When should a business close?

It is estimated that 60% of new businesses fail within the first year with the most common reasons being that new businesses don’t invest in the market, don’t plan properly, don’t have the correct financing, incorrect marketing and try to scale too fast. Most frequently new business owners will identify the issues when it is too late meaning that they will already be having to many problems for a simple correction. 

 

Is there a correct time to say enough is enough and start to close down the business or ask for help? Is there a simple metric anyone can use to achieve this? 

 

The answer is yes, 37%. 

 

The principle of the 37% rule is a mathematical theory for optimal stopping however it does become more complex. In this article we shall try to summarise the theory into its basic components. 

 

The 37% rule was dreamt up by mathematician Merrill Flood in 1958 #biography who developed a problem known as only the secretary problem. 

 

The secretary problem demonstrates a scenario involving optimal stopping theory that is studied extensively in the fields of applied probability, statistics, and decision theory. It is also known as the marriage problem, the sultan's dowry problem, the fussy suitor problem, the Google game, and the best choice problem. 

The basic form of the problem is the following: imagine an administrator who wants to hire the best secretary out of rank able applicants for a position. The applicants are interviewed one by one in random order. A decision about each particular applicant is to be made immediately after the interview. Once rejected, an applicant cannot be recalled. During the interview, the administrator gains information sufficient to rank the applicant among all applicants interviewed so far, but is unaware of the quality of yet unseen applicants. The question is about the optimal strategy (stopping rule) to maximise the probability of selecting the best applicant. If the decision can be deferred to the end, this can be solved by the simple maximum selection algorithm of tracking the running maximum (and who achieved it), and selecting the overall maximum at the end. The difficulty is that the decision must be made immediately. 

(Wikipedia, https://en.wikipedia.org/wiki/Secretary_problem) 

 

The secretary problem has also been tested through house hunting, when if you are aiming to look at 100 houses, you should choose the first house that fits the criteria after the 37th house if none are better than any of the first 37. 

 

But how does this apply to business? 

 

There is an estimate that 80% of businesses open without having a dedicated plan to how they are going to grow and scale their business with 50% of the remainder having an informal plan that is not written down or recorded while the final 50% have a detailed plan to work towards. 

 

By creating a business plan that is written down you are able to benchmark your progress against what you achieve and therefore you are able to apply the 37% rule. 

 

For example, if you have a 5-year plan to generate £500,000 then you know you will need to add £100,000 to your turnover year on year. 

 

At what point does the 37% rule come into effect? 37% of the way through your five-year goal at month 22. If at month 22 you aren’t turning over the predicted amount to hit your targets then you need to start thinking about changing the plan. 

 

However, as always, it isn’t as simple as deciding to close down the business. There are other factors to take into account. For example, what other options do you have to grow the business to the intended level? #more 

 

Or is this just a blip that has no relevance to the history of your business, for example, looking at the monthly turnover of the business below the income isn’t as consistent in our previous examples because in business income isn’t as consistent as we all like it to be even with a subscription-based model once you start to include projections in attrition, therefore we as business owners have to make predictions about the future. 

 

But how can you make these predictions? 

 

Predictions can be used by benchmarking the actual turnover against the forecast and creating a trend line, ideally, you want the trend line to cross above the forecast by the 37% mark of your plan as below. 

 

What this shows is that even though the crossover happened on the 22nd month of trading the company in question knew that they had a product that would scale in time and not follow a linear projection as their first 2 years would be heavily developed on research and development.