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7 Types of Data That Every Demand Forecasting Expert Needs

Demand Forecasting for Business Growth

In its most basic form, a forecast is a prediction of future occurrences. Demand forecasting, in a corporate context, is the process through which demand planners attempt to estimate what demand for a certain product or service will be in a particular period, which can be a week, a month, or even a year. Its only goal is to find the accurate answer, hence demand forecasting is heavily data driven. Demand forecasting is a critical part of many businesses. But it can be difficult to get accurate predictions without the right data. Here are seven types of data that every demand forecaster needs in demand forecasting for business growth. With this information, you can make better predictions and keep your business running smoothly.

1. Sales Data:

Any information that is machine-readable and useful to sales teams is considered sales data. It aids in decision making, better understanding of consumers, and leads to an increase in future organisational performance. It is critical that sales executives understand how to assess data and apply its insights to improve their strategy. To acquire accurate and timely insights, the sales data must be of high quality. One cannot predict demand if they do not know what people are buying. Look at past sales patterns to see what customers tend to buy and when they buy it. This will give you a good starting point for your predictions.

2. Weather data:

The more disruptive the weather, the greater the perceived cost to the company. An unexpected storm following a day of high heat is likely to have an impact on demand in a variety of ways, but if that storm develops into severe weather and causes floods that impact stores or supply chains, in that case, businesses need a data-driven plan ready to go and up-to-the-minute updates to know when to initiate an appropriate plan. A severe weather event can sometimes have expensive and long-reaching implications that extend far beyond the activities that are immediately affected.

Therefore, most businesses are investing in learning about extreme weather, natural catastrophes, and other high-impact unforeseen events like COVID-19 rules, health alerts, and terrorist strikes. Some simple examples are that if it is a sunny day, people might be more likely to buy cold drinks or go to the beach. If it is cold and snowy, they might buy more winter clothes or stay home instead of going out. Pay attention to local weather patterns and adjust your predictions accordingly.

3. Economic data:

Economic data assesses a country’s, regions, or market’s financial health or well-being. It is frequently provided in contrast to previous measurements. This information is used to power economic research and to supplement other types of financial data. This is not only in terms of financial aspects, but it also has an impact on various businesses, and understanding inflation and economic recession is critical for business growth. The state of the economy can also affect demand. If people are worried about money, they might cut back on spending and buy only essential items. On the other hand, if they feel confident about their finances, they might be more likely to splurge on luxuries. Keep an eye on economic indicators like GDP and unemployment rates to get a sense of where the economy is heading.

4. Competition data:

It is not just your own products that you need to worry about when making predictions – you also need to consider what your competitors are doing. A competitive analysis clarifies how a company operates and finding ways to outperform them. It also allows the business to remain on top of industry changes and guarantee that the current market demand is met by the product and exceeds industry standards on a continuous basis. If they are launching a new product or running a sale, that could affect how much people buy from you. Keep track of what your competitors are up to and adjust your predictions accordingly.

5. Customer data:

Customer data is described as information provided by customers while interacting with your business through various modes, such as via websites, chatbots, sales calls, mobile applications, surveys, social media, marketing efforts, and other online and offline channels. An extraordinary company plan can be built on customer data. Data-driven firms recognise the significance of this and take steps to capture the essential customer data points to improve customer experience and enhance company strategy over time.

Getting to know your customers is essential for making accurate predictions. Find out as much as you can about their demographics, likes and dislikes, and spending habits. The more you know about them, the better you will be able to predict their behavior.

6. Social media data:

Marketing teams use social media data to review and track the efficacy of their digital media campaigns in real time, deciding what sort of content performs best. There are various channels on which the data performance is tracked. This is done to obtain the data from past, present, and future results to reflect the higher potential of the business.

Social media is a wonderful way to get insight into what people are thinking and talking about. Pay attention to trends on sites like Twitter and Facebook and look for mentions of your brand or products. This can give you a clever idea of what people are interested in and how they feel about your company.

7. Market research data:

Finally, do not forget to consult market research when making your predictions. This can give you valuable insights into industry trends, consumer behavior, and other factors that can impact demand. Use market research to supplement the other data you are collecting and get a more complete picture of what is going on.


With these seven types of data, you can get a good sense of what people want and when they want it. Use this information to make more accurate predictions and keep your business running smoothly. Businesses grow at a larger scale when the demand for them increases. As a result, tracking current data and relying on demand forecasting tools to predict future data helps the business’s long-term profits.

About ThouSense Lite – Demand Forecasting

ThouSense is an AI/ML based, easy-to-use SaaS platform that helps businesses worldwide predict short-term and long-term demand planning and forecasting. You can get automated and accurate demand forecasts of macroeconomic indicators, currency fluctuations, weather and seasonality patterns, etc. With historical sales data, it predicts future sales volume, anticipates demand trends and seasonality without compromising on accuracy. It also helps with financial forecasting, budgeting, scenario planning, and supply chain management for small-to-large enterprises.

  • Free up planner capacity.
  • Higher customer satisfaction index and brand reputation.
  • Increase revenues and profits.
  • AI-based forecasting achieves 99% accuracy.
  • Reduce stockouts and inventory.

Demand forecasting is an essential process for businesses of all sizes. It is used to predict future demand for products and services, to ensure that the correct amount of stock is kept on hand and that resources are allocated in a way that maximises profits. Creating an accurate demand forecasting model can be tricky, but with these eleven tips, you will be on your way to success!

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