In most businesses, the sales team will talk about a funnel of opportunities that will mature into closed deals. They may have a waterfall
spreadsheet showing the sequence of sales stages and conversion events, and they’re almost sure to calculate “pipeline coverage”: an indication of
how much they have in potential deals to cover their target number for the quarter. Despite the apparent precision of their numbers, sales forecasts
al too often don’t match reality.
What if the forecasting problem isn’t the data, but the model and assumptions they’re using? And what does this imply for measurements,
reports, and dashboards in your CRM system?
What if the Funnel is a Sieve?
The image of a funnel is nice, because it properly reflects a lot of possible deals being collected (as leads) and implies that a smaller number of
deals will actually mature. But a real funnel eventually gets all the “flow” through the thin stem, and that’s not at all reflective of reality in sales or
marketing. In many B2B situations, only 5% of the stuff at the top of the funnel will mature into deals — the other 95% either go to
competitors or, more likely in this economic environment, are lost to “no action.”
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So a sieve, or a filter, or a net might be a more accurate model to use. What’s the difference in how you measure? In a funnel, measurements
tend to focus on how long a prospect stayed in the mouth of the funnel, and how frequently you took actions to move them. In a filter,
measurements would tend to focus on the match between prospect characteristics/behaviors and your company’s ideal profile. You’d want to
measure how quickly you were able to identify, disqualify, and reject the bad matches and how well you were able to move the good matches
along. This type of measurement fits well with the Sandler sales methodology, among others.
What if the Funnel is a Refinery?
Sales will sometimes speak in terms of a pipeline rather than a funnel. This is good, but let’s take it one step further: a distillery or refinery.
The input to the refinery is low grade “ore” — leads — and the job of the refinery is to increase the commercial quality of the lead
batch to the point a small number of prospects are willing to take a meeting, engage in a serious sales cycle, and sign a contract. This model
reflects reality (particularly in terms of the amount of effort it takes to actually make deals happen), and implies that there can be byproducts that
can be re-refined (that’s your “remarketing database”).
A refinery is measured in very different ways, both on the cost and output (sales) side. If you invest too much in the “low grade ore,” there’s
waste. You want to measure marketing events that didn’t improve the win-ratio of prospects enough to justify the cost. This is done with
conversion-ratio statistics around campaigns, along with longitudinal analysis of lead/contact maturation. There’s another way you can be
overinvesting in low-grade ore: if you start the sales cycle too early, before the prospect is really ready, sales will be spending too much effort on
marginal payoff. You want to measure sales cycles that fail early, and look for ways to improve (almost always, toughen) prospect qualification
criteria. It’s also a best practice to put qualification “pop-up” screens and approval-cycle workflows in your CRM for expensive parts of the sales
cycle, such as on-site demos, loaner equipment, or proof-of-concept projects. Putting screens in the system to force justification of these resource
expenditures is a great way of getting the sales team to really think about the profitability of their selling investment.
On the output side of the refinery, you want to measure new things. A classic “invisible problem” in forecasting is deals that go backwards: the
close date gets rescheduled, the probability goes down, the sales stage recedes, or the amount of the deal decreases. Simplistic CRM reports hide
these things (“ore” that’s stuck in the refining process), and you need some analytic tools to help you spot them, such as:
- An email alert going to the sales management chain whenever a deal above, say, $50K goes backwards.
- A weekly report flagging all deals that have gone backwards.
- Detailed deal progression reports that compare deal status from pipeline reports that have been archived on a weekly basis.
Salesforce.com and some other CRM systems now have mechanisms to do point-in-time pipeline comparison without having to actually
archive spreadsheets. In any case, the analysis will probably require the use of external BI tools or purpose-built Sales reporting applications, both
of which are now available as SaaS at reasonable cost.
In addition, forecasting reports (that focus on your team’s opinion of the deal) need to be supplemented with prospect behavioral reports that
validate (or not) where the deal really is. For example, it can be a very positive sign if a prospect has downloaded your product competitive
papers, customer testimonials, or price list during the negotiation stage. The lack of any prospect website activity during the late stage of the sales
cycle is probably an indication of flagging interest and less likelihood of a deal.
Finally, get the best forecasting results by using the fanciest options to your CRM system’s forecasting module. Whether it’s advanced
forecasting, forecast accuracy reports, or sales rep scorecarding, these features increase the depth of sales collaboration. The pay off for your
company: better forecasting accuracy, and less likelihood of a miss.
David Taber is the author of the new Prentice Hall book, “Salesforce.com Secrets of Success” and is the CEO of SalesLogistix, a certified Salesforce.com 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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