This is the last blog in our 4-blog series on Scientific Forecasting. In the previous blogs we discussed three major topics, i.e. cultural hurdles to forecasting, in-quarter corrective actions, and forecast types by forecasting time horizons. Once mastered, addressing these three areas will result in higher forecasting predictability. What looks easy, is however not so easy to implement in practice. So, in this last blog we mention some highlights on what it takes to roll out Scientific Forecasting into the organization.

For one, a likely addition to the team needs to be business or revenue operations staffed with one or two, analytically minded folks that understand both, modern marketing techniques as well as sales psychology. They can build all the various models and / or implement some of the described monitoring tools.

Second, if the needed cultural transition to transparency in reporting, commitment to analysis of facts and data no matter what the conclusions, and an emphasis on accurate sales and marketing data entry is not supported or understood by upper management, then don’t even try. Ultimately the risk is they will find it an annoying slowdown at best or a threat to their authority, at worst. The crucially important point to make here is that there is cultural acceptance of scientific forecasting – as a complement to “gut feel” management (which also has its place). And that there is an environment where sales and marketing leaders are not punished for projecting pipeline or sales shortfalls. Fact-based and data driven business analysis is a company’s equivalent of a country’s free press. If they’re not allowed to flourish, find a new home if you’re one that does trust facts and data but doesn’t get listened to, or worse, is on the outside of the political currents driving the company because of it.

Another key group of stakeholders that need to be brought along typically is the board of directors. At one company, once we had built out a robust analytics and reporting functionality that was all based on real-time cloud applications like Marketo, Salesforce, InsightSquared, Salesloft, Bizible, and the like, we gave them login credentials. They loved it because if they wanted to see where the pipeline stood for the quarter, or what was coming in for next quarter at 3 am on a Sunday morning, they could see it. Real time. We had had the conversation about not then using that data to micro-manage us (we had seen it already at 2 am that Sunday morning :), that transparency did a lot to instill credibility in our forecasts.

Disagree with our theses? Please send us your comments.

In the meantime, for some further reading, here are links to some useful articles that further elaborate what investors are looking for beyond just growth but also around predictability:

• “Venture Capital Investors Like Revenue, But They Love Predictable Revenue”
• “The One Thing VCs Love to See Even More Than Growth”