discuss the top cognitive biases that play out behind the scenes using real-world stories. We will then discuss actionable recommendations to tackle each failure pattern.

Related: Expose Cognitive Biases For What They Are

The three most common failure patterns in startups

1. False Start

In his book, The Lean Startup, Eric Ries recommends scaling businesses iteratively by building out minimum viable products (MVPs). However, when founders don’t truly understand the customer’s needs before commencing their engineering efforts, the MVPs end up wasting valuable time and money. These are called false starts.

Startups that run into this pattern of failure often suffer from confirmation bias. They tend to search for, interpret and favor evidence that confirms their prior beliefs. Founders often jump the gun in a hurry to get their product out. They assume that the problem is big enough for the customers to care about and painful enough to pay for.

Take the case of Triangulate, an online startup that set out to build a matching engine for dating sites. The startup failed despite raising funds, building a competent team and making three big pivots in under two years. Eisenmann considers this a case of “wrong opportunity, right resources.” The team launched early and often, true to the Lean Startup principles, but without performing a sound customer discovery phase. It was a false start that killed the business.

2. False Positives

It hurts startups when the response to their initial products is lukewarm. However, it could be far deadlier to get wildly positive signals — when this turns out to be a false positive. Founders are tempted into rapid expansion by an enthusiastic response from initial adopters. However, when the mainstream market needs are widely different from the first customers, it leads to failure.

Humans tend to rely on information that is readily available to them rather than make efforts to look deeper. This is called the availability bias. Founders often make product roadmap decisions based on feedback from the early adopters. If these features are unlikely to appeal to the broader market down the line, it leads to costly pivots or reengineering efforts.

In 1992, PepsiCo introduced Crystal Pepsi, a variant of its flagship brand that looked like sparkling water but tasted like soda. Buoyed by its initial success in test markets such as Denver and Sacramento, the company launched it nationally. They ran expensive campaigns, including a Super Bowl ad. The innovation failed terribly in the broader market and was shelved by the end of the year.

3. Good Idea, Bad Bedfellows

Some startups stumble upon the right idea and get a truly positive response from the market. However, if they don’t team up with the right stakeholders, they invite failure. It’s not just about hiring good employees, but they must also onboard strategic partners and investors with complementary abilities — the right bedfellows.

We tend to gravitate towards people like us — in appearance, beliefs and backgrounds. This is called the affinity bias. When founders shy away from hiring and partnering with people who don’t fit into a familiar mold, they end up with a diversity deficit. This comes back to haunt them in the form of narrow perspectives and faulty decisions that impact startup survival.

Quincy Apparel helped young women find affordable and stylish work apparel that fit them well. The startup founded by HBS graduates had an attractive value proposition that resonated with target customers. However, the team they assembled lacked specialized apparel production expertise. They missed onboarding advisers and partners to help bridge the gaps that led to shipping delays and unsustainable inventory levels — factors that ultimately sank the startup.

The three steps to tackling startup failure

To tackle the above patterns of startup failure, you must address the underlying cognitive biases head-on. Fighting cognitive biases starts with becoming conscious about their existence. Once you know about these biases, here’s how you can tackle them in your startup.

1. Challenge your assumptions

To avoid false starts, we must critically examine our beliefs. When we realize that our deep-rooted assumptions could well be wrong, we can avoid fatally clinging onto them. Tackle confirmation bias by seeking evidence based on your opinion but with the primary intent of disproving it.

To evaluate the potential of product ideas, founders must run customer discovery studies. This exercise should critically test the product’s value proposition, validate why customers would buy it and test how much they would be willing to pay for it. Such a study should be designed to refute a founder’s hypothesis. The idea shouldn’t be taken at face value until proven otherwise.

2. Challenge available data

To distinguish false positives from the real market feedback, we must invest in collecting the right evidence. Getting the right quality data starts with an intent to go beyond one’s comfort zone. To avoid getting blindsided by the availability bias, scout for unusual data in unusual places and from unusual sources.

Today, startups can extract unusual insights by tapping into public data. The value of open, free data is underrated. At the start of the pandemic, OpenTable, an online restaurant aggregator, published data that gave early indications of the falling demand for dine-in bookings. Startups can harvest public data from Google Trends to spot market niches, find synergistic products, explore seasonality and monitor competitor positions.

3. Challenge the desire for conformity

Startups need a well-rounded team for sound decisions at the strategic and tactical levels. A startup’s collective wisdom is measured not by the team’s average intelligence quotient (IQ) but by its diversity — in backgrounds and perspectives. To tackle affinity bias, promote diversity, openness and collaboration.

Diversity is often overlooked until companies run into a challenge. In the early days of YouTube, when about 10 percent of user videos uploaded were upside down, the designers found something unexpected: the mobile app didn’t factor left-handed users who picked up their phones differently. This was missed by a team that had all right-handed people. Ensure that your processes to hire teams, onboard partners and raise funds keep diversity as a prerogative.

Related: The 4 Cognitive Biases Entrepreneurs Should Avoid

Think you got it all covered? Think again

Did you imagine yourself or your startup to be immune to these biases? You aren’t alone. This is precisely what 76 percent of respondents told researchers at Stanford University in a study on cognitive biases. 

Blindspot bias is the tendency to recognize others’ mistakes rather than our own. 

The next time you make a decision, big or small, watch out for easy solutions and natural reflexes. Ask yourself if you’ve challenged your assumptions, your data and your desire for conformity.

Related: 7 Ways to Remove Biases From Your Decision-Making Process


Thanks to Sravani Gadhamchetty for sharing inputs for this article.

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