Implementing CoPilot is a straightforward process that can be accomplished in one week. Ongoing CoPilot professional services (e.g., revenue operations, sales enablement and coaching) are provided through our partner network.
We haven’t found one we can’t integrate with yet. And, we have the ability to manually update CoPilot by uploading a report that is exported from your CRM.
The algorithms we run against the different data feeds allow comparative analytics and trending performance data that CRMs don’t produce.
While it can be done in a week, it typically takes three weeks.
In the first month, Individual contributors and managers will begin noting what they are learning from seeing their personal data and working through enablement activities. Shortly thereafter, performance in that stage will begin to improve. And, lagging results (e.g., revenues) will improve based on the length of time between sales stages.
The simulated forecasts for the upcoming quarter will improve in the first nine months. The incremental improvements after that point is immaterial.
A CRM, a defined sales process, and an intent to follow your sales process.
This won’t work if reps don’t attempt to move leads into the proper stages. However, given how this frames expected commission gains to the reps, there is a good chance that it will help reps move things into the right stages.
We hear you. Tool fatigue is a real thing, often because the cost, value and user experience don’t line up. This is used as you update your forecast (weekly), work on sales enablement activities (weekly or biweekly), and review your personal data (prior to and during one-on-ones). In short, it’s not a daily thing.
Please allow us to nerd out for a moment on how we provide a risk-adjusted forecast of possible outcomes for quota, commissions, awards and promotions.
Using the rule of five (conceived by Douglas Hubbard, the author of “How to Measure Anything”, an established expert in risk management), we achieve an acceptable level of accuracy to understand an individual’s ability to accurately forecast (e.g., “sandbagger” or “overly-optimistic”).
This is then combined with a Monte Carlo simulation that performs a risk analysis by modeling possible results by substituting a range of values--a probability distribution--for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions. Voila--a calibrated forecast that doesn’t require behavior change.
We are inundated with tools to help us - as managers and individual contributors - to be successful. Some actually help -- and this is one of those. I’ve stopped relying on my gut (or my rep’s gut) to determine what we will work on. Now, it’s clear. Today, we are more closely aligned and working together.