February 25, 2020

Revenue Operations

Part 1: How Atlassian, McAfee and Databricks Changed Their Sales Forecasting Game

Molly St. Louis

Molly St. Louis

Molly St. Louis
Molly St. Louis

Senior Producer

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Accurate sales forecasting isn’t just about keeping your board members happy, it can transform your entire organization into a culture of efficiency and predictable growth. Just ask Masters of Revenue, Chris De Vylder of Atlassian, Virgilio Vargas of McAfee, and Stephan Blendstrup of Databricks.

These three remarkable revenue executives recently shared their insights about coupling Clari’s predictable forecasting alongside best practices for nurturing a unified sales team, and as Vargas so eloquently put it, “It’s not just about using the technology, but changing the process, as well.”

Here are some of the highlights of our conversation:

Predicting Targets

For Atlassian, predicting revenue is not only a top priority, but the very fabric of the revenue organization.

“We see forecasts as a commitment to the business,” says De Vylder. “So, we triangulate with finance at the beginning of the year (and) we do a bottoms-up plan. Then every quarter, we triangulate again with finance to make sure we stay on that plan.”

There is a very consistent forecasting cadence at Atlassian, according to De Vyler, who says that visibility into their continuous pipeline and sales activity intelligence is where Clari is needed the most.

Engaging Quota-Carrying Individuals

“Excel Hell” is the phrase that revenue experts have coined to describe the long, monotonous, and frustrating task of gathering spreadsheets to make a simple sales forecast and it’s painfully accurate. “We were managing all the forecasts in Excel spreadsheets and it probably took us about three or four weeks to prepare for a forecast call,” says Vargas.

For a company as large as McAfee, this meant managing the spreadsheets of 750 sales people in territories all over the world. Naturally, the data in these spreadsheets changed very quickly, so maintaining accuracy was a huge challenge.

When Vargas and his team adopted Clari, there was a “come to Jesus moment.” Not only did they need to adopt a new technology to make their forecasts more accurate and time-efficient, but also transform the way their organization operated. Vargas was able to rally the team behind the technology very quickly and it paid off in valuable time.

“(Now) when we jump into a forecast call, it takes us five minutes to prepare at the most,” he says.

Forecasting Growth and Investing In It

Blendrup says that forecasting for the current quarter isn’t his only objective in using Clari’s platform – it’s also about looking into future quarters and determining whether or not the company will hit its targets. By having this kind of visibility and understanding, his team at Databricks can confidently make decisions for the future.

“For us, we were lucky enough to kind of beat our target this year and know about it in early July, so we were actually able to forward invest about $30 million to hire more AEs for ’20 and ’21 to continue the growth.”

De Vylder, Vargas, and Blendstrup shared many more inspiring insights about building and scaling massive RevOps engines. Check out the full interview below!

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