by David Taber

Accurate Sales Forecasts and Other CRM Fantasies

Feb 16, 20126 mins
Business IntelligenceCRM SystemsEnterprise Applications

Every company needs a sales forecast, but generating sound growth projections is devilishly hard, and accuracy depends on rethinking old forecasting processes.n

Some companies are in the enviable position of having a sales backlog, and in many parts of the economy demand is starting to heat up. But prosperity has its own problems, including inventory shortages, guessing wrong on what the hot items will be, and excess work in progress that makes Wall Street cranky.

If forecasting itself were easy, then economists would all be rich and weathermen sages. In other words, it ain’t. One root cause: Since careers and stock valuations depend on forecasting and revenue management, the process can be highly political. There’s a lot of emotionally charged data. As sales execs can be quite adept at, ahem, managing the flow of information, nobody — not even the CEO—knows the whole story of the pipeline. This can be dangerous. (And good luck with Sarbanes-Oxley compliance!)

At their best, SFA/CRM systems give a comprehensive view of the pipeline, as well as detailed drill-downs on the state of play for any specific deal. Unfortunately, few CRM customers can really depend on (or even use) the forecast that the system produces. Most of the time, executives must second-guess the CRM data, making judgment calls that may not be consistent week to week and are rarely recorded anywhere. Worse, everyone’s first reflex is to call the rep if they need to find out what’s really going on with any account. As a result, the CRM data is seldom authoritative.

What Are the Steps to Better Revenue Visibility?

Nobody will put the time into good SFA/CRM data unless there’s a personal payoff for them. So you need to make the system a key tool that’s integral to how the sales team actually operates. Artificial incentives won’t help here—you need to put the CRM in the critical path for sales cycles. Identify resources that reps need (travel approval, loaner machines, quotes, available-to-promise dates, etc.) and make them available only via requests through the CRM.

Commissions drive behavior. Require that all deals be in the CRM system for at least a third of the length of the sales cycle before they become commissionable. Deals that just pop in three days before the close date may be welcome news, but they’re bad for the organization. Make them bad for the reps as well.

Any sales methodology—Miller-Heiman, Sandler, SPIN, MEDDIC; name your favorite—will help forecasting accuracy. But don’t get hung up on dogma or process. The key issue is having unambiguous definitions in your sales cycle, and building discipline and consistent behavior across your sales team. That’s what improves the “signal to noise ratio” in forecast data.

Characterize the sales cycle as a series of three to seven stages (or status changes), and make sure that they realistically reflect what actually goes on in your deals. Then create small penalties for deals that skip stages or have other surprises.

Make deal stages such that they can’t be selected or changed directly by the reps. Instead, have the deal stage set as a result of answering simple questions about the criteria for each stage. For example, have the “technical win” stage set only when the rep has checked the boxes for “product demo,” “answer tech objections” and “received customer evaluation form.”

Most sales organizations have a formula for pipeline coverage that runs along the lines of, “If the pipeline is three times my quota at the start of the quarter, I’ll probably make my number.” These are good for rules of thumb, but it’s important that they do not become wildly inflated (five times quota!), and are strictly enforced at an individual level. In either case, the sales rep is being given an engraved invitation to dream up bogus pipeline. Fictitious pipelines can only hurt forecast accuracy.

Your CRM system will probably require you to assign probabilities to each opportunity stage. Unfortunately, it is often impossible to hide or rename this probability field. And it’s a misleading name. Really, that percentage reflects “percentage of our sales cycle we’ve completed”—an indication of effort or milestones. But it does not reflect where the prospect is in the process, or the likelihood of that prospect choosing you. The two keys here are to ignore the standard probability number, and not to use the “expected revenue” calculations that your CRM may provide.

You should add your own probability field—manually set and entitled something like “rep’s bet”—that is meant to indicate the realistic likelihood of a win. Build reports around this, and start to identify which reps are able to correctly assess the deals, when reps’ judgment can be trusted, and what bias the inaccurate reps have. The idea is to develop a model of win/loss forecasting accuracy. This process can be incentivized with small rewards (e.g., free dinner) for the best forecast accuracy of the month. But do not punish inaccurate forecasting.

After you’ve done all these, you need to go beyond your narrow internal view. Find a way to involve the customer in your forecast cycle, perhaps by letting them tell you where they are in their purchase decision making, and the likelihood of their choosing you. Your reps can only guess at these things, and they are inherently biased. The best way to do this is either with a portal or a survey tool that allows the prospect to provide input in an unbiased, unpressured way.

When it comes to channel forecasts, you don’t have the kind of control you do with your own troops. Often, you won’t know what processes and infrastructure the partner uses to prepare their forecasts. All you can do is provide incentives for their forecast accuracy, such as preferential treatment when it comes to inventory allocation or delivery schedules, “points” in your loyalty system, or credit towards quota or commission accelerators.

Accurate forecasting systems have a lot of prerequisites, but once in place they shouldn’t have a lot of moving parts. Try to keep things as simple and transparent as you can, and try to remove by design the temptation to game the system—a tendency guaranteed to hurt forecast accuracy.

David Taber is the author of the new Prentice Hall book, “ Secrets of Success” and is the CEO of SalesLogistix, a certified consultancy focused on business process improvement through use of CRM systems. SalesLogistix clients are in North America, Europe, Israel, and India, and David has over 25 years experience in high tech, including 10 years at the VP level or above.

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