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Experimenting With Your Earnings

– Contributed content –

25 August 2017. No matter what field you’re involved with, business is a science. It’s all about determining human behaviors. You need to work out what they mean and find ways to either alter or meet them. You may not be donning a white coat and using a microscope, but your enterprise is an experiment of its own. And, though the results aren’t going to lead to a medical breakthrough, they may save the life of your company.

Scientist at microscope


When you look a little further into startups and their processes, it’s clear to see that the similarities with scientific research go even deeper. If you want to make a go of things, it’s best to follow a set process. And, you need to follow it in every different aspect to tackle business in the right way.

So, you actually undertake a whole plethora of experiments when trying to make a go of things. But, today, we’re going to look at the one which arguably has the most riding on it – the financial experiment. The way in which you approach finances when it comes to your business is a life or death situation. With that in mind, we’re going to look at the science of spending in your enterprise. So, get your lab coat on!

The hypothesis and the theory

Every experiment begins with a hypothesis. This is an outline of what you’re trying to prove. If you were experimenting with a new drug, for example, your hypothesis would be that ‘(enter drug name here) can cure (enter illness here).’.

With that in mind, consider what your spending hypothesis could be. For the most part, it’ll be something concerning the amount of return you wish to see in a set time frame. You may, for example, want to see a 10% return on your investment within two years.

Revenue curve


Then, move on to your theory. This is the way in which you will achieve your hypothesis. Bear in mind that developing a solid theory takes research and work. Scientists can’t just charge in with a new drug and hope for the best. They have to consider the different elements and think about the part each plays.

Do the same for your business. Consider your audience, and how best to get them to buy from you. Consider your expenses, and how much you would need to sell to see a profit.

The experiment and the analysis

With your hypothesis ready to go, it’s time to start the experiment. This is the make or break moment of the process. If all goes well, your theory will prove your hypothesis is true. If not, you’ll need to return to the drawing board.

The experiment with your business expenses will be your first few months of trading. You’ll need to analyze sales and customer data to get a clear understanding of your progress.

There are two possible outcomes to an experiment: your hypothesis will prove true, or false. If it proves true, you’ve seen a return and you don’t need to worry. You can carry on putting your hypothesis into practice when it comes to finances.

But, what do you do if your hypothesis proves false? For one, don’t lose hope. There’s no right or wrong answer in science. Even a false hypothesis can teach you a lot. All you need to do is take it back to the drawing board

The second hypothesis

Instead of giving up straight away, find a way to finance your second experiment. You have the facts and figures to make things work; you just need the backing for a second attempt.

In science, this would involve applying for grants and extra time in the lab. For your business, it could involve borrowing money, or opting for an installment loan to see you through. To make sure the move pays off, analyze your data as much as you can. Consider how far off your first hypothesis was. Is there an easy fix to the problem? By looking at the habits of consumers, your problem areas should become evident.

Comic figures on bar chart


Include these in your second hypothesis. Did you overestimate sales? Or, did those sales simply not bring the figures you were expecting? Amending your marketing or costing methods could be all it takes to get you where you should be.

Or, perhaps the issue was with your timeframe. Did you expect to see results faster than you got them? Consider this from every angle, so that you can hit the mark with the experiment that follows.

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