Managing a business comes with a lot of risks. When your financial gain depends on people you’ve not met before, unpredictability is to be expected. Although spontaneity is appreciated in many aspects of life, it’s usually not a good tactic when it comes to growing a business. To avoid most risks, businesses employ a convenient value by the name of forecasting. What’s that you ask?
It’s quite direct when it comes to its definition. Simply predicting the possible outcome or reception of your product based on prior performance and statistical data pertaining to the market is not quite simple when it’s being put into effect, let me explain:
Why do you have to forecast
Mainly, because of three reasons,
- To understand where your business is headed. You’ve got to know your target audience and how they might behave depending on how they’ve behaved before. This helps in providing products that’ll keep them investing and you’ll be able to figure out what might be the problem if they don’t.
- To supply according to demand. Forecasting helps in estimating the number of people interested in investing in your product. If supply is more than demand, you’ll be facing a loss and if it’s the other way around, you might lose the interest of potential customers for you don’t have enough supply.
- To function seamlessly. Letting the business go along blindly might work to some extent but growth may not be obvious. Forecasting helps in establishing clear goals for the company to work towards and makes sure the revenue is distributed accordingly.
Ways to forecast
Primarily speaking, there are 2 ways by which you can go about forecasting. The first one is statistical / machine learning based forecasting, where you gather data of the past and present, from your target audience and plan accordingly. There are a few ways to gather the data, namely –
- Survey – The most direct way of approaching a product. Ask whether they want it and in what ways they would prefer it. Example, what’s the demand for vacations on the moon.
- Associative – How a certain decision by the company might impact other factors related to the product. Example, the impact on sales due to a 10% increase in the price of Jam.
- Averaging – This applies to products that have steady importance regardless of seasons or trends. It’s applying the data gathered in the past and assuming it’ll be replicated in the future. Example, the demand for toothpaste in the market.
- Seasonal – Certain products gain prominence during certain times of the year. A thorough understanding of the behavioural patterns of your customers helps in planning product launches that promise the utmost financial gain. Example, demand for Mother’s Day cards during specific times.
- Trends – These aren’t particularly predictable, but when they happen it’s vital that you analyse how your products might come into play with the latest trend before the talk dies out. Example, the popularity of a new television series.
The other way to forecast is experience based forecasting. With time, managing a business builds up your judgement and instincts on what may work and what may not. Usually, this is how you start approaching a product, before backing it up with quantitative data. However, we could use this experience as an apriori for a machine learning based forecasting if we are given with the data.
Forecasting should be done to project a timeline over the next few months or years, rather than about immediate results. When planning, you’ve got to prepare for the worst that could happen, while projecting the best turnout possible.
But before you start considering this as the best thing since sliced bread, you’ve got to ask the question, can forecasts go wrong?
Even with all the data in the world, it’s never possible to predict exactly how the future will play out. Unreliable data, modelling errors, changes or errors in the structure of the company and the simple randomness pertaining to human nature are some of the ways forecasts may go wrong. But the fact persists that humans tend to follow certain routines in their lives. How we gather that data and analyse them, depends on us.
Preparing for the worst
A company may observe unforeseen circumstances, whether they are big or small. When such things happen, as they will, the company needs to be financially strong to deal with them as they come. This doesn’t mean accumulating lots of wealth without investing it even when opportunities come knocking on the door. Proper forecasting gives you a clearer picture, allowing you to budget while maintaining a good enough amount to fall back on if need be.
Imagine a car, with every part running smoothly and fuel, not an issue, there isn’t a limit on how far you go. Similarly, when everyday functions are running smoothly, there exists scope for expansion. Proper segmentation of hours, time and budget makes room to work on new ventures and initiatives. Further forecasting will help said ventures to head off in the right direction as well.
Points to remember
- Don’t rely only on statistical data. At times, the human nature is better at predicting their fellow species way better than numbers ever could. A proper combination of quantitative data and judgement helps in achieving a better turnover.
- Prepare for the worst, project the best. An overcautious hand or a head that’s in the clouds are both blind to the possible alternatives. Both the extremes need to be considered, for being prepared is never a bad thing.
- Forecasts can go wrong. It could be due to internal business disturbances or uncontrollable factors outside of it. Although measures should be taken to reduce human errors as much as possible, the point isn’t to be right or wrong about the outcome, but whether the plan has been laid out expecting a certain outcome. Usually, that’s what makes the company perform better.
As an end note, it’s crucial for the company to think of its future for it to even have a future. If you haven’t been using forecasting as one of the core business value in your management, it’s about time that you did.