For a company’s executive management and all members of the sales team, a CRM system proves its value by improving the quality of the sales pipeline and the efficiency of closing those deals. Sure, a CRM’s contact and account information are valuable to management — but those pale in comparison to the value of deals that will (or won’t) make the quarter. If a CRM system helps win just one more deal every quarter, it more than pays for itself.
Unfortunately, too many sales pipelines are just baloney: wishful thinking about deals that will never close (or, worse, that don’t exist at all). There’s also the opposite problem: Big deals that may well close, but don’t show up in the system at all until they do. These “miracle” deals are never harmless surprises, and can be downright dangerous if you have a long supply chain.
The good news is that there are several metrics you can use to validate the pipeline health. But simple numbers need to be supplemented by policies, automation, and business processes that provide incentives for good behavior. Let’s look at some examples of policies that should be in place:
If a deal isn’t in the CRM, it doesn’t exist
This is really basic, but many companies still have some deals that aren’t in the CRM pipeline, and some others that are pretty badly misrepresented. To keep things consistent, it’s best to create synthetic deals that represent all the corner cases (such as “run rate” business, distributor/reseller deals, renewals, etc.). Make sure that these deals are clearly distinguished from standard ones using record types or other flags that prevent confusion in reporting and other business processes.
Incorporate automatic deal dupe detection, particularly across channels
This will require some fuzzy logic, but makes sure that you’re not double or even triple-counting deals in the pipeline.
[Related: 6 CRM predictions for 2016]
All deals have realistic close dates and amounts
Again, basic … and way too often violated. The key is the enforcement of the word “realistic,” using criteria such as the following:
- How many stages does the deal still need to go through?
- What’s the average number of days required for each stage?
- At the later stages of the sales cycle, is the amount field substantiated by line-item quotes?
- Do the quoted line items make for a coherent order, or do they appear to be random entries designed to fool the system?
- Are those quoted items available on schedule for the deal?
- How often has the deal record been updated?
- Does the latest update cause the deal to shrink in value, move out in time, or go backwards in the sales cycle?
- How many completed activities have been done for the deal, considering its stage in the sales cycle?
- How many open activities need to be completed before the deal can close?
- As discussed in this article, the criteria for the above bullets will have to vary depending on vertical industry (e.g., government vs telecomm vs financial services) and country (e.g., US vs UK vs Japan). To avoid confusing/meaningless criteria, you have to split data modalities and follow the edict of that 50’s song, “Beware of the blob.”
It’s best that these criteria be checked automatically, although that can involve some fairly complex code.
A deal’s 'likelihood of close' reflects the customer’s behavior, not ours
- This is tricky, because your internal activities and sales cycles are easily monitored but your customer’s intentions are not. In big deals, the customer has an incentive to hide information.
- Ideally, customer actions and intentions (e.g., downloading of install guides, trial/pilot usage, request for quote, asking questions in customer forums, etc.) are monitored and collected in the CRM system. It’s a best practice to have email exchanges or forms filled out by the prospect as part of your sales process, so that you have objective evidence of their state of mind.
- Obviously, these indicators are much more readily available for deals involving SaaS products and direct sales cycles. But with some creativity, almost any sales cycle can be instrumented.
Institute automatic pipeline cleaning
For example, two weeks after the end of a quarter, all deals that were supposed to have closed at the end of the quarter but haven’t been updated by the rep are automatically closed out. If you want to be ornery, make it so they can’t be re-opened. But don’t take it too far, as this kind of thing can become an engraved invitation for reps to game the system (e.g., automation that re-assigns in-progress deals to another rep if they haven’t been updated in 30 days).
Channel/distributor forecasts incorporate data from partners
- This one is really hard, unless your company has significant market power. Your channel partners have to work with a lot of vendors, and they have little time to fill out forms for you.
- Provide a portal that makes it really easy for them to get leads, download information, hear about specials, and get sales support. Make the portal a magnet, so they are in there at least once a week. Only when this is working do you start requiring them to register their deals and update the state of the business on at least a bi-weekly basis. Give them spreadsheet templates and an easy way to upload their pipeline and inventory data directly into the CRM.
- Make sure you have incentives for accurate forecasts, not big ones. Make sure they understand that discounts and allocations are contingent upon the best data, not the most optimistic.
All fields and related entries of the deal should have audit trails turned on
“Audit trails” means a date/time stamp, user ID, and before vs after values for every single change (i.e., this is not a weekly snapshot). Keep these audit trails for at least three years, just like the rest of your CRM data. This is for your own protection when the lawyers come a-callin’, so just do it.
All forecasting is based only on the data from the CRM system
Even if you are using some sort of external forecasting tool, all the data it uses should be coming from the CRM system.
- External spreadsheets may be used for reporting, but not as a data entry or modification vehicle.
- Assuming that your system has a real forecasting tool, all adjustments to the pipeline, commit, and other forecasting parameters need to be recorded in the CRM. If your system doesn’t have a forecasting tool, it is acceptable to have managers’ forecasting adjustments made in the form of synthetic deals (with either negative or positive amount values).
- Again, audit trails must be on for all forecasting entries and results, whether you are using the CRM’s forecasting system or an external tool. Definitely keep these audit trail tables around for at least three years.
Forecasts must be analyzed to outlaw bogousity
Bogousity means any of these things:
- Mandated by any level of management (e.g., “your number this week will be X”). This issue is painfully fraught with politics.
- “Gamed” entries from the worker-bees (e.g., “if I move this deal into next month and that other deal into this week, nobody will notice that I’m behind”)
- Adjustments to the forecast have been made with no comments recorded or apparent changes to the underlying pipeline.
- The forecast in aggregate involves an unusually fast or optimistic close rate.
- The forecast contains no notion of probabilities or ranges: if the forecast is “just this one number,” it’s being forced. True forecasts have a high, low, and “most likely” component.
All quotas are in the CRM system, with audit trails on
- This supports “I can see everything in one place” and eases management conversations.
- This also reinforces the distinction between “your nut” (the number you need to achieve to make on-target compensation) from “the forecast” (the realistic appraisal of the deals).
All commissions are contingent …
…upon deals satisfying the pipeline criteria of the first bullet and the “anti-gaming” criteria described throughout.
And then there’s that culture thing
Every level of management needs to explicitly reward good pipeline and forecasting behavior, while visibly highlighting sloppiness and sloth. With a sales organization of any size, the culture of accurate forecasting is a key success factor and a bad culture is a nexus of failure. Unfortunately, getting this right requires consistency, patience, and time -- things that many managements aren’t very good at.