Making smart business decisions requires in-depth business forecasting. So, what does this mean, and how can you do it well?
Business forecasting is a vital tool for business decision-makers across all industries. It can help your business cut costs and assist with general operational management.
But what exactly is it, and how and why should your business management team utilise it? This guide will cover the ins and outs of everything related to forecasting – from your workforce to the overall business.
Business forecasting refers to the data analysis process which allows a business to predict future needs and make insight-driven growth decisions. A quality business forecast should provide clear, real-time performance visualisation while simultaneously facilitating fast analysis and streamlining business planning.
Past data is collected and analysed using either quantitative or qualitative models during the business forecasting process. Management can then identify patterns in these data sets and subsequently determine any action that should be taken.
Forecasting is an indispensable business management tool. It can be critical, especially in situations when the future might appear uncertain or when management is making an important strategic decision.
This is because it provides information that aids managers in identifying and understanding any planning weaknesses, adapting to changing circumstances and – most importantly – achieving effective control of business operations.
Business forecasting can also support managers in generating reports easily and provide a better understanding of predictions. The more the business can focus on the probable outcome, the more successful it’ll be moving forward.
Here are some of the other major advantages of creating effective forecasting for your business:
By thinking and planning in advance, you’ll be better prepared for upcoming market demands and avoid the extra expenses associated with an unexpected demand influx.
Your business can create decision strategies that help you thrive in the face of market challenges before they morph into costly surprises.
Demand planning can benefit the customer, too. You’ll be able to offer them what they want, whenever they want while meeting their expectations with the correct business forecast. Retention and loyalty rates will often increase if you can meet demand targets in your market.
Forecasting allows you to collect and analyse historical company data. Tracking this data can help you identify where you might have previously made poor decisions and provide the necessary information to ensure you avoid comparable mistakes in the future.
Whether you look at the short-term or further ahead, tracking progress can help you outline future business pursuits.
Business forecasting can be split into business areas, including finance, sales and demand.
Find out more about each type below…
Financial business forecasting allows a business to project its overall monetary value using current assets and liabilities for an economic period.
Demand forecasting predicts the future needs of your target market. Whether this is the type of products and services they want or the quantity, any business should value these predictions as a robust way to enhance customer satisfaction and maximise profits.
This does exactly what it says on the tin – general business forecasting predicts the overall market trends and external factors affecting the overall success of your business.
Sales predictions look at the expected success of a company’s current offerings and how this could affect future sales and cash flow.
Forecasting can be carried out across a business and its operations to help improve and streamline them. Other types of business forecasting include accounting, budget, capital, supply and workforce.
The general process of business forecasting entails…
The two main methods for business forecasting are quantitative and qualitative. Both have unique approaches but are similar in their goals and the information they use to create predictions. Find out more about the two and how to choose the right one for you below…
Quantitative forecasting relies on historical data to predict future needs and trends within a business. The data can come from the business itself, market activity, or a combination of the two.
Quantitative data can focus on hard numbers showing the course for change and action.
So, when is it best to use quantitative forecasting methods?
This method is most beneficial to companies already armed with an extensive dataset. If you already have the data, you save time and money since you don’t need to expend further resources collecting it.
There are four methods within quantitative forecasting:
Trend series method
This is the most common forecasting method since it uses past data that has shown reliable results. Simply use this historical data to identify common patterns over time.
Indicator approach
This approach follows different sets of indicator data that help predict potential influences on the general economic conditions, specific target markets, and supply chain. Some examples of indicators include changes in Gross Domestic Product (GDP), unemployment rate, and Consumer Price Index (CPI).
Businesses can easily predict how these potential influences could affect business needs and profitability by monitoring and observing how they interact with one another. This approach would be the most effective for companies heavily affected by specific economic factors.
The average approach
This method is based on repetitive trends and assumes that the average of past metrics will predict future events. Businesses will most commonly use this approach for inventory forecasting.
Econometric modelling
Taking a mathematical approach, this method uses regression analysis to measure the consistency of company data over a set period. Regression analysis uses statistical equations to predict how variables of interest might interact and affect a business. The data utilised can be internal datasets or external factors affecting business operations, such as market trends or GDP growth.
Meanwhile, qualitative forecasting relies on the input of those influencing the success of your business. The target customer base and your leadership team can provide this input to help you make accurate business predictions.
This method is most beneficial for businesses that don’t have enough complex data to conduct a quantitative forecast.
Again, this type of forecasting contains different methods, which we’ll explain below…
This is the process of collecting data points via direct correspondence with the market community. For example, this could involve conducting surveys, polls, or focus groups to gather real-time feedback and opinions from customers or the leadership team.
Remember: there are rules surrounding data compliance that must be adhered to when collecting customer information.
Market research also studies the competition to see how they adjust to market fluctuations and adapt to changing supply and demand.
Businesses usually use this to forecast expected sales before launching a new product or service.
The Delphi method collects forecasting data from business professionals. The future needs of the business are presented to a panel of experts, who collaborate to forecast expectations and decisions that can be made with the gathered insights.
Businesses will use this method to create long-term business predictions and sales forecasts.
Workforce forecasting can answer questions about capacity and how projects might impact your teams, but what is it?
This type of forecasting refers to the use of historical and predictive data to help identify trends, much like the other types of forecasting.
You can use data to ensure your workforce can handle the workload you’re giving them while also ensuring they aren’t spread too thin.
Customer care management is a huge aspect of workforce forecasting, and if your goal is to improve service and satisfaction, then this could be the right starting point for your business.
For example, your management team might identify that customer retention rates are low and require improvement. Workforce forecasting could then use quantitative and qualitative data collection methods to investigate why customers aren’t returning. From this analysis, you can spot trends and figure out a plan for increasing the retention rate.
You might discover that this is due to customer service agents fielding too many queries and not being able to provide each customer with the care, attention, and empathy that they need. You can then make changes based on this – such as hiring more agents or bringing in automation – and predict that these should improve your retention rates.
Don’t forget to track your progress and see how forecasting has helped you reach your goals.
