In a GTM organization, the concept of a ramp period — the time it takes for a new sales rep to fully onboard — is crucial for financial planning and capacity modeling. Accurate ramp models lead to accurate quota deployment assumptions, which in turn leads to greater revenue predictability. Inaccurate ramp models on the other hand run the risk of leaving a team short on quota coverage or driving up costs through higher than expected commission payouts.  

My fellow Revcaster and RevOps extraordinaire, Jeff Serlin, shared this great piece about how to set the right ramp periods for your team. Jeff provides it as a practical guide - more science than art - on how to arrive at the right ramp model. 

But he also points out that like any assumption you make in planning, ramps are something that you need to not only model, but also measure, analyze, and optimize. It turns out that for many organizations, this is easier said than done. Data silos and bandwidth constraints often result in a lack of real-time visibility into ramp performance vs. assumptions. Without this type of ongoing visibility, organizations are left to carry over (potentially faulty) ramp assumptions from quarter-to-quarter and year-to-year.

At Revcast we want to change the way revenue organizations work when it comes to ramp models. Put simply, we want to simplify the way ramps are modeled, analyzed, and optimized so that organizations can master this fundamental building block.

It all starts with the ability to capture ramp models directly in Revcast as shown below:

As you can see, ramps are easy to define in Revcast, and they’re also easy to assign to teams. You can create as many different Ramp Models as you need, and once defined, Revcast uses them to calculate quota on the street in sales plans created on the platform. (We recently announced new default sales ramp models that provide for a rapid start.)

Of course, revenue leaders need to understand more than just the assumed quota deployment that results from Ramp Models. They need to understand, and continuously monitor, actual ramping performance throughout the year.

As shown below, Revcast provides a real-time view into actual ramp performance across the entire GTM organization: 

The chart above tells a revenue leader a few interesting things that may require action on their part:

• The Enterprise team is overachieving for multiple months during the ramp process. Maybe ramps should be adjusted so as not to consistently overpay reps during the ramp period.
• There’s something going on with the Com West team - ramp performance is bad and bottoms out in month 3 before finally coming back in month 5.
• The Com East team is remarkably consistent. Which begs the question, what’s the difference between Com East and Com West when it comes to ramping?

These questions drive action – action that’s only possible if leaders have real-time insight into performance vs. assumptions.

Finally, leaders need a real-time view into the current ramp exposure across their GTM organization. A higher-than-expected number of “ramping” reps to “fully ramped” reps is often a risk signal, and at the very least should factor into the formulation of any sales forecast. Revcast provides real-time insights that put these insights at the fingertips of revenue leaders.

Insights from Revcast are summaries of risks and opportunities that exist at a given point in time across a revenue team. From the insights shown above, we can quickly surface information that's critical to know for identifying early potential risks to revenue achievement:

• Attrition sits higher than expected (4 attritions against a plan of 1). More attrition means more backfills.

• The exposure shows in the fact that 4 reps are ramping against a plan of having 0 ramping reps at this point in the year.

• The net result is that there is only 69% of deployed quota from fully ramped reps vs. the plan for this point in time.

There’s much more to cover with ramps and Revcast, but hopefully the above sheds light on how the platform simplifies the ability to model, measure, analyze, and optimize this crucial GTM building block. If you’re interested in learning more, schedule a demo today.