The 7 Best Sales Forecasting Methods for Realistic Predictions

Posted by:
the Attention team
on
November 21, 2021

The ability to perform accurate sales forecasting is an essential ingredient of your company’s success. Forecasting refers to the ability to make data-based predictions about your sales performance. Basically, sales forecasting focuses on the amount of revenue your company will bring in and the timing of that revenue. Making realistic and accurate predictions has a tremendous impact on the quality of your decisions and factors like time management. Forecasting can help you determine what leads to devote the most time to, and what activities to prioritize. Many companies either don’t devote many resources to forecasting or make frequently inaccurate predictions. Following the tips below can help you make accurate forecasts.

If you want to take your company to the next level in today’s competitive sales landscape, you can’t afford to waste huge aunts of time on ineffective training strategies. Attention uses the latest cutting-edge artificial intelligence technology to train your junior reps and provide them with real-time feedback, saving considerable time for managers and sales reps alike. 

1. Great Data

Your ability to forecast is only as good as the data it is based on. Most sales companies today have an abundance of accurate data at their disposal, and their main problem is knowing what to do with it. A great forecasting process involves tracking a wide variety of analytics such as growth, pipeline stage, and length of sale cycle to determine which leads and processes are most promising. As we will discuss, there is room for speculative or intuitive forecasting in sales, but this needs to be complemented by predictions based on hard data. Without analytics, your forecasting will mostly be confined to guesswork.

2. Sales Cycle Length

One highly reliable way to predict the success of a sale is by looking at the length of the sales cycle. If the cycle is too short or goes beyond a certain length the odds of a sale plummet. Using past data, you can objectively measure the probability of a sale based on the sales cycle length. This can also help your team members to reach the sweet spot in terms of sales cycle length, instead of trying to rush it to make sure they meet their quotas. 

3. Pipeline Stage

This method uses the stage that leads are currently on in the pipeline to determine their chances of buying. An advantage of this technique is that it can be very specific and precise. Each pipeline level has a specific probability of resulting in a sale that can be determined through historical data. Sales leaders simply have to multiply the amount of the prospective sale by the odds based on the pipeline stage the prospect is currently on in order to get a forecast.

4. Testing

A great way to make data-based predictions is to market-test your product. This involves making a small rollout for a select test group, usually spread over a few locations that are representative of your target market. You can then extrapolate your predicted revenues from the percentages of the test market who make a purchase. Test marketing is a reliable and commonly used form of sales forecasting. However, it has its drawbacks: it can be time-consuming and costly, and it can also be difficult to find a representative test market.

5. Polling

This method is a simplified, more convenient version of test marketing. It entails finding a group of people similar to your target market and inquiring about their interest in the product. This is less costly and time-consuming than full-fledged test marketing, but also less accurate and reliable.

6. Expert Opinion

This method is pretty straightforward. The opinion of a sales expert can be very helpful and informative for making predictions. Consulting a jury of experts can be even more beneficial. However, this method has its drawbacks, as experts can be wildly off base, so you want to balance it with more data-based techniques. 

7. Intuition

It may sound unscientific and vague, but your salespeople’s hunches and gut feelings should not be ignored during the forecasting process! One of the key traits of a great salesperson is a well-developed intuition and ability to read their prospects’ intentions, so hopefully, your salespeople should all have these qualities! If they don’t, take a look at this article on how to identify star sales reps. Your reps should be able to tell, especially at later stages in the pipeline, how likely their prospects are to become customers. If a trusted sales rep tells you they feel sure their prospect will end up buying, or, on the flip side, they have a hunch that they will back out, you should take this into account! Of course, it can be hard to quantify this sort of insight, and you should also have an alternate forecast that registers the worst-case scenario, or what will happen if your rep’s predictions are off base.

8. Multivariable Analysis

Multivariable analysis is a complex and sophisticated forecasting system that takes into account most of the other techniques on this list. It usually entails a mix of sales cycle length, pipeline stage, historical forecasting, and other methods. It requires accurate and abundant data, and the math involved can be pretty complicated. A good CRM can greatly simplify this process, but you need to make sure your data collection is on point. However, if you want a comprehensive and thorough method for making predictions, it is hard to beat multivariate analysis. 

To be a great sales leader, you need to know how to forecast and plan for your future. Many of the best forecasting methods require data from past events or expert predictions. However, some experts believe that intuition is one of the most important forecasting principles in order to get an accurate prediction about what will happen next. Which method do you think has been most helpful for your company's success?

If you need help training and ramping up new hires, consider trying Attention to save time and get your junior reps to senior level in a matter of weeks.

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