When it comes to analyzing your company or setting up predictions for company development, there are few things that are as useful as historical data. By learning how to make use of it, you can base your future business plans on facts and not only wishful thinking. Nevertheless, people are surprisingly unaware of how to gather historical data and how useful it actually is. In this article, we will cover the importance of historical data in forecasting. While doing so, we will also give you a few tips on how to best utilize it in combination with your client relationship software for movers.

What does it mean to use historical data in forecasting?

In essence, using historical data in business development is simply applying the scientific method to the corporate sector. You take the data that you have and use tried and true scientific methods to make predictions. If your data is valid, and you use the available methods properly you should achieve a high degree of accuracy. This, as you might guess, is quite valuable for forecasting as it allows you to prepare for what’s to come.

A woman working on a laptop while using historical data in forecasting.
Using historical data in forecasting is one of the best ways to make sound business decisions.

Your goal is to take a look at the history of your company and gather the necessary data. Then you outline the variables and see what those variables can tell you about the future. To do so effectively you need to have an in-depth understanding of your industry, as well as basic knowledge of probabilities. Keep in mind that, just like any other application of the scientific method, using historical data takes practice. You need to gain experience and learn from more knowledgeable people in order to come to valid conclusions.

How to utilize historical data

Using historical data to forecast business development is no simple matter. The more you look into it the more you will see just how many different types of data there are, and different ways to use it. So, take the next steps as a rudimentary guide to the use of historical data. If you do plan on using it, we would strongly advise that you proceed to research your industry and how historical data was researched and used within it.

Outlining what you want to forecast

Before you even start gathering historical data, you first need to clearly outline what you are going to use it for. There are countless types of data you can use, and even more forecasts you can make out of them. Your first goal should be to make your first prediction as precise as possible. For example, predicting the next year of customer relations by using client relationship management software for movers is much better than predicting the future of the moving industry in general in the next ten years. The first allows you to decide on which data is important and which variables to keep in mind.

Gathering the necessary data

A person gathering data.
Figuring out which data is relevant for your forecast is easier said than done.

Once you have your forecast requirement figured out, you need to gather the necessary data. Know that this is a process that you will probably go back to thorough the making of the forecast. Different questions will pop up, and different variables might come into play. So, it is usually impossible to predict what data you will actually need. You can start off by considering the forecast at hand, and figuring out what data will answer it. But don’t expect to gather it all in one go.

Outlining the variables

Having the right variables to make your forecast is one of the most important tasks you need to solve. Unfortunately, it is also one of the hardest. The three main types of variables you need to differentiate are:

  • Dependant variables.
  • Indepenadant variables.
  • Confounding variables.

Dependent variables are those that you are trying to predict. This can, for instance, be the amount of money your company is going to move, the amount of audience you are going to reach, or a marketing goal you want to achieve. The dependant variable is based on independent variables and confounding variables. Independent variables are those that directly influence the dependant. The whole point of gathering historical data is to come up with decent independent variables and figure out how they influence the dependent ones. Finally, it is important to note that you can never take every bit of data into account. Some data, even though it might influence independent and dependent variables, needs to be put aside. That data is considered confounding variables.

Understanding the nature of your industry

Two professional people talking.
Consulting with a more experienced professional is always a good idea.

Once you figure out your variables, you need to see how they interplay with one another. This is usually done through statistical analysis. Unfortunately, we cannot cover statistical analysis here, which is why we’ve linked a decent guide. But, what we can do is outline one of the common mistakes people make when interpreting statistical data. Namely, having data is not worth much if you don’t have the necessary knowledge to place the info within your industry. Anything can make sense without prior knowledge, which is why not everyone can make forecasts. This is also why it isn’t enough to simply read research papers, but also have the necessary background to interpret whether that research is well-grounded.

What is the importance of historical data?

Now that you have a basic understanding of what historical data is, the question remains how useful it actually is. Well, when it comes to project management, using historical data in forecasting is invaluable. Any serious company that needs to make a sizable investment grounds its decisions in historical data. Without it, you are basically relying on your instincts and temporary impressions. And, while instincts are important for successfully running a business, you really ought to base your decisions on something stable. All in all, we can safely say that modern business development is unfathomable without careful use of historical data.

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