Survivorship bias and ‘invisible data’ can be observed across several areas in business and economics:
New Business Start Up: Entrepreneurs look at successful market leaders who have converted concepts into multi billion-dollar industries. Jeff Bezos at Amazon or Bill Gates at Microsoft are often cited as examples of how entrepreneurs have started small and built scaled successful companies.
However, many aspiring entrepreneurs fail to look at the range of data that shows how most start-ups fail. The ‘invisible data’ outlined in the chart below, often fails to influence the strategy for entrepreneurs who show a bias towards those who succeed, rather than the volume of those who fail, and factoring that into their model3.
Supply Chain Logistics Costs: Supply chains are complex and expensive structures that are often targeted for cost savings by businesses through initiatives such as network optimisation, inventory reduction and outsourcing of manufacturing to CMOs4.
In the Logistics space, many large brokers operate and control high profile distribution routes (landing slots and sea routes), preventing smaller, and at times more efficient companies, from competing with their alternative distribution options.
Many of these smaller companies are ‘selected out’ of consideration by supply chain owners who may otherwise generate significant cost reduction.
Hidden Unemployment: Unemployment statistics are a critical indicator of economic strength and performance. Following the 2008 recession, subsequent UK governments were focussed on reducing the level of unemployment across the UK. However, many economic commentators in 2017 have argued that the official government figure of 1.5m unemployed ignored the nearly 800k individuals of working age that were claimants of incapacity benefits5.
This data that was ‘selected out’ of the official unemployment data both paints an inaccurate picture of overall unemployment numbers. In addition, as outlined in the image here6 this also prevents a clearer understanding of where this unemployment is most prevalent – in this case in the poorest areas of the UK. This diminishes the ability of UK policymakers to drive the right outcomes and economic decisions.
Returning Customer Revenue: Many companies calculate their ARR based on statistical data that reflects revenue from their current customer base. This fails to consider those that never returned or rejected the company for a competitor.
This can drive the wrong business culture that assumes the strategy is fit for purpose and that competitors do not pose a long-term threat. Considering the behaviour of ‘non customers’ is a critical piece of invisible data that should be used to influence strategy and assess actual organisation performance.