Take Control of Your Forecast
Being data-driven is no longer an option, it is a necessity. Understanding every variable that impacts your revenue teams success is critical. Canopy automates this analysis giving you the answers to confidently drive your business forward.
One Platform. Every Answer.
Real-time awareness through active data monitoring, notifications, and custom goal tracking.
Streamline your forecasting process while monitoring and measuring variance to drive constant improvement.
Drive productivity and results through individual assessments, improvement tracking, and notes.
Answer key questions around trends, risks, and past performance through snapshot reporting for any period and any subset of data.
How can Canopy help you?
What are we going to bring in next period?
Whether you are on a monthly or quarterly cadence this question always seems to be the hardest to answer. The reason being that most sales teams base their cadence off of their sales cycle. If you have a sales cycle <20 days you probably look at things on a monthly basis if you have a sales cycle <60 days you probably look at things on a quarterly basis. We do this because there is a large amount of revenue that the team will create and close in the given sales period. Making the question of “what will you close next period” even harder to answer.
When we look at forecasting the next period we have to account for three major buckets:
- Existing Pipeline: Deals set to close next period plus slipped deals from the current period.
- Intra Period Revenue: New Pipeline that will be created and close in the same period
- Pulled In Revenue: The pipeline that will be pulled forward into this period but is currently slated to close 2+ periods from now.
Pipeline that Exists
The Problem with Traditional Weighted Pipelines:
There are a number of factors that come into play when looking at existing pipeline. The simplest and most traditional way to look at this pipeline is with a basic weighted model. Most CRM’s come with this capability. In the most basic terms, you assign a random % to each stage of your sales process. Salesforce for instance comes with these amounts filled in out of the box. The biggest issue here is that these arbitrary weightings do not reflect reality in any way. This normally looks something like this:
Improving Your Weighted Pipeline:
The easiest way to take the next step to improve weighted pipeline accuracy is to calculate actual win rates from each stage. By taking all of your historical data you can calculate the win rates and conversion rates and apply them to each stage. This can be a tedious process but will give you a slightly more accurate picture of your existing pipeline. Resulting in more accurate weightings:
What we are still missing from this model is the true “in period” win rates. An aggregate win rate tells you the likelihood that a deal will close at some point, not the likelihood of a deal closing in the period it is currently set to close. The primary driver of inaccurate forecasts is slippage. Taking slippage into account is a much more difficult calculation. This requires point-in-time win rate calculations. Additionally, win rates fluctuate constantly. For example, your team may be getting better at converting from a specific stage, or economic factors may have slowed down your business and your deals are converting at a much lower rate. Keeping these up to date is crucial to achieving an accurate weighted pipeline. Finally, you must take into account roll forward pipeline, or deals that will likely slip from the period you are in. Calculating the slippage rate for deals and determining the likelihood of slipped deals to still close is a very challenging metric to calculate. Data hygiene plays a major factor in this as well. We recommend taking a central tendency or geometric mean when looking at this type of value. This will eliminate outliers and drive better results.
Intra Period Revenue:
A major challenge for leaders is predicting how much pipeline will be generated and closed in the same period. The primary factors that drive this number are going to be pipeline velocity (sales cycle), deal size (acv), growth rates, and seasonality. By taking each of these historical variables, and time boxing them into the relevant historical period, you can begin to model out the potential value entering into the next period.
Pulled In Revenue:
One of the most common ways sellers try to cover themselves from slipped deals, and missed forecasts, is by pulling deals set to close in future periods forward into the current period. This has major impacts downstream but can also cause major fluctuations in your forecast. Identifying the average value that gets pulled into different periods will allow you to account for this shift and plan for coverage issues two to three periods out. Seasonality can be a significant factor in pulled-in revenue. End of Quarter and End of Year deals are incredibly common. Incentives for sellers to hit their targets force major discounts and drops in value to existing pipeline. Below is a wholistic view of an accurate top-down model.
What Else Are We Missing?
At the end of the day, this is sales! Humans are involved in this process therefore uncertainty and variance play key roles in the accuracy of any forecast. Each of these inputs alone can help drive improvement to your top-down forecast but does not account for randomness.
Team Size & Behavior:
Calculating all of the metrics above is a massive time-consuming process if done manually. Breaking each of these inputs down to the team and individual seller level makes this nearly impossible to do without a tool in place. Let’s take the most common example of an organization with multiple sales managers. One manager may be focused on Small to Medium Businesses, the other on Commercial or Mid-Market, and finally Enterprise. Each of these teams will have different deal sizes, sales cycles, and accuracy. The way each manager leads their team will impact win rates and conversion rates.
It is due to all of this complexity that the traditional top-down model is broken and outdated. This is why we focused our time on building Canopy’s Scenario Planner.
This tool allows us to leverage Machine Learning and Artificial Intelligence to run simulations and scenarios to account for every variable in real-time. Giving you the ability to answer the question of “Where will we land” with confidence. We even took it one step further and gave you the ability to apply your own logic and inputs marrying your experience and knowledge with data-backed insights. Giving you full control over your forecast and the ability to identify how to drive your team forward.