Forecasting the dollars to be delivered by a sales pipeline, especially in technology or a business-to-business context, is an exercise fraught with uncertainty. It provides an excellent example of the interplay of business intelligence (BI) automation and business process management. It also provides a valuable example of the risks of relying on technology alone. Without a business process that supports the integrity of the prediction, no amount of sophisticated customer relationship management (CRM) software will avoid the issue of chronically inaccurate sales forecasts. Of course, without technology, the sales team will continue to thrash in a morass of what is still a labor-intensive, manual process.
The BI goal is to obtain visibility into the sales pipeline so that an estimate can be made of the likelihood of attaining the revenue goal for the quarter. In turn, this drives the need for other defined sales activities, such as the ratio of cold calling to renewals or upgrades, proposal writing and delivery, travel and meeting scheduling. For example, if the pipeline lacks prospects, then more cold calling is needed.
Suppose your CRM system has a forecasting database in it. This would include the data elements identified in Figure 1.
Figure 1: Sales Table
The key to the business process itself - the sales cycle, in this case - involves complex interpersonal dynamics. If the salesperson is able to articulate (and document) a shared vision of client pain with organizational impact that identifies an individual with the power to make the purchase and a vision of a solution that includes the vendor's product, then the seller has a good chance of closing the deal. If not, then the chance of ultimate success is low. The checklist of product (or service) features and functions, often published in a request for proposal (RFP), has generally been authored by the prospect based on the input from a vendor that has built a shared vision of a solution to the prospect's pain. Unless you are that vendor or have expectation of other benefits such as consideration for future business (or need practice writing RFP responses), you are just procurement fodder needed to satisfy rules about competitive bidding.
The point is that an accurate assessment of the status of a deal is not something the salesperson can (or even ought to) do alone (see Figure 1). In order to be a salesperson, one must be an eternal optimist. Therefore, there is a tendency to overestimate the likelihood of success. The more the sales manager pressures the salesperson to close, the more the salesperson cranks up the sunshine machine to generate a rosy prediction 30 days into the future to get the manager off his back. Hence, the BI produced by this method is often unreliable. The key to improving the quality of the BI generated from the sales forecast is to improve the accuracy of the fit (alignment) between the sales cycle and what is represented in the system. This maps directly to the sales process itself. Managerial judgment is needed here.
The little data element status is the key to a quality forecast and demands a business process with high integrity and accuracy to feed the forecast. A "P" is a prospect in the salesperson's territory who has responded to a promotion, expressed interest or is willing to learn more. A "D" is a qualified sponsor who has acknowledged pain, responded positively to a shared vision of a solution ("yes, it would help if financial reports were ready on time"), and agrees to introduce the seller to whoever has the budget (power sponsor) if the seller is able to demonstrate capabilities that solve the problem. This is generally documented in a memo to the sponsor with copy to sales management to justify entering a status of D. When the power sponsor shares the vision and agrees to an evaluation schedule, possibly including demonstration and draft proposal, then the grade advances to a "C." The status remains a C until an attempt is made to close the deal, generally at the preproposal review. If the buyer agrees to the draft proposal (says "yes"), then the status is upgraded to a pending sale, an "A." If the buyer is evasive and says, "Bring us the proposal next week and we will think about it," then the status is a "B." As a rule of thumb (which may differ in detail depending on the product or industry), D's have a 20 percent chance of closing, C's have a 40 percent chance (which improves with each milestone), A's have a 90 percent chance, and B's have a 10 percent chance. Obviously a win, "W," means the agreement is signed and a loss, "L," means the competition got the business or the proposal time limit elapsed without result.
One key to accurate BI about a sales forecast is removing the salesperson from the business of making the forecast. For many organizations, this will imply significant change to the underlying business process of selling. Based on the milestones in the underlying selling cycle, the manager assigns the status (grades) based on the documented milestones. If executive management wants a pessimistic forecast, it selects only A's and C's that are 60 percent complete, with the earliest close date of the end of the current quarter and a manager swag of 60 percent and above. An optimistic forecast would also select the B's and manager swag of 40 percent.
This is also a perfect example of right-time BI. While a forecast is needed at the end of the quarter or month, it is also needed much more frequently in order to adjust ongoing sales activities. Does the salesperson spend the day cold calling, proposal writing or scheduling an out-of-town trip to obtain face time with the prospect? Is the salesperson going to make quota by bringing in one huge deal or an assortment of smaller ones? Obviously, the risk of a surprise is much greater if all the eggs are riding in one large basket. There is no magic formula, but the quality and status of the pipeline make all the difference. Thus, the final lesson learned here is that the accuracy of the status data element (i.e., its data quality) requires a total team effort extending to the sales process and its implementation. As is so often the case, data quality depends on the quality of the underlying business process, which, in turn, is a critical path in generating accurate metrics in the forecast.
- Based on Solution Selling by Michael T. Bosworth McGraw-Hill, New York, p. 181. This text also contains particulars on the underlying business process that in detail and insight are second to none. Space limitations prevent considering all the rich implications here.
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