May 12, 2021

Written by

Collective[i] Team

  • Posted in
  • Sales Forecasting
  • Sales Forecasting Examples

Sales forecasting best practices

Historically, sales teams have relied on complicated forecasting tools and their own instincts to keep a pulse on their sales forecasts. This makes sense; manual forecasting can involve a ton of labor, data, and time to gather close-to-accurate estimates on what to expect. But, even with all of that hard work, making decisive maneuvers to correct and improve what comes next isn’t easy to manage manually.

Sales pros looking for a better way: We’re talking to you.

Fifteen years ago, Excel spreadsheets were the name of the game. Easily shared and edited, they enabled large teams to keep score as a whole unit. Times are different now. Let’s talk about sales forecasting best practices, and how you can change the game to improve the accuracy of your sales budget while building better processes for your entire organization.

How to improve sales forecasting accuracy

At their most basic, sales forecasts rely on historical data and sales team opinion to provide predictions of future sales. While forecasting isn’t a new measure that sales professionals need to report on, it’s a tall order to keep track of every factor that affects the accuracy of forecasts. This includes managing historical sales data, incorporating sellers’ opinions and instincts, and learning to spot changes in the market that can impact the future.

So, how do you improve your sales forecasting accuracy?

By relying on AI-empowered sales forecasting tools. Utilizing machine learning and a growing neural network of data, these modern tools can automate the collection of data and improve forecasting accuracy. Many modern sales teams rely on these tools to gather a more complete forecast that can give daily recommendations for next best steps. The result is a sales forecast that’s focused on results and offers accuracy as a byproduct of that pursuit.

Let’s explore some tried-and-true efforts that many teams take to find a more holistic forecast.

Sales forecasting best practices

1. Get better data. This may sound obvious, but many sales pros aren’t fully prepared to maintain their data, and for good reason. Manual data entry isn’t always quick or painless — shoddy tools, old processes that haven’t married to modern functionality, Excel files passed around team members. These are all dated steps in sales forecasting that no longer carry their own weight. This is where technology can help there are now AI-empowered tools that can take care of data entry and quality automatically.

2. Make sales forecast accuracy a yardstick, not a target. Sales pros are used to meeting estimated revenue goals, but those goals have historically been guesses at best — and wishful thinking at worst. Shift your organization’s thinking so that sales forecasting acts as a measuring tool rather than an arbitrary target sellers are required to hit.

3. Use the right forecasting tools. If you’ve been using the same sales forecasting approach for many years, it’s time to unlock the full potential those tools bring to the table. With AI-empowered forecasting to communicate with existing tools and CRM, it becomes easier to create daily forecasts that guide sellers’ next steps. From there, you always know where you should pivot to make the most of your accurate forecasts.

Which forecasting method is most accurate?

When it comes to the most accurate sales forecasting method, only a prescriptive, networked approach can interpret all of the necessary data to provide confident, actionable sales forecasts. Many teams use different methodologies, but the right forecasting tool can blend them all together, automating data entry, referencing historical data, and giving market-based recommendations.

What does that kind of a system look like? It looks like Collective[i].

Our prescriptive forecasting tool accomplishes each of these objectives, utilizing neural networks to move beyond questions of how to forecast sales using historical data. By automating things like CRM data management and looking at current indicators of buyer behavior, Collective[i] brings clarity to forecasting process steps, providing sales teams with daily forecasts and clear recommendations about which deals have promise and which ones are DOA. That frees sellers to punch above their weight and close bigger deals faster — increasing revenue in the process. Making even more money than you estimated? That’s what sales forecasting should help you do.

Click here to see what the future of sales looks like with Collective[i].

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