Why is the AI Projection different when I change the Breakdown Field?

You may notice that all the AI Projection when you're using Forecast Category as a breakdown field does not necessarily match the AI Projection when you're using Stage as a breakdown field. This is because your typical conversion ratios for your forecast categories are different from your typical conversion ratios for your stages.

Let's take a simple example. Here is an example of conversion rates broken down by forecast category for the quarter:
 

	Q1 - STQ Pipeline	Q1 - EOQ	Q1 - Win Rate
Pipeline	$25M	$10M	40%
Best Case	$30M	$21M	70%
Commit	$45M	$35M	77%
Total/Average Conversion Rate	$100M	$66M	~62%

Here is an example of the same quarter, but with the conversion rates broken down by stage: 

 

 

As you can see in this example, even thought the dollar amounts add up, the conversion rates of the forecast category breakdown don't translate to conversion rates of the stage breakdown. The average of the conversion rates from the forecast categories from Q1 is about 62% , but the average of the conversion rates from the stages in Q1 is about 55%.

 

For each breakdown field that you use, Clari provides the projection with the historical conversion rates from that breakdown field taken into account . So, if you toggle between breakdown fields, then Clari reevaluates the projection given the historical conversion rates from the new breakdown field. This offers more accurate numbers according to whatever breakdown field you're currently using.

 

If you notice that one Breakdown field seems to produce a more accurate AI Projection, or seems to produce a more accurate AI Projection earlier in the quarter, then you may have more rigor around one of your breakdown fields than the other. For example:
 

• Some organizations find that the AI Projection seems to be more accurate when they use Forecast Category as their Breakdown Field because they have more rigor around their forecast categorization than they do around the stages in their sales cycle.
• Other organizations find that the AI Projection seems to be more accurate when they use Stage as their breakdown field because they have more granularity in their stages (i.e. 6 Stages vs. 3 Forecast Categories) and a disciplined/defined process for moving deals through the stages in their sales cycle.

 

If you notice that one breakdown field produces a more accurate AI Projection (or a more accurate AI Projection earlier in the quarter), then it's probably a good idea to enable your teams on the breakdown field that doesn't seem to be as accurate or address any paint points your team is experiencing as they try to maintain a disciplined process around that breakdown field.

Ultimately, it's helpful to compare the AI Projection when you use Forecast Category as the Breakdown Field, and the AI Projection when you use Stage as the Breakdown Field to triangulate or pressure test your forecast. So, you'll get the best AI Projection if you have a well-defined process around both fields.
 

Learn More

To learn more about how the AI Projection is calculated, check out our article on How the Clari Projection Works. Want to better understand where the projection is coming from? Read this article on How to Interpret the Clari Projection.

Finally, if you need any help enabling your teams on your terminology and processes, check out our webinar on Establishing Your Revenue Operating Cadence.