How Companies Plan Revenue Will Drastically Change—Here’s What Sigma Is Making Possible Today

Revenue planning and forecasting are two of the most important pieces of any revenue operations team. We’re both the business partners for go-to-market leaders, as well as the predictive element of the go-to-market function. That means we have to know what teams are performing, what teams aren’t, and exactly how to fix it. And fast. 

The Real Goal of Sales Forecasting and Revenue Planning

The basics of revenue planning are simple: Coming up with forecast methods, implementing forecast systems and processes to actually generate a forecast, and then doing the analysis to understand how accurate you actually are. You then use that information to improve your forecast methods. 

Everyone thinks their goal in RevOps is to be able to predict further into the future. That’s actually not the case at all. It’s not really a goal of ours to give a three-year-forecast vs. a one-year forecast.  My real goal would be to build a forecast model we can use every single year and make the process itself simpler over time. 

It’s way more important to look at accuracy and adaptiveness, and then turn it on a dime every six months as funding environments change. That’s real RevOps success. Our entire job is answering the question, “What would it look like if…” on every single scenario, and having a really accurate answer. 

Confidence in the Data 

Confidence in the data is critical because all decisions on go-to-market strategy are made based on that data. If you show that conversions or efficiency are lower for a certain segment, then decide to focus a ton of resources trying to improve it, (only to realize that you made that decision based on faulty data,) you just wasted time and resources. Sigma allows you to have much more confidence in the data because you can easily drill down to validate it. The data model is also just much more transparent compared to legacy tools.

The other thing that Sigma is able to back us up on in building confidence is that we can always show the data lineage. We can show all the transformations along the way, allowing you to see whether it's spaghetti or not. 

In previous roles, I (Jacob) was constantly at war with low data confidence. Models too massive for the spreadsheets they were built in, data lineages buried deep within personal desktop folders, and exported CSV’s older than the concept of RevOps were waiting for me around every corner. When the alarm rang for ad-hoc work, cadenced business reviews, or annual planning, the challenges I was solving were too frequently ones of data confidence. This stole time from my schedule and impact from our business.

An analysis and planning tool that thrived on massive scale, put clear lineage tracking in plain view, and digested data as fast as the business could generate it would have given RevOps the ability to crack more critical challenges and given my GTM business partners the ability to make pivotal decisions with confidence.

Using Sigma for RevOps Planning

A year ago we couldn’t do revenue planning in Sigma. It’s a dirty little secret, but we were doing it in spreadsheets—all the while we are building a product that’s replacing spreadsheets. 

Everything changed when we launched a product feature called input tables earlier this year. Now we are able to write to the data warehouse, build models, and plan scenarios at cloud scale with our own data and our own assumptions.  

That was game-changing for us, because revenue planning is essentially part analysis, part modeling, and part input. You have to be able to input data, and until we had input tables it wasn’t really possible—and still isn’t possible in other BI tools. Now we have the ability to do true scenario modeling and user input.

The weighted pipeline is an example of workflow we can do 100% all the way through in Sigma. We keep track of at all times where we are in relation to previous quarters, where we are in relation to our forecast, and how things have changed over time. We’re monitoring for anomalies, and we're doing predictive analysis and historical comparisons. Sigma shows us all the green lights and red alarms in real time, so we’re not caught by surprise. 

I’ve done this in Tableau before, I’ve done it in Workday Adaptive Planning before, and I’ve done financial models in Excel before. But what sets Sigma apart is that it's straight from the cloud data warehouse, it’s in my browser window, and it’s as up-to-date as I can hit the refresh button (I can even plan refresh schedules quicker than I can remember my password to log in and click).

The Future of Revenue Operations

We don’t depend on anyone else to pull and analyze the data—we do it ourselves. No data engineers required. The data team has a vital role in managing data infrastructure, creating new data sets, and enabling teams to use the product, but RevOps can be self-sufficient from warehouse table to dashboard end-product and everywhere in between. 

RevOps requires a broad skill set. Hiring searches often need to stick to candidates with experience with legacy reporting tools. But because the barrier to entry for using Sigma is so low, and it has the spreadsheet interface, I see the talent pool for RevOps really broadening in the next five years. We can give a home to people who are skilled in data engineering, or people in financial modeling, or even just sharp business or data analysts—as well as getting the people who are traditionally spreadsheet warriors to an even deeper level. With that, I'm hoping that RevOps can start hiring for a much more modern data stack in the future.