How Does AI improve Sales Forecasting Accuracy + 11 Benefits
We know that sales forecasting is an important part of running a business, so how does AI improve sales forecasting accuracy and what’s wrong with the traditional methods?
Well, a recent research report by InsightSquared of nearly 400 B2B enterprise organizations shows that 68% of companies miss their forecast by more than 10%.
Creating an accurate sales forecast is no easy feat and traditional forecasting methods can be time-consuming, prone to errors, and often miss important trends and patterns.
Artificial intelligence (AI) has become one of the fastest-growing technologies today and it is revolutionizing the way companies approach and use next-gen sales forecasting tools.
In this article, we’ll take a closer look at why sales forecasts are usually inaccurate and why that matters, how AI-Powered sales forecasting works and the benefits of it vs traditional methods, and what that will mean for your business in the long run.
OK, let’s get to it.
Why are sales forecasts often inaccurate?
We know that current sales forecasting methods are not always reliable, leading to inaccurate sales forecasts that can impact business decisions and sales performance. But to understand the power of artificial intelligence in sales forecasting, it is vital to understand why current methods are often inaccurate.
Here are 6 real and possibly all too familiar reasons why traditional sales forecasts are often inaccurate:
- Reliance on Historical Data: Forecasting methods that solely rely on historical data may not accurately reflect changes in the market or consumer behavior.
- Ignoring External Factors: External factors such as the economy, competition, and government regulations can have a significant impact on sales and are often overlooked in traditional sales forecasting.
- Sales reps ‘optimism’: All too often sales reps don’t qualify properly and over-inflate when they think deals will land. This can greatly impact the accuracy and consistency of individual forecasts which will have a massive impact on the final forecast number.
- Limited Visibility into the Sales Pipeline: Traditional sales forecasting methods may not have a comprehensive view of the entire sales pipeline, leading to inaccurate projections.
- Inconsistent Data Quality: The accuracy of sales forecasts can be impacted by inconsistencies in the data, such as missing or inaccurate information.
- Inadequate Modeling Techniques: Traditional sales forecasting methods may use outdated or inadequate modeling techniques, leading to incorrect predictions.
Now we know why sales forecasts are often inaccurate, let’s take a look at why that really matters.
Why do more accurate sales forecasts matter?
Accurate sales forecasting matters for many reasons. With better forecasts come more accurate budgeting decisions which can lead to increased savings over time. Increased accuracy also allows companies to pinpoint areas where they may have competitive advantages or gaps in their product line allowing them to adjust accordingly if needed – leading to improved customer service levels.
The ability to predict events further out in advance enables organizations to better plan their strategies which reduces resource waste due to last-minute adjustments or changes throughout the year – ultimately saving money in the long term as well as avoiding potential problems down the line due to prior poor planning decisions.
How does AI improve sales forecasting accuracy?
AI-powered tools enable sales teams to quickly identify patterns in customer behavior and market trends in order to gain greater insight into potential opportunities or risks ahead. Machine learning algorithms are used to analyze large amounts of data points collected from a variety of sources such as, opportunity data, internal databases, CRMs, historic deal data, customer surveys, web analytics tools, social media platforms and many more that may be available.
Advanced predictive analytics models can then be constructed from these data sets which allow users to forecast with greater accuracy than before. AI can also be used for automated tracking of key performance indicators (KPIs) such as conversion rates or average order value over time – giving sales managers real-time visibility into their current performance compared to previous trends or goals.
What are Predictive Analytics Models?
You may be saying this all sounds great, but what exactly are predictive analytics models? I will try to cover it without going into too much techno-babble!
Predictive analytics uses advanced data analysis techniques and machine learning models to anticipate future events and trends. So in the context of sales forecasting, it is the practice of estimating future sales performance by analyzing historical customer behavior, market trends, and other factors.
The predictive analytics aspects are what you see as the person using the AI-driven content. So it’s the graphs and charts, tables of data for human consumption, AI-driven alerts and warnings that you might see on a dashboard of your sales forecasting software.
It is essentially the end piece of the puzzle that will ultimately help businesses identify potential opportunities or risks before they arise, allowing for more informed decision-making and improved sales forecasting accuracy.
You will hear many AI sales forecasting software vendors talk about their predictive analytics. This is essentially how they arrive at their analysis and how it’s presented to you.
How does AI-Powered sales forecasting work?
AI-powered sales forecasting uses machine learning algorithms to analyze large amounts of sales data and make predictions about future performance. This can be done within an organization if the skillset is available but typically software packages will be deployed that will do the heavy lifting and automate much of the activities.
The process typically involves three steps:
- Data Collection
The first step in AI sales forecasting is to collect data from a variety of sources, including your sales database, customer relationship management (CRM) system, and market research reports.
- Data Preparation
Once the data is collected, it needs to be prepared for analysis. This typically involves cleaning the data, removing any outliers, and transforming the data into a format that can be used by machine learning algorithms.
- Model Development
Finally, the machine learning algorithms are trained on the prepared data. This involves selecting the most appropriate algorithm for the data and adjusting its parameters to optimize the accuracy of the forecast.
This is the key part where involvement from the sales leaders, managers, and reps is critical to success. Many sales forecasting platforms will have differing forecast methods available and metric tracking capabilities so guidance is required to ensure forecasting is aligned with the required business outcomes.
11 benefits of AI-Powered sales forecasting?
Now that we have looked at what AI-Powered sales forecasting is and how it works under the hood, let’s look at some of the benefits that this new way of forecasting can offer.
Here are 11 unique benefits to using AI in your sales forecasting process.
1. Better data quality
AI-powered sales forecasting can help to clean and standardize data, removing inconsistencies and errors. This ensures that forecasts are based on accurate, high-quality data, leading to more reliable and trustworthy results.
2. Improved accuracy
AI algorithms can analyze vast amounts of data and identify patterns that humans may miss, leading to more accurate sales forecasting.
3. Faster processing
AI can process large amounts of data in real-time, reducing the time required for forecasting and allowing for more frequent updates that sales reps can use to better manage their opportunities to close.
4. Enhanced data analysis
AI can analyze data from a variety of sources, including unstructured data, to provide a comprehensive view of sales trends that would simply be impossible for the average human!
5. Personalization
AI can create personalized sales forecasting models for individual customers or segments, taking into account their specific behavior and preferences. This is vital for large organizations that deliver solutions across industries.
6. Reduced manual effort
AI can automate routine and time-consuming tasks, freeing up sales professionals to focus on more strategic activities.
7. Lead scoring
AI-powered sales forecasting can help to prioritize and rank leads based on their likelihood to convert into sales. This allows sales teams to focus their efforts on the most promising opportunities and increase their chances of closing deals and not wasting their time on tyre kickers.
8. Predictive analytics
As mentioned earlier, predictive analytics is the backbone of AI-powered sales forecasting. It provides valuable insights into future sales trends and helps sales teams make data-driven decisions to increase their chances of success. You can even plug AI sales note takers in to get from client calls to forecast, instantly!
9. Higher client retention rates and overall customer service
By using AI-powered sales forecasting to understand customer behavior, preferences, and upsell opportunities, companies can improve their ability to retain existing customers, deliver optimum service and reduce churn.
10. Improved business planning
AI sales forecasting provides a comprehensive view of sales trends, enabling companies to make more informed decisions about their sales and marketing strategies. This can help businesses plan their resources and activities more effectively, leading to improved results.
11. Faster decision-making
By automating the sales forecasting process and providing real-time insights, AI-powered sales forecasting enables companies to make decisions faster and respond to changing market conditions more quickly which may give you a competitive edge.
By providing valuable insights into sales trends and enabling companies to make data-driven decisions, AI-powered sales forecasting can help organizations to improve their sales results and achieve their business goals.
By reducing manual effort, increasing efficiency, and providing accurate forecasts, AI can help organizations to optimize their sales processes and achieve a competitive advantage which can often be fundamental.
FAQ
How does AI help sales forecasting?
AI helps in sales forecasting by leveraging advanced algorithms and techniques, such as machine learning and data mining, to analyze vast amounts of data and identify patterns and relationships that humans may miss.
How does AI improve sales?
AI can improve sales by enabling sales teams to make data-driven decisions, automate routine tasks, and personalize their approach to customer needs.
AI can analyze large amounts of data in real time and provide insights into customer behavior and preferences, helping sales teams to prioritize leads, forecast sales trends, and target their efforts more effectively.
By reducing manual effort and increasing efficiency, AI can help sales teams to focus on higher-value activities, such as building and mapping relationships and closing deals, leading to improved sales results and a more efficient sales process.
Why does AI in retail create better demand forecasting?
AI in retail is creating better demand forecasting by leveraging advanced algorithms and techniques to analyze vast amounts of data from various sources, including sales data, customer behavior, and market trends.
AI can process this data in real time, identify patterns and relationships that would be difficult for humans to detect, and create personalized forecasts for individual products, categories, or entire retail stores.
This leads to more accurate and reliable demand forecasting, allowing retailers to make informed decisions and optimize their inventory, pricing, and promotional strategies. The result is a more efficient and effective retail operation, improved customer satisfaction, and increased revenue.
How does machine learning help forecasting?
Machine learning helps to forecast by using highly advanced algorithms and statistical models to analyze large amounts of data and identify patterns and relationships that are relevant to the problem being solved.
This enables the creation of predictive models that can estimate future values based on historical data and automatically update as new data becomes available.
Machine learning helps to improve the accuracy and efficiency of forecasting, reducing manual effort and eliminating human biases and errors, leading to better and more informed decision-making. It’s a complex area that you or I would struggle to comprehend!
Final Thoughts
AI-powered sales forecasting offers a wealth of opportunities for sales leaders and their sales teams looking to increase accuracy in their forecasts while reducing costs over time.
By leveraging high-quality data sets combined with powerful predictive analytics models – organizations around the world are realizing significant gains in productivity & profitability. This is largely due to advancements made possible through artificial intelligence technology.
You have probably heard of ChatGPT by now, the newest AI kid on the block. Take a look at my article on how ChatGPT can be used within the sales process and become the newest member of your Go To Market team!
