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Using Machine Learning for Revenue Forecasting and Pipeline Management

The machine learning industry is expected to generate $31.36 billion in global revenue by 2028, a monstrous increase over its already high performance. This begs two questions: Where’s your slice of the pie? How can you take advantage of that industry growth? 

Machine Learning improvements have given businesses incredible opportunities. Machine learning is now expanding into areas that it has never been. This includes revenue forecasting and pipeline management.

Precise revenue forecasting ensures you are hitting your targets and meeting sales goals. Pipeline management keeps new customers flowing into your business and allows you to improve your bottom line. Both of these are critical business functions.

Revenue forecasting and pipeline management are different functions. However, they have extensive overlap. Both demand appropriate data. Both require a dedicated team of competent employees. Furthermore, both can benefit from machine learning. When applied correctly, machine learning enhances the accuracy of your forecast, identifies new customers for your sales pipeline, and allows you to deploy marketing and staff resources.

Traditional Challenges in Revenue Forecasting and Pipeline Management

Revenue forecasting and pipeline management face similar challenges. For example, both often require manual data entry and search features. These issues can arise from typing data in a spreadsheet or googling for clients to pitch. Unfortunately, these methods are time-consuming and often subject to significant errors. These mistakes can cost your business time and money.

Historical data is critical to creating parameters and guidance for revenue forecasting. To be clear, the lack of data in sales forecasting can be fatal. According to numerous studies, 93% of sales leaders can’t come within 5% of their sales revenue, and 2/3 of all businesses don’t have a formalized forecasting system. This lack of information can be devastating and negatively alter your resource allocation. 

Historical data can inform critical economic considerations or how micro-trends impact your business. In addition, such information can lead you to address the potential impact of marketing campaigns and allows you to steer your resources in the most efficient method. 

This data is also necessary for pipeline management. After all, you can’t achieve account growth going forward if you don’t know what got your account growth in the past. 

This interaction between historical data and multiple other variables can be difficult to manage. For example, how can anyone fully grasp multiple uncertain variables’ potential interactions on revenue forecasting and pipeline management? These challenges are further expanded by the need to update a sales forecast in real-time

Such challenges are often too difficult to overcome. As such, your company may want to consider an investment in machine learning. 

Key Applications of Machine Learning in Revenue Forecasting

Machine learning refers to the creation of specific algorithms and computer programming. When used for revenue forecasting, machine learning can adapt to your business needs. Users can customize these programs to better fit specific applications. As such, machine learning can take multiple factors and adjust them as a human would. This “intelligence” allows for creating a more accurate revenue forecast. 

Machine learning can help your revenue forecasting in many ways. First, it can allow you to adopt various forecasting techniques. These adjustments enable machines to take advantage of predictive or scenario-based modeling. You can then create accurate forecasts that can grow and change based on various inputs. Such inputs include external factors, market trends, and economic occurrences specific to your industry. 

Machine learning can also allow you to take advanced looks at historical data and patterns. As a result, you can create a more robust forecast that accounts for past sales and behavior. Better yet, this data can be automatically incorporated into your forecast, reducing errors and saving staff time. With this automation comes real-time updates, thus ensuring that your forecasts update on a second-by-second basis. These updates ensure you can, in real-time, have updated forecasts that enable more accurate resource allocation decisions. 

Benefits of Machine Learning for Pipeline Management

Machine learning for pipeline management and account growth is an often overlooked area. 

Indeed, machine learning can determine what factors can increase or decrease a prospect’s likelihood of doing business with you. As a result, you can have more accurate lead scores and make better prioritization decisions. This information can enable you to determine sales patterns better and find trends before the competition. With this data, you can make more accurate decisions about what customer segments to target.

Machine learning can help you flag potential bottlenecks, issues, and concerns within your pipeline. This information can allow you to redress these issues before they impact your revenue. 

Implementing Machine Learning for Revenue Forecasting and Pipeline Management

Properly implementing machine learning makes the difference between its success and failure. After all, your business does not have time to experiment with a faulty model. Your challenge is to find the right algorithm with experience in your sales and service industry. Furthermore, while cutting-edge technology is changing sales, you must find machine-learning components that fit your needs. 

You must also ensure that any machine-learning formula fits your existing CRM and sales system. Integration should be seamless and protect your team’s time, creating a data platform that operates as a single source of truth. Any machine learning should integrate with existing models, like Salesforce. Such integration ensures that your team doesn’t have to relearn the basics to operate new revenue forecasting or pipeline management technology. It also enhances your ability to interpret data and ensure that this data is the only data your team uses. 

Partner With Next Quarter

At Next Quarter, we are deeply proud of the reputation we have built for integrating machine learning and artificial intelligence with revenue forecasting, account growth, and pipeline management. With a history stretching back decades, we are trusted by dozens of companies in the Fortune 500. This reputation is well earned: The Next Quarter platform operates within Salesforce and can help your team improve forecast models by over 97%.

Ready to learn more? Contact our team today and learn how Next Quarter can help your business grow.