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Creating and using AI sales forecasts is essential for any B2B company. It is also one of the most important tasks for sales managers who use sales forecasts for sales planning and strategy.

There are many ways to create sales forecasts. From sales rep surveys and estimates of future sales to rule-based Excel calculations or business intelligence applications. But the most unbeatable predictions use artificial intelligence. This article discusses why this is the case.

Roughly speaking, sales forecasts help companies gain some planning certainty in an uncertain future. The goal is to allocate resources efficiently, save money and costs and increase sales.

But this only works if the forecasts are reliable – or precise. For many B2B companies, sales forecasting is a never-ending story. Precise sales forecasts are essential and promise great opportunities – but they still don’t work regarding implementation. What is the reason for this?

Two Main Problems with Making Accurate Sales Forecasts

Anyone can make forecasts: “Next year, on 01 May, it will be 25 degrees Celsius in Karlsruhe.” Writing that was easy. What is this forecast based on? Probably on wishful thinking.

So, the sticking point is with “precise” forecasts. Predictions based on proven models can demonstrate a certain probability of occurrence. A forecast must be at least better than chance. A good forecast should be significantly better than chance. That applies to all types of forecasts and, thus, also to sales forecasts.

In sales practice, many companies deal with sales forecasts and encounter the following two main problems:

1. Some forecasts are based on sales team estimates. “How much will you sell next quarter?” Salespeople are often already annoyed by the surveys. Such estimates are prone to error once a certain number of customers and products are reached. No salesperson can have all the customers and products in their head.

2. Many B2B companies try to find rule-based forecasts and “alerts” with Excel or even individual filtering in business intelligence systems. For example: “If a customer makes at least €10,000 in sales per year, is based in Switzerland and hasn’t bought for more than four weeks, report a churn alert.” This example is, of course, very simplistic. In some companies, IT experts and data scientists sit together to define and programme applicable rules. That is time-consuming and expensive. Nevertheless, beyond a certain number of customers and products, these rule-based forecasts are no longer precise enough to be convincing in practice.

Artificial intelligence makes more accurate Sales Forecasts

If you have a lot of data, AI-based predictive analytics methods are significantly more precise than manual, rule-based forecasts.

A study from 2018 also proves this:
It shows that AI-based models deliver higher accuracy and better temporal adaptability to trends than rule-based models.

The researchers’ conclusion is as follows:

“Machine Learning and Big Data are leading to a paradigm shift in developing a better forecasting model. They have the potential to analyse huge amounts of data and provide instant insights that can significantly improve business performance.”

Source: Mehendale, A. & H. R., Nadheera Sherin (2018): Application of Artificial Intelligence (AI) for effective and adaptive Sales Forecasting. Journal of Contemporary Management Research. Sep2018, Vol. 12 Issue 2, p17-35. 19p.

The goal of predictive analytics is to make the most accurate forecasts possible. Artificial intelligence and machine learning algorithms have given predictive analytics a boost. AI-based sales forecasts with predictive sales analytics are unbeatable. They save time, resources and are less prone to errors – especially from thousands of products and customers.

Advantages of AI-based Forecasting in Sales

1. Data-based sales decisions

AI allows companies to analyse large amounts of data and play out data-based recommendations for action. That enables them to identify patterns or trends otherwise tricky for humans to grasp. For example, AI-based sales forecasting software can calculate probabilities for the future behaviour of your customers. Which customer might be most likely to accept which prices? Which customers are most likely to be interested in which products? Or which customers are most likely to churn?
With these predictions, sales staff can take much more targeted action. The probability of hits increases enormously. Also, consider that every sales action costs something. By managing sales with AI, you can do more with less.

1. Real-time sales forecasting

Real-time sales forecasting is also an excellent example of how AI can improve sales forecasting. As demand for products and services fluctuates in most businesses, real-time data provides information to predict customer needs and behaviour at the right time. That ensures that products are available when customers want them. In this way, you improve both customer satisfaction and business performance.

 
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How Artificial Intelligence Improves Sales Forecasting – Conclusion

AI-powered predictive sales software uses machine learning algorithms to improve the accuracy of sales forecasts.

It can analyse historical B2B sales data and predict future customer behaviour more accurately and reliably than humans or rule-based systems. That gives you the foundation to make better business and sales decisions.

Machine learning models are evolving rapidly and getting better all the time. Just think of ChatGPT. So it is with AI-based sales forecasting. Don’t close yourself off from using AI-based technologies, or your competitor could overtake you.

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Further Read:
 

Vivek Menon (2022): How to Use AI to Improve Sales Forecasting Accuracy? Ed.: Geekyants



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