Skip to content

Sales

How to get accurate sales forecasts with demand forecasting 

How many units of that new product will you sell next year? Or how many units of last year’s biggest seller? Having some idea of what your sales might look like can be essential to business planning. It can affect how much raw material you order if in manufacturing, or how many units to order if you are in retail.

Demand forecasting has always been an important part of any business as it looks ahead to the next year or quarter. In recent times, demand forecasting has become more accurate, thanks to the advent of AI-powered machine learning models that can analyze data and identify relevant patterns. Just what is demand forecasting and how can it use machine learning (ML) to help improve your business?

What is demand forecasting? 

Demand forecasting is a process that allows businesses to predict future demand for their products and/or services. It looks at a range of internal and external factors such as your historical sales data, the current market trends (and if those trends are growing), and also how your customers are behaving and thinking when it comes to their wants and needs. 

Verified market research estimates that the global demand planning software market size was valued at $8.2 Billion in 2023 and is projected to reach USD 15.58 Billion by 2030.

Short-term demand forecasting can be very accurate but becomes less accurate as you extend your prediction window. It’s going to be easier to consider what are B2B sales going to be in a year than it is if you’re thinking five years ahead. In recent years, the accuracy of demand forecasting has improved with the introduction of artificial intelligence (AI) and ML. 

Why is demand forecasting important?

Demand forecasting can be essential for any type of business and any data-driven sales strategy. In manufacturing, it can help plan the ordering of raw materials and can be essential to supply chain planning. While different sectors may have different benefits, there are some common ones that cover nearly every type of business. 

  • Inventory management. Efficient inventory management ensures that you have high enough inventory levels to meet demand. However, you also want to avoid overstocking any products and facing the costs of storage or warehousing. 
  • Risk management. Risk management planning and mitigation strategies are an important part of any business planning. Having some idea of what will sell and when can be integral to a good risk management plan. Like inaccuracies in demand forecasting, payroll mistakes can cause significant financial and reputational damage, underscoring the need for precise business management.
  • Good business planning. You don’t have an unlimited budget, whether for buying products or for marketing them. With demand forecasting, you can have an idea of where to focus your efforts in procurement and marketing strategy. Incorporating predictive sales analytics into your forecasting process can further refine these strategies, providing deeper insights into potential sales trajectories and enabling more targeted resource allocation. 
  • Customer expectations. Brand reputation is important, as is customer retention and loyalty. By meeting the expectations of future customer demand, you can achieve both business goals and stay ahead of your competitors. Data from your CRM can help you understand both expectations and likely behavior. 
  • Market trends. Market trends play a big part in your business planning. You know that seasonal fluctuations will see an increased demand for Christmas trees in the run-up to Christmas, but what about other trends? Effective product research is vital to identify and respond to these trends, enabling businesses to stay competitive and adaptive. You also need to be able to look at possible changes in economic trends. Will a downturn adversely affect demand? 
  • Changes. What happens if you replace a current product with a new one? Or update it (as often happens with SaaS products)? Will any changes be welcomed by your customers? Demand forecasting can help you predict this too.

Rise of the machines

Artificial intelligence and machine learning are two of the ‘buzz’ phrases of recent times. 

Grand Review Research estimates that the global machine learning market size was valued at $36.73 billion in 2022 and is expected to grow at a compound annual growth rate of 34.8% in the period from 2023 to 2030.

The use of ML in demand forecasting has added a higher level of accuracy to the practice. With AI algorithms and ML, it’s possible to analyze huge datasets to make a more accurate forecast based on a very wide range of data.

Before the introduction of AI and ML, demand forecasting relied on fewer factors and less data. ML can spot patterns that may escape the human eye which means that businesses can be more confident when looking at any forecasted demand. 

As well as increased demand forecast accuracy and analyzing more data, ML demand forecasting can offer an automated and streamlined process that can save you time and costs. It can also adjust quickly if there are any changes, whether in the market itself or the relevant economy. 

How to implement ML-based demand forecasting

While there are some ethical concerns over the use of generative AI applications, there is no such worry when it comes to AI and ML in demand forecasting. The big question for many businesses is how do they get on board the ML-based demand forecasting train. 

1. Choose the right software 

As with any new technology or tool, you want to choose a program that suits your business. As always, budget is a good starting point and it makes sense to shop around and also look for software that offers you a free trial so you can see whether it is a good fit. Once you have considered the budget, there are some other things you should be looking at.

  • Ease of use. Is the software easy to use and intuitive? Will the relevant staff need training on how to use it? Is it a code-based program or a no-code platform? Looking at how easily you can get the program up and running should be a major consideration. 
  • Scalability. Every business wants to grow, so scalability should be included in your chosen program. You want demand forecasting software that will work with your current business size but that won’t lose any accuracy if you grow to twice that size. 
  • Compatibility and integrations. There is little point in choosing software that is not compatible – or will not integrate – with your current tools and systems. If you do, then you will face added expenses in replacing older tools. Look for integrations with your system that helps with the process. 

Read also: Unlocking the potential of AI tools in sales

2. Clean and quality data

Even the best ML demand forecasting system will struggle if the data it’s presented with is low quality. Given you want to include a wide range of data, from market research to inventory and from market data to sales forecasting, you want it to be of the highest quality. If the data is of low quality, then it will adversely affect your demand forecasting results. 

Have the team managing your data ask questions such as, what type of schema or what help will data lakes be? Have them undertake thorough processes of data cleaning and data validation so that they can transform datasets into formats your chosen system can analyze easily. 

3. Build a model that fits your business

Just as the software you choose needs to suit your business, the demand forecasting model you build has to fit it too. Start by identifying the problem or goal you want to resolve and then select the features of the model you want. You can then choose an ML algorithm that will – hopefully – give you the desired results. You can break down your model building into several steps. 

  • Prepare the model and upload any relevant files.
  • Ensure the data you are using is clean and of high quality. 
  • Set up the model with the desired period of time, parameters, and prediction fields. 
  • Train the model as required.

4. Deploy

Before you let the model loose on making actual predictions for your business, you need to be sure it’s working properly. You can do this by testing it with real-life data where you already know the outcomes. If everything works well, then you can move the model into your business processes and let it start making predictions. 

Ensure the model you have built is scalable to include analysis of the increased data you will be feeding into it as time goes on. You also need to be sure that it can handle real-time data. Things really can change at the drop of a hat, from market or industry trends to national or global economic performance. 

Read also: AI in sales: Will AI replace salespeople, or boost them?

5. Monitor constantly 

You want to be sure that the model you built continues to be accurate. That means monitoring outcomes of informed decisions made based on actual sales compared to the actual demand forecasts it gave. For example, your model may have predicted 800 future sales in Q1 and 600 in Q2. If those predictions were accurate then you have a baseline to compare future predictions and outcomes to. 

One way to do this is by setting up a CI/CD (continuous integration/continuous deployment) pipeline. This will monitor and report on how close predictions are to the actual results. If you see any deviation in accuracy, then you may need to look closer and consider retraining your model. Having the CI/CD pipeline alert you to any major changes in accuracy can prevent anything from going majorly wrong. 

The takeaway 

Accurate demand forecasts can make a huge difference to a business in terms of efficiency and profitability. They can look beyond known factors such as any seasonal trend and drill deep down into the data provided. The efficiency of everything from procurement to marketing campaigns can be improved. Trends are one of the keywords here, including market conditions and trends, consumer behavior, and patterns in economic performance. 

The forecasting process – and forecasting accuracy – can also play a major role in operational efficiency. By combining qualitative methods, quantitative methods, and the most-suited demand forecasting methods, you can streamline processes to achieve peak efficiency. Whether you are looking at short-term forecasts or longer-term predictions, machine learning can help you with more accurate forecasts.

Prev:

What is a contract obligation? The essential guide

Next:

How to optimize your B2B SaaS marketing funnel to reduce churn rate

Related articles

How to integrate digital contracts with HubSpot-Workflows? - Oneflow
Contracts

How to integrate digital contracts with HubSpot Workflows with example use cases

Emailing contracts This everyday practice could ruin your contract process - Oneflow
Contracts

Emailing contracts: This everyday practice could ruin your contract process

The future of AI in legal: Don't confuse ChatGPT with my law degree - Oneflow
Work & Culture

The future of AI in legal: Don’t confuse ChatGPT with my law degree

Why Oneflow is the ultimate contract management software for modern businesses - Oneflow
Contracts

Why Oneflow is the ultimate contract management solution for modern businesses

Improving procurement processes through contract management software - Oneflow
Contracts

Improving procurement processes through contract management software

Product Updates

Never miss an update with Oneflow x Slack

What is a signatory? Oneflow guide
Contracts

What is a signatory? A guide to signatory rights and responsibilities

How to print a Word document? - Oneflow
Work & Culture

How to print a Word document?