What is the best forecasting method for sales?
The best forecasting method for sales is one that incorporates historical sales data with networked intelligence to produce actionable recommendations. But chances are that if any member of a sales team is asked about their best sales forecasting practice — their back-pocket card that really gives them an edge — there will be a range of different answers. Be it a gut feeling or countless spreadsheets of backlogged purchasing history, each sales member relies on different methodologies. That’s why effective sales teams measure their work based on results, not process.
This is why the question of which sales forecasting method is most accurate might not be the right one to ask. Collective[i] believes that AI-enabled, prescriptive forecasting is the right way forward, but even this approach relies on both quantitative and qualitative methods to produce recommendations that help sales teams meet and exceed their goals. So let’s take a closer look at all three of these methods and their approach to collecting, analyzing, and interpreting factors that could influence future sales.
Massive in scale, it becomes quickly apparent that the data available in sales forecasting is beyond any one individual’s take on what works best.
What are the types of sales forecast?
There are three basic types of sales forecast: quantitative, qualitative, and AI-enabled. Quantitative and qualitative sales forecast methods each have their pros and cons, which is why they are often combined for sales team forecasting. But even together they are no match for ai-enhanced, networked data enabled forecasting.
Quantitative forecasting examples
The first type of sales forecasting method is quantitative, dealing with hard numbers. The following quantitative tactics are all about utilizing historical data in different ways to predict what future sales will look like.
Historical forecasting: Looking at sales data over a specific period of time to predict future sales data in a similar set is the most basic kind of quantitative forecasting. While not a perfect indicator of sales trajectory, this at-a-glance information shows sales with time as a variable — which helps sales teams understand and predict the long-haul trends.
Opportunity stage forecasting: When a sales process is mapped out clearly, this type of forecasting assigns a likelihood value to different stages to know how a customer will act based on previous scenarios. For example: when a team member celebrates a call-back or a response to an RFI that moves them to another “stage” of the buying cycle and increases the likelihood of closing accordingly, that’s opportunity stage forecasting.
Lead value forecasting: While judging the value of a given lead might sound subjective, there’s actually data to support which leads are more or less likely to arrive at a sale based on previous situations. This type of forecasting assigns a value to certain leads based on criteria like their engagement in marketing emails or how similar leads have behaved in the past. The danger here is dismissing good leads that, historically speaking, don’t look as promising as they might actually be.
Test market: Learn the likelihood of success for a service or product within a certain demographic or geographically-specific area through market tests. Based on sales figures from that smaller test, forecasts can project sales in a broader rollout.
In just these methods alone, there’s an enormous amount of consumer data available, and among them, there’s no clear answer to which forecasting method is best and why. The amount of data collection and analysis needed to properly forecast requires new tools to inform your sales team, including the use of AI. With AI-enabled forecasting, manual CRM management and data entry become a thing of the past.
Qualitative forecasting examples
The methodologies we’ve looked at so far give sales teams quantitative data to make accurate predictions. And we know there’s so much data available that new tools and systems are needed to be able to harness this into tactical analysis. What about the examples of forecasting in business that are harder for a spreadsheet to measure? That’s quantitative information — the firsthand knowledge of your sales team, expert opinion, jury of executives — that are at the human heart of sales.
Expert opinions: Consensus on market predictions from experts with credibility and reputation has merit, and sales teams should take heed. That doesn’t mean their opinion is the hard truth, but it’s worthwhile to have their input.
Jury of executives: Internal leadership can also provide valuable insights into future developments and financial factors that might impact sales. The Delphi Method garners expert opinion through questionnaires and surveys to help internal stakeholders reach consensus efficiently.
Sales opinions: This boots-on-the-ground approach studies the insight and personal knowledge from sales team members who have a direct channel to consumers and customers. If your people have spent years with certain customers or have developed close B2B relationships, their input is tantamount to primary source material.
As you listen to experts and sales team members with decades of customer feedback, your pile of market data to determine sales trends amasses. How do sellers take the garnered quantitative and qualitative data, so large in scope, and apply it to current sales strategies and sales growth? By utilizing the best method: AI-enabled forecasting.
What is the best method to forecast sales?
AI-enabled, prescriptive sales forecasting is our preferred method to forecast sales because it applies machine learning to a wide set of qualitative and quantitative data to produce actionable insights. Collective[i], for instance, uses a growing neural network to synthesize historical and current data about buyer behavior across multiple sales organizations, not just one. With machine learning, it provides better sales forecasts that are more accurate, and help to keep sales teams flexible and focused on the best opportunities right now.
The best method to forecast sales is one that’s comprehensive — and Collective[i] has the tools to empower modern sales teams to forecast, and sell, better.Explore Collective[i]