Forecasting at a board meeting is the part that everyone attends and is most interested in. The challenge is that most of the board members and deal team members leave without a level of belief in the current period or fiscal year booking and revenue forecast. What causes this level of disbelief, and what opportunities are there to get everyone feeling more confident?

Sample output of multi-method forecasting: Some say that forecasting is an art and others say it’s a science. The indecisive say that it is a combination, and many board members and PE deal teams say it’s a disaster. In some cases, portfolio company forecasts make astrology look credible. Why do we walk away so skeptical about the forecast? A lot of it probably has to do with history and conditioning. When the forecast is right, it is somewhat anti-climactic, but when it is wrong in a negative direction, the consequences are very memorable: RIFs, difficult discussions at the investment committee QBR, and in the worst case, a reset of valuation that will be exposed all the way to the LPs.

Download the Forecasting Criteria Worksheet. This worksheet takes the emotion out of forecasting, guides board, CEO, and CFO on types of questions to ask about each deal, and aligns everyone on what it really means when a deal is in a particular forecast category.

Forecasting Challenges:

1. Not enough data points
2. Lack of understanding of how forecasts were derived
3. Forecasts tied to sales process that only tell us how far through a process we are versus how likely we are to win
4. Weighted forecasts are deceiving – What’s the difference between 45% and 50%? Have you ever won 45% of deal?

As a PE firm, how do you address these challenges?

In PE we like data. The more the better. Data tells stories we can relate to and understand. Looking at different sets of data that tell us a consistent story gives us confidence. Data that that tries to tell the same story, but diverges tells us where to look to solve for inconsistencies. So, the key to forecasting is to have enough data sources and methodologies for us to draw conclusions or confidence.

What are the different methods and data we can use to get convergence?

1. Conversion rate – This is a simple percentage based on history that converts from forecast to bookings and/or revenue. In every company, there are critical weeks where this rate will change. The key is to know where that takes place. History will tell you this story.
2. Algorithmic forecast – This looks at a week by week basis and has a formula that dictates how much business from each forecast category will likely turn into bookings and revenue. It also take into account deals that go from forecast to closure in short periods of time. Additionally, it will have a consideration on “big deals”
3. D+C+P – In this case it is a “Done” + “Commit” + “Probable” calculation. Depending on your forecasting categories, it is the total of a subset of most likely forecast classifications.
4. Manager gut – This is a roll up from the sales management and finance teams of the total of the deals that they feel will close within the period.
5. Average – It is of value to look at the average of all the methodologies and see where that sits in relation to the other methodology results to emphasize big outliers

But My Companies Are Unique…

Everyone says that. Stop thinking it. Math is universal and it works as long as you lock some variables. Unless your portfolio companies operate inside a blanket of dark matter, the laws of math will work for them too.

This method of forecasting has proven effective for companies with small numbers of large deals, large numbers of small deals, long sales cycle businesses, short sales cycle business. It relies on mathematics and learns over time, so it recognizes and accounts for patterns in the business. The only dependencies that exist for this to be effective are:

• A defined set of forecast categories that a company uses consistently
• A set of objective criteria that dictates what category the deal should be in
• The entire organization (not just sales) that uses the criteria to qualify and categorize each deal
• Sales stage is separated from forecast category. The first only tells you how far through the cycle a deal is, not how likely the company is to win that deal.

All of this leads to consistency in forecast categorization. Everyone needs to forecast deals in exactly the same way for forecasting to be effective. See example of documented criteria below:

Sample output of multi-method forecasting:

Now take it one step further and imagine the opportunity if all your portfolio companies had consistent forecasting methodologies that you clearly understood and provided accuracy beyond doubt.

• Better predictability across an entire portfolio
• Shorter board meetings
• Better opportunity to plan and react early (good news or bad)
• More confidence in management teams
• Less time with multiple data requests to portcos

Take the diagnostic below and see some sample graphs of a multi-methodology forecast.

Rapid Diagnostic: Forecasting (Respond to each with Yes or No)

1. I clearly understand how each of my portfolio companies comes up with a forecast
2. Each portfolio company has a clear set of objective criteria for categorizing deals
3. Each company has several different methodologies that we can compare to come to consensus
4. Our portfolio companies almost always forecast +/- 5% at week 2 of the fiscal period
5. We have a forecasting methodology that “learns” over time

On a scale of 1-5, how important is this to you, your portfolio, and your firm?

Download the Forecasting Criteria Worksheet. This worksheet takes the emotion out of forecasting, guides board, CEO, and CFO on types of questions to ask about each deal, and aligns everyone on what it really means when a deal is in a particular forecast category.

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