As with the human mind, companies too have Cognitive Bias. After all, Companies are the collective intellect of the humans that run them.
With Coronavirus Pandemic restricting the mobility, Digitalization is once again on the focus of executives. Where the potential of digitalization is often measured by return on investments, the number of tools deployed, efficiency gain, etc.
Often these questions are answered with a ‘collective’ bias to approve or reject new ideas or to justify the success or failure of digitalization.
The success of such digital projects is destined on the chance probabilities which can go either way, as they are driven by cognitive bias and not thought-through objectively.
1. Anchoring “Google-Facebook-Amazon” Bias – The most prevalent, yet most difficult to remove objectively.
We often benchmark the “rest”, against what was made available “first”.
Companies going for digitalization often follow or pitch themselves against the digital economy giants or the startup Unicorns. We all want to replicate the ideas of Uber, Airbnb, Amazon, Facebook, and whatnot, while it is good to get inspired by the leaders, they often have different value propositions than what you have as a company.
The problem with Anchoring Bias is that it sets the foundation for other biases to kick in.
2. Confirmation Bias – Company executives start looking for data to strengthen their Anchoring Idea. A bias that originates from a bias.
The first principle is that you must not fool yourself and you are the easiest person to fool – Richard P. Feynman
Google pumped a lot of money in GooglePlus to dethrone Facebook but eventually has to be shut down after being in existence for 9 years.
They also tried multiple communication platforms against WhatsApp namely Allo, Hangout, Duo. [1]. All to be shut down without any success.
Such copy-cat investments are not unique to Google but are often seen across the industry.
3. Sunken Cost Fallacy Bias – Company executives often hold on to the past poor investments just because they had heavily invested in them emotionally, money and time, while they have already failed.
As seen above most of these emotional investments are driven by Anchoring Idea and Confirmation basis, yet companies continue to pump in more money in their digital projects. Companies even start playing with data to justify their investments and viability of the idea.
4. Sample Size Neglect Bias: This is the most sophisticated and pseudo-objective bias of our time. Driven by the need to justify “The Sunken Cost Fallacy” bias, companies trick themselves by analyzing too small or too large data sets and even selectively start calling out data sets showing the reality as outliers. All to identify justifiable patterns that will help them to continue investments in an already failed project.
With Data Science in vogue, this is practiced as a cult by Data Scientists, often using heuristics to justify decisions of executives, strategy consultants or miss-sells.
5. Barnum Effect Bias – Companies usually hire third-party consultants from top-notch research and advisory companies or consultancies to drive digitalization. While there are many positive sides to getting an external expert but what companies are actually looking for is an “Astrologer”.
Companies look for an Astrologer in the guile of an Expert.
Consultancies know this for a fact and give suggestions tailored specifically to suit the personalities who hired them.
These suggestions are so vague and often a balanced bundle of positives and negatives that they can be driven towards an Anchoring Bias (Idea).
While we can carry on identifying with different biases (and there are dozens of them), but those discussed above often results in strategic blunders. It is therefore important for companies to first figure out their Digital Strategy and then drive digital projects objectively. You can read more here on how to create an effective digital strategy for your organization.
[1] https://killedbygoogle.com/
Attribution & Disclaimer – Photos by KOBU Agency & Christopher Rusev. This article is the personal opinion of the author and not related to his past or present employers.
Originally published on LinkedIn by the author here
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