Ready to see the future? Let's dive into the art of predicting your sales numbers with a dash of Python wizardry.
In this guide, we'll harness the power of time series forecasting to unveil the secrets of your future sales revenue.
Armed with pandas for data mastery and statsmodels for crafting our crystal ball (aka forecasting model),
we're setting you up to forecast like a pro.
I'm spending most of my time this year rebuilding our sales team and process. This includes polishing our messaging. I recently read the excellent book "New Sales. Simplified." by Mike Weinstein and in it, he offers a framework to help craft a compelling sales narrative.
At Autohost, we care about making data-driven decisions. I know many companies say this, but we mean it. We have a lot of data, and we want to use it to make better decisions. One of the issues is related to understanding the differences between signed contracts and billed invoices, and how to predict revenue. We can't tell if we are on track to hit our revenue goals, because we don't know how much revenue we will get from our existing customers as product usage changes.
Here's something I wish I knew three years ago: Sales is the most difficult function to build in your startup. I would have taken a different approach to building my sales team if I had known this. The best products don't always win; the best sales teams do. At least in the short term.
As a sales leader, I know how important it is to get contracts out to customers quickly. The faster you can get a proposal or a contract to a customer, the more likely you are to close the deal.