Data Drives Metrics

I remember trying to forecast the old way, and it was a disaster. People would put deals on forecasts just to have something, even if there was no chance of closing them. Managers would remove the obvious clunkers and make three forecasts—the best guess, the up side, and the worst case.

Too often, the worst case was the best guess, but it almost didn't matter. That's because forecasting was not taken that seriously and because, quite often, there were enough deals in the pipeline that a manager could usually make a number even if all the reps didn't. But that was the case in a strong economy and an emerging market, when people were buying and sales reps were not doing much more than taking orders.

That experience got me interested in metrics and predictive analytics—anything to gain an edge. One of my favorite tools these days is the weighted forecast because it mirrors reality so well. Maybe you use a version of it.

Everything in the pipeline ought to be in the pipeline report with a weighting attached. If you have a four-month sales process, things that are four months out should be more suspect than something that's 30 days out. You can determine the weightings by evaluating your deals for a reasonable period of time, say, six months to a year.

You'll find that young leads have a low probability of close—in the 10 percent range. So even if you have $30 million in stage one, the weighting makes it look more like $3 million, and if you have a quarterly nut of $20 million, you know right away that you need more in the pipeline or three months from now you'll be starving. All these numbers are just put together for easy math. You should discover your own weightings to make this real.

The probability of a deal closing goes up as deals transit the sales process, even as the number of deals in the pipeline falls off until you get to something that you can be reasonably confident with to show the higher-ups. What comes from all this weighting is a clear understanding of what you need in every stage to be successful and the steady state of the pipeline you need to be managing. It can also tell you how much the sales team needs to do to keep the pipe healthy and how many marketing programs need to be run.

The same is true with marketing programs. If you have an idea of the efficiency of different types of programs—content, Webinars, trade shows, etc.—you can better understand your marketing budget requirements and the leads and revenues these programs will generate. You might even discover that leads from one kind of program might be more expensive, but they mature faster than the leads from other programs. That's a good thing to know if you're forecasting a shortfall in the sales pipeline.

What's amazing to me is that many organizations are not using these types of measures and metrics. Studies I've done, including one I shared at the CRM Evolution conference, show that while we're incorporating analytics into our sales and marketing processes, we're not using these tools to their greatest advantage. One finding that surprised me—many salespeople and marketers are not yet time-stamping events in the marketing funnel. Without time stamping we lose out on knowing as much as we could about the yield of programs and even the quality of some of marketing's qualified leads. Sales is much the same. Time stamping can tell you when a deal is drying up, just sitting there with a diminishing chance of closing.

Here's the thing, though. We can only get better at understanding the integrated marketing and sales funnel if we use analytics, but that's not enough. Analytics run on data, and if you aren't collecting enough data, such as time stamping, or if you aren't discovering the metrics of your organization, you won't improve.

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Denis Pombriant is the founder and managing principal of Beagle Research Group and The Bullpen Group. He is a widely published CRM analyst in the U.S. and Europe, and his latest research spans all areas of social CRM, cloud, and mobile computing. His latest book, The Subscription Economy, is available on Amazon.

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