Demand Forecasting for Business Growth
In its most basic form, a forecast predicts future occurrences. Demand forecasting, in a corporate context, is the process through which demand planners attempt to estimate what demand for a specific product or service will be in a particular period, which can be a week, a month, or eaven a year. Its only goal is to find an accurate answer; hence demand forecasting is heavily data-driven. Demand forecasting is a critical part of many businesses. But it can be challenging to get accurate predictions without the correct data. Here are seven data types 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:
It aids in decision making, a better understanding of consumers, and increases future organisational performance. Sales executives must 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 buy. Look at past sales patterns to see what customers 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. 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 sunny, 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, region, or market’s financial health or well-being. It is frequently provided in contrast to previous measurements and is used to power economic research and supplement other financial data. This is not only in terms of financial aspects, but it also impacts 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 understand where the economy is heading.
4. Competition data:
You need to worry about not just your own products when making predictions – you also need to consider what your competitors are doing. A competitive analysis clarifies how a company operates and finds ways to outperform them. It also allows the business to remain on top of industry changes and guarantees that the product’s current market demand is met by the development and continuously exceeds industry standards. 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 customer information 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 this significance 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 possible about their demographics, likes and dislikes, and spending habits. The more you know about them, the better you will be able to predict their behaviour.
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 think and talk 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 behaviour, and other factors impacting demand. Use market research to supplement the other data you are collecting and get a complete picture of what is going on.
Demand Forecasting: What Role Does It Play in Supply Chain Management and Demand Planning?
Demand forecasting’s importance may be seen in the many industries where it is used. Retailers, for example, stock up and renew products regularly throughout the year, whereas a company’s inventory is typically counted once a year.
Demand forecasting estimates demand, supply, and pricing within a specific industry in supply chain management. To forecast an industry’s future, it examines the competition, obtains data from suppliers, and assesses historical patterns.
Forecasting is an essential ability for a supply chain manager to have, and it comprises a wide range of abilities that should be developed as one’s career grows. Other reasons for demand forecasting’s importance in supply chain management and demand planning include the following:
Conclusion:
With these seven data types, you can understand 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. Historical sales data predicts future sales volume and 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. Creating an accurate demand forecasting model can be tricky, but with these 7 tips, you will be on your way to success!
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