Why revenue forecasting should go beyond your CRM

Dan Morgese:
Approximately 15% of the opportunities we analyzed in our research had a red flag indicating a deal might not be progressing as well as expected, hidden in email exchanges. It’s easy for sales leaders and reps to miss these red flags, meaning the core problem remains unaddressed. This is where AI comes in. With the right interaction data, like what Gong captures in emails and calls, you can get an accurate sense of which deals are likely to close and spot emerging risks before they become pipeline issues.

This is Reveal: The Revenue Intelligence Podcast, here to help go-to-market leaders do one thing: stop guessing. If you’re ready to unlock reality and reach your full potential, then this show is for you. I’m Danny Wasserman, coming to you from the Gong Studios. Welcome back to Reveal, the podcast that helps revenue leaders reach their full potential. I’m Dan Morgese, Director of Content Strategy and Research here at Gong, and we have another fantastic episode for you today featuring Udi Ledergore, our Chief Evangelist at Gong and a five-time marketing leader at B2B startups.

Dan Morgese:
Revenue forecasting has become increasingly difficult for both public and private companies. Many leaders lack the reliable and complete data they need to responsibly and predictably grow their revenue. One major reason is that many organizations need to leverage more than just their CRM data to forecast accurately. CRM data, which is manually entered by sellers, often provides an incomplete, biased, and inaccurate account of what’s truly happening throughout your deals.

Dan Morgese:
Enter revenue intelligence. Revenue intelligence finds buying signals in emails, sales calls, and other interactions that CRM data often misses. Based on an analysis of over one million sales emails and 30,000 sales calls, Udi will help us uncover the contextual insights that most revenue leaders today lack. This can help surface red flags and buying signals to better inform forecasts and go-to-market strategies at large.

Dan Morgese:
For sellers and sales managers, understanding and responding to buying-related red flags and signals throughout your deals—like mentions of competition, legal issues, future steps, or scheduling—can actually increase win rates and improve deal outcomes. All in all, these signals can help revenue prediction models be 20% more accurate than CRM data alone and 22% more accurate than forecasts reported solely by sales reps.

Udi Ledergore:
We did it! Don’t you just hate it when you do everything right—digest all the data available—and still have an outcome that falls short of your expectations? I know I do. I read the recipe, bought the ingredients, set aside the time, and this happened. True story.

Sure, when it comes to baking, I can chalk up my Instagram versus reality fails to being an amateur baker. But there’s so much more that explains why the recipe I followed didn’t work out.

Maybe the recipe doesn’t account for the uneven heat distribution in my oven, or the fact that I was using year-old flour, or even the hardness of my water. All these factors impact how the croissants turn out, but it’s hard to say exactly how, so I end up with a less-than-perfect result.

When it comes to my morning pastries, that’s one thing. But when we’re talking about quarterly revenue falling short of expectations, that’s no laughing matter. I’m Udi Ledergore, Chief Evangelist at Gong, and I’m going to share with you what you’ve been missing for accurately forecasting your revenue and how to close that gap.

Udi Ledergore:
Some of you are leaders at public companies, so you know that missing revenue can affect stock price and erode investor trust. Others in private companies know that forecasts are key to controlling burn rate, hiring, and keeping investors happy. But forecasting is not easy. Last year, Gartner found that 67% of sales and operations leaders agreed that projecting sales accurately is harder than it was three years prior.

To achieve accurate forecasting, leaders need accurate and complete data. Unfortunately, 78% of revenue operations leaders say they lack this data. Everyone wants to be confident in their forecast, but the problem is that despite your efforts, you may be missing the full picture.

Depending on who you ask in your organization, you’ll get a different set of data points, projections, and opinions. It’s like the parable of the blind men and the elephant—you never get a complete picture. Now’s the time to get a more accurate sense of what goes into your forecast to keep up with market expectations.

Udi Ledergore:
At Gong, we’re in the business of using data to not just show the gaps in your predictions and reality but also help you close those gaps. We analyzed over a million emails and nearly 30,000 sales calls to help you understand the red flags lurking in your pipeline and the positive signals that indicate whether a deal is likely to close or not.

Our research confirmed that relying solely on CRM data isn’t enough. CRM data relies on reps’ manual entries, which are often incomplete and subjective. In fact, Gartner says that only 1% of your data makes it into your CRM. That’s how you lose 99% of information before it even makes it to your CRM.

We found that when accounting for the hundreds of deal signals found within customer interactions, forecasts were 20% more accurate than those using CRM data alone and 22% more accurate than those relying on sales reps’ understandings of their pipeline.

Udi Ledergore:
Thanks so much for listening to this episode of Reveal. If you want more resources on how revenue intelligence can help you create high-performing sales teams, head over to Gong.io. And if you liked what you heard, please give us a five-star review on Apple Podcasts, Spotify, or wherever you listen.