Forecasting can help businesses gain a competitive advantage by planning for future demand, consumer behavior, organizational health, and more. There are several different forecasting methods that can be used to make these predictions and the one that will work best for your business will depend on the data you are using and the predictions you are trying to make.
Fortunately, there are many forecasting tools available to help you make these forecasts more easily and empower your marketing and operations staff to make more informed business decisions.
Here’s what you need to know about forecasting:
Forecasting is the process of using historical data to predict future events (source).
When it comes to business forecasting, we are going to raise this definition one further by saying that accurate forecasting needs to factor in external data.
External data is data that is outside the control of the business. This can include interest rates, weather patterns, and unemployment rates, along with other demographic and economic factors. External data helps you understand the entire environment that your business operates in so you can predict how it will do in the future.
Sales forecasting is the process of using past sales while also considering advertising, pricing, and the supply chain to predict future revenue.
That is sales forecasting in its most basic form; however, a real company may craft a sales forecast more like this:
A group of motorcycle dealerships have their past sales data and are trying to predict what their sales will be in the next quarter. In order to create their sales forecast they look at their historical sales, historical advertising, historical promotions, external macro-economics, and weather factors. Some examples of factors include low interest rates and low unemployment. They take all of this data and use forecasting tools to get a prediction of what their sales are expected to be.
Businesses create forecasts by using different tools and techniques to take historical, internal, and external data to help predict outcomes like demand, revenue, and sales. Forecasting is used by both large and small companies alike. All that is needed is some type of past data and an idea of what outcome you are trying to predict.
If forecasting is done accurately, it can allow your business to gain a competitive advantage.
Forecasting can help your business preview what’s to come and allow you to plan for your budget, future demand, consumer behavior, organizational health, and logistics:
Accurate forecasting can help you get a better idea of sales, revenue, media spend, demand, and more.
It’s easier to anticipate something when you already have an idea of what the outcome could look like. It allows you to have an accurate estimate to manage your inventory and fulfill consumer demand without a surplus.
Let’s look at a simple example of this concept below:
You are a business that normally sells anywhere between 30-60 cheese wheels a year. Based on previous years you decide to purchase enough raw materials to make 60 of them. However, this year you only sell 45 cheese wheels, wasting some of the raw materials. If you had done a forecast of demand this year you could have made a more informed decision and would have only bought materials for 47 cheese wheels, wasting less money.
Forecasting allows for businesses to better understand if they are growing or shrinking in size. If a business is certain that their demand for the next quarter is larger than they have the capabilities for, modifications need to be made. They can make plans by hiring more people, getting more equipment, and making necessary logistic adjustments.
A forecast can also let you know that your company might be heading into the red. You may have to make necessary changes in advance like employee buyouts, hiring freezes, budget freezes, and so on.
Understanding past and current customer behavior can be a powerful tool to predict and forecast future customer behavior.
Let’s say you own a gym and have customers on a monthly subscription. You can analyze their customer behavior to evaluate the number and frequency of their visits, the number of amenities utilized, whether or not they have referred a friend or family member to join, etc. All of this data can be valuable predictors of when customer attrition may happen and allow you to not only forecast customer attrition and retention, but also develop marketing plans to re-engage customers who may show signs of upcoming attrition.
There are many seasonal businesses that require flexible staffing models to ebb and flow with demand. A tire service and maintenance shop in the Midwest, for example, may see significant increases in demand for their service and products as the weather changes from fall to winter and impacts roads.
The ability to accurately forecast when to ramp up or ramp down on seasonal workforce during specific months, weeks, days, or even hours of the day can optimize the business’s ability to maintain the most effective staffing. This can minimize times when staff are either overloaded with work or waiting around for business to come in.
Forecasting can help you by using current and past demand to plan for tomorrow. If there are certain products that are high in demand you can restock or order items in advance with an accurate forecast.
Forecasting can help you navigate supply chains because your company is prepared for the future, so you can order ahead to account for longer wait times or adapt to new materials that your business will need later.
There are several different forecasting methods that can be used to make these predictions and the one that will work best for your business will depend on the data you are using and the predictions you are trying to make.
Qualitative forecasting is a method of making predictions about a company’s performance through market research surveys, focus groups, and industry experts. This involves analyzing the relationship of past strategy and performance and potential future opportunity. Benefits of qualitative forecasting include the ability to gain direct customer feedback and the use of information from industry experts.
Qualitative forecasting can be very helpful for developing marketing campaigns and highlighting elements of the business that are resonate highest (or lowest) with the target customer
Time series forecasting is utilizing historical time stamped data, analyzing the observations, and building predictions based on prior trends and performance. It is not an exact prediction, and the likelihood of forecast scenarios can vary both on historical factors and factors outside of a business’s control. However, forecasting insight around which outcomes are more or less likely are valuable for strategic decision making.
Time series forecasting works best when there is clean time stamped data, and where there are historical trends and patterns to be identified. Analysts and modelers can distinguish between random fluctuations, outliers, and season variations to derive insight.
Time series based forecasting is helpful in identifying which direction the data is changing.
Much like time series forecasting, econometric forecasting utilizes historical data but also evaluates the impact of exogenous factors (weather, economic, demographic, public health) on the parameter of interest (sales, demand, etc.). This approach reveals relationships between these factors and sales.
Econometric forecasting shares the same benefits and drawbacks as those outlined for the time series method and is a great way to further incorporate additional factors into the forecast to improve upon and make the forecast more resilient.
There are many forecasting tools available, ranging from built-in functionality within Microsoft Excel to advanced machine learning tools embedded directly within data science and business intelligence tools in Domo, Aleryx, DataRobot, etc. For those that want to do it themselves, there are endless packages available to tackle this in R and Python as well.
At Ready Signal, we are providing the means of quickly and easily incorporating external factors into your forecast.
For example, one of our customers in the health food industry was recently experiencing production and supply chain challenges and needed to build an accurate forecast of demand to determine when to ramp up or slow down production efforts to effectively fulfill new customer orders. To do this accurately, they needed to identify some leading indicators for their business.
Ready Signal was able to analyze their historical demand data and discovered that disposable income with a three month lag was a strong predictor of their upcoming orders. By including this leading indicator with their historical trend and seasonality, they were able to build a forecasting model to manage their inventory and production three months in advance and optimize their ability to fulfill new customer orders.