5 Forecasting Metrics That Every Retail Sales Team Needs to Track

In sales forecasting, mistakes are to be expected. However, with each mistake that you make, you should learn something, in order to refine your forecasting methods, and improve the accuracy of your forecasts. As a rule of thumb, the more metrics you track and include in your forecast, the more accurate the forecast will be. In the following post, we will discuss some key metrics that all businesses should track.

1 ACCURACY

Accuracy is not only a characteristic of your forecast but an actual metric that you need to keep track of. There are several ways in which you can measure the accuracy of a forecast.

By the measure of error

Each sales team should measure the error of the forecast which is calculated by taking the absolute value of the difference between your forecast and your actual sales, and divide it by the divisor (the largest value between the forecast and the actual numbers)

Measure of error = (F-A)/F

F – forecast

A – actual sales

By product type

Accuracy can also be calculated by product, a strategy which can be a lot easier for your sales team to implement. This strategy is best suited for off-the-shelf products that don’t have customization options that can complicate the sales cycle. When it comes to complex products, you can simplify the process, by breaking the prediction down into several parts.

By size of the deal

For this strategy, you will start with a less accurate forecast in the early stages of a deal, which you will refine along the way. In the later stages of the sales cycle, you will have a better understanding of the needs of the buyers, which will help you refine your numbers.

By time period

There are two critical stages where you can measure accurately. The first stage is 90 days out, a stage at which your accuracy should be quite low, as the deal is a long way from closing. Nonetheless, there are a lot of early signs that can help your sales reps predict the chances of closing a deal. The second stage is 30 days out. At this point, the sales cycle should be at the final stages, and your accuracy should be above 90% at this point.  

2 PUSH RATES

When it comes to complex transactions, it is not uncommon for planned close dates to be pushed back. It is important to track these types of sales and discover indicators that show a deal’s chances of getting postponed. A pushed deal is not a lost deal. In most cases, it is just a deal that is postponed into another quarter, but this will heavily impact your predictions.  Moreover, pushed rates can also indicate a problem with certain sales reps.

3 VARIANCE

The variance is calculated as the distance between the commit and the pipeline upside. This indicator will help you understand how an initial forecast can change from the beginning to the end of a quarter. To improve the accuracy of your variance, you need to be very involved in the deal activity of your reps, but you must also consider their gut predictions. In the long term, tracking the variance will not only help you understand the changes in your forecast, but you will also be able to narrow down the factors that influence these changes.

4 LINEARITY

Depending on your business, and the complexity of your sales cycle, a sales forecast can be balanced throughout a quarter, or it can weight more heavily towards the end of the quarter. To measure the linearity of your forecast, you will have to track the stages of a sales cycle and the close dates. The point of tracking this metric is to adjust your sales strategy so that in the long run you can rely on an accurate and linear forecast.

5 DATA COMPLIANCE

As most of your forecasting strategies rely on historical data, it is important to keep an eye on your team’s commitment to recording all valuable data. The compliance numbers will show you if all the members of your team have submitted accurate forecasts for the relevant opportunities. Obviously, the sales rep forecasts are refined by a team leader, but unless all the members of your team are committed to tracking all relevant metrics, you won’t be able to improve the accuracy of your forecast

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