What is sales forecasting?
A sales forecast is a projection of expected sales revenue. Nearly every business — especially product-driven ones — use some sort of forecasted projection. But how does it work? Think of it like a weather forecast. It’s easy to look at forecasts for more than a few days away with a healthy skepticism. Even a 5-day forecast is only 75% reliable, but beyond that, the accuracy is 50/50 or less.
At the same time, the weather is an essential piece of news for people to plan. Will you need an umbrella? Is it time for short sleeves? Or should you cancel that idea of camping out next weekend?
For many years, sales forecasts have operated the same way as weather forecasts. Based on historical data and hopeful assumptions, sales teams share projections that are more guesswork than guarantees, but they are held accountable for making the estimate a reality. Business leaders outside sales make decisions based on the same unreliable forecasts. And the results of the misalignment can add up to the downfall of a business.
The good news about both types of forecasts is that technology is making them increasingly reliable. Ai-enabled opportunities for going beyond historic data mean sales forecasts today can be a lot less guesswork and emotion, and rely more on informed analysis and support for sales teams.
Let’s explore not just the basics of sales forecasting methods, but how the sales forecasting process is changing and improving thanks to digital transformation.
Types of sales forecasting
Some sales forecasting techniques are as old as business itself, while others make the most of the digital age. Let’s run down the list from oldest to newest:
Qualitative sales forecasting methods:
Qualitative sales forecasts are based on estimation and opinion from experienced employees, executives, and even outside experts. These sales forecasting examples sometimes even rely on wishful thinking.
Jury of executives: This is exactly what it might sound like: the executive team gathers to share knowledge of the market and look at internal data, then makes the sales forecast for the company.
Sales opinions: Sales professionals are asked about the prospects and growth in their territories or departments, and that input informs the sales forecast.
Expert opinions: An outside expert about the market reviews the company’s past performance and other data to bring outside perspective to a forecast.
Quantitative sales forecasting methods
Quantitative sales forecasting uses historical performance data, market research findings, and consumer self-reporting to generate predictions. Each method might not go far enough on its own, so many methods on this list are often combined, meaning lots of work for the leaders and staff of the sales department.
Historical forecasting: This method takes the sales data from the same month or quarter in the past and assumes the sales in the present will be equal or greater.
Test market: Based on the performance in a limited geographic test market, the company projects a forecast of sales.
Consumer self-reporting: Customers are approached and asked about their likely purchases within a time frame, which is used to inform the sales forecast.
Market analysis: Market factors that correlate with the business’ sales are analyzed and forecasts are made based on those behaviors.
Lead-driven forecasting: Each lead in the pipeline is analyzed and assigned a value based on how likely to convert they seem.
Opportunity stage forecasting: The sales team looks at where each lead is in the buying journey and calculates the likelihood the lead will convert.
AI-enabled sales forecasting methods
AI-enabled sales forecasting uses automation, machine learning algorithms, and neural networks. Some simply speed up the assembly line, while others provide sales teams with direction and support to actually achieve the forecast.
Predictive analytics: Using historical data, statistical modelling, and basic machine learning, an algorithm projects sales performance over a given timeframe. Demand forecasting: This specific type of predictive analytics pivots focus to predicting customer demand using market data and other information.
Adaptive forecasting: An adaptive sales forecast aggregates the many of these other qualitative, quantitative, and AI-enabled methods to provide unbiased daily forecasts, recommendations, and risk alerts.
Adaptive forecasting represents a simpler process and method of forecasting of sales. Here’s what that looks like in action for teams today.
Modernize the sales forecasting process with Collective[i]
Collective[i]’s adaptive forecasting tool is designed to modernize the sales forecasting process. Our Intelligent Forecast tool is the first of its kind, focused not just on helping sales teams create a more accurate forecast, but helping them return to the heart and soul of sales — engaging with other humans and closing deals. After all, no one cares if the forecast was accurate when you outperform it.
We believe sales forecasts should transform from an arbitrary target sales teams are pressured to hit into something that supports and informs them. The artificial intelligence of the Collective[i] platform elevates and optimizes every aspect of sales, starting with forecasting. Our proprietary algorithm automates data capture across every sales touchpoint, as well as analyzing competitors and real-time market factors.
We envision a world where sales teams wake up every morning to a fresh forecast, along with advice and insights about how to make it better by activating their network and making smart actions designed to improve the chance of closing deals. Machine learning has the power to help identify the individuals in and just outside a sales person’s network who can help turn a slow lead into an excited new customer. And it can all be done while protecting personal data and keeping individual privacy a priority.
Explore the Collective[i] platform today and see the future of sales in end-to-end digital transformation.Explore Collective[i]