For many B2B technology sales leaders, it’s “nail biting” time. Many of us have an April 30th quarter end and we’re all trying to close as much as we can to help make our forecasts and quotas.
Much of the outcome of the next few days will be achieved the old-fashioned way, with hard work, a lot of elbow grease and more than just a little too much “flying by the seat of our pants.” While that has served us well in the past we should, in this era of big data and AI, be applying more math to this traditional gut-wrenching process.
For quite a while now I’ve been working hard to incorporate more analytics into my traditional quarter-end processes. During my time at Google, as we scaled the Google Cloud business, we saw the need to apply data science to our process. Aviso is similar in that we’re dedicated to bringing data science to sales. So, from that perspective, let me offer some data driven insights that are broadly true in the B2B tech industry.
First, the bad news.
You will close fewer deals in your commit and best-case pipeline than you think. We have scrubbed these pipelines, inspected the deals at a granular level and had sales reps and managers swear on a stack of bibles. But, the facts are pretty consistent, we will close fewer deals in the top forecast categories than we project we do. I’ve spoken with many an enterprise sales leader who swears that they will close 80-90% of the commit deals and 50% of the upside deals. My experience, and Aviso data, unfortunately do not bear those projections out, even with 1 week left to go in the quarter. We close fewer deals that are in commit and best-case than we predict.
Now, some good news.
You will get some business from deals that are not in this quarter’s CRM. Many people will disagree here, but the data does not lie. First, deals will be pulled from future quarters into this quarter and secondly some deals will appear and close quickly. The quantity of the good news will depend on your mix of sales models and targets markets, but the facts once again do not lie, we tend to under predict in this area.
Deal size is the wild card!
Your forecast and attainment will also depend heavily on not only which deals close, but the size of those deals that close. It’s not an easy job to predict whether the prospect is buying the large, medium or small deal and we also have to predict the price and discount level to get this right. Even with the implementation of advanced CPQ systems, our discipline around projected deal size is not good.
It’s not easy!
Closing a quarter and accurately forecasting are not easy. You have to predict a lot of things like: 1) whether the customer will buy anything, 2) whether the customer will buy from you, 3) how much will the customer buy, 4) what price will the customer buy at and 5) when exactly will they buy. We also have to do this across a large portfolio of deals in various products, geographies and business segments. We, in sales, can do this the old-fashioned way with gut feel and a little bravado or we can leverage big data and AI to add to our intuition and experience to make the right calls.
My best wishes for a great quarter! If you’re interested in taking a more data-driven approach next quarter, drop me a line at firstname.lastname@example.org. We’d be happy to analyze your data and let you know your historical close rates by forecast category and by week. This simple bit of insight could help make next quarter a little less nerve wracking than this one.