The Power of AI in Sales Forecasting - Driving Growth and Revenue

Do your sales predictions feel more like gut feelings than data-driven choices? Traditional sales forecasting methods can make it feel like something is missing from the equation. Even though you adjust to shifting market trends, buyer engagement is plummeting. 

Machine learning and sales forecasting AI combine traditional statistical models and algorithms to yield a more accurate, consistent picture of buyer engagement. Better customer data and a clearer picture of buyer intent lead to better decision-making for sales teams and the sales forecasting process. 

How Machine Learning and AI Boost Sales Forecasting

Machine learning sales forecasting models are not a one-size-fits-all scenario, allowing plenty of customisation. You have many options depending on your team’s needs and goals.  The most common approaches for B2B sales include:

• Time-series analysis relies on sequenced data points to provide information about progress and values over a period of time.
• Regression analysis allows teams to forecast relationships between a single dependent variable and some independent variables.
• Decision trees represent a human-supervised approach that relies on categorisation to arrive at a decision. It is the closest approach to mimicking human thought processes.
• Artificial Neural Networks (ANN) attempt to imitate neural networks much like the human brain. To enhance analytics, they involve layers, including input, hidden values, and output.

Successful teams use several machine learning methods to provide actionable insights and develop a solid sales strategy. Approaching analytics from multiple perspectives creates the most comprehensive overview for accurate sales forecasts.

Benefits of Machine Learning in Sales Forecasting

Despite the controversy surrounding machine learning and AI for some industries, they are widely accepted for predicting future sales. The technology gives sales teams a competitive advantage by improving accuracy and productivity.

Improve Accuracy 

Using machine learning for accurate sales forecasting means adapting algorithms that can analyse more data than a human ever could. 

Did you know? Over 70% of professionals feel AI can identify sales trends in data that they wouldn’t otherwise notice.

Consider the immense volume of longitudinal data on your product lines. How long would your team go through it to create sales forecasting accuracy? Now, imagine factoring in real-time buyer engagement sales data. Not only would it feel like an impossible task, but it’s unlikely that your team would be successful.  

Increase Efficiency 

Leveraging machine learning and AI allows for the automation of mundane, cumbersome tasks. Teams can devote more time to doing complex tasks and interacting with buyers.

Predictive Analytics for Better Decision-Making  

Identify patterns humans might miss because machine learning and AI can collect, organise, and analyse much more information. Your team can review the results based on complex algorithms, making them more flexible and accurate decision-makers to keep up with evolving market trends.

Adapt to Trends and Changes in Buyer Behaviour 

Enhancing sales forecasting with AI means accessing and comparing historical and real-time data to empower sales professionals. Your team will be better prepared to adapt to market trends and address interested buyers more efficiently.

Best Practices for Boosting Sales Forecasting with AI

Integrating machine learning and AI into your sales forecasting model requires more than the right software. 

Check Your Data

Incomplete or corrupted data can derail the entire process. You need complete and clean data to build successful models. Thankfully, AI software allows for automated analytics to avoid missteps.

Forward offers comprehensive analytics, including longitudinal and real-time metrics, to provide your team with what they need. Take peace of mind knowing that your forecasting model will have the clear, correct data required for machine learning sales forecasting models.

Select the Right Model

You know there are different ways to generate accurate sales forecasts, but they might not all suit your business. It’s best to start with one model based on your available data, the degree of accuracy you require, and your company budget. 

For example, if you are a startup, you probably don’t have significant historical data. Try starting with pipeline analytics until you have more historical information available.

Account for Internal and External Conditions

Like any system, you will encounter factors that can impact and influence future sales. These unexpected factors can cause inaccuracies in the data and alter your forecasting. Carefully consider some of the common culprits, including economic changes, seasons, shifts in the industry, and even new legislation that impacts sales.

Monitor and Refine Regularly

Between unexpected conditions and shifts in customer behavior, you need to maintain flexibility with your forecasting. Routinely check your model and make adjustments as needed to ensure accuracy. 

Forward makes your team more flexible with automated reports allowing you to quickly adapt to changes.

Supercharge Your Sales Forecasting With Forward

Using AI and machine learning to generate an accurate sales forecast is the only way to remain competitive. It’s the go-to option for accurate, efficient forecasting and keeping a pulse on buyer intent.

Forward supports teams with accurate forecasting by offering real-time data on buyer engagement. Gain true insight into buyer interactions so you can make better decisions about which prospects are most likely to convert. Real-time buyer insights allow you to adapt faster and adjust your sales tactics to close deals faster. 

Discover how Forward can guide better decision making to help you boost buyer engagement. Try Forward for free today.