Rules that earn their place
The test for a rule is simple. If a report, model, or automation breaks when the field is wrong, the field deserves a rule. If nothing downstream reads the field, a rule on it only adds friction.
| Rule | What it protects |
|---|---|
| Close date cannot be in the past on an open opportunity | Period assignment and slippage measurement |
| Amount required before a deal advances past qualification | Pipeline value and coverage math |
| Loss reason required on closed lost | Win-loss analysis and stage diagnostics |
| Contract dates required on closed won | Renewal pipeline and retention reporting |
| Stage cannot skip from early to closed won | Stage conversion rates and cycle length |
Where rules go wrong
Rules fail in two directions. Too loose and they let through the errors that matter. Too aggressive and reps learn to satisfy the rule instead of describing reality. A required text field with a minimum length produces "n/a n/a n/a" at scale. A hard block on quarter-end close dates produces dates two days earlier, which looks compliant and means nothing.
Fire rules at the moment of the decision rather than on every save. A rep advancing a deal into a late stage is already thinking about the deal, so asking for pricing detail costs a few seconds. Asking the same rep for the same detail while logging a call interrupts work that has nothing to do with the field.
Validation and forecasting
Bad close dates are the expensive failure. They decide which quarter a deal counts in, so an unvalidated close date moves real money between periods without anyone approving the transfer. ORM data shows that of the pipeline carrying in-quarter close dates on day one of the quarter, roughly 20% closes inside that quarter. Some of that gap is normal deal slippage. A large share of it is dates that were never grounded in anything the buyer did.
Validation cannot make a date true. It can stop the impossible versions and force the rep to record a reason when the date changes, which is what turns close date changes into a usable slippage signal instead of noise. That is the foundation forecast accuracy is built on, and no model can supply it after the fact.
Reviewing the rule set
Audit rules quarterly and count how often each one fires. A rule that never fires is either perfectly preventive or obsolete, and the error log tells you which. A rule that fires constantly is describing a process problem the rule cannot solve, so fix the process and retire the rule.
Frequently Asked Questions
What should a validation rule actually block?
Conditions that are impossible or that corrupt a downstream calculation. A close date before today, an amount of zero on a stage that requires pricing, a closed-won record with no contract length. Rules that block merely undesirable behavior, such as a short description, teach reps to enter filler text and make the data worse.
How many validation rules should a CRM have?
Few enough that a rep can predict which one will fire. Long rule sets produce workaround behavior, where reps enter whatever passes rather than what is true. Measure rule value by whether a report or model breaks without it, and retire any rule that fails that test.
Do validation rules slow down sales reps?
Poorly targeted ones do. A rule that fires on every save costs selling time and buys nothing. A rule that fires only when a rep advances a deal to a late stage costs seconds and protects the field the forecast depends on. Placement matters more than count.
What is the difference between a validation rule and a required field?
A required field checks that something was entered. A validation rule checks that what was entered makes sense. Requiring a close date stops an empty field. A validation rule stops a close date set to the last day of the quarter for a deal in first-stage discovery.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like crm validation rules into prescriptive action for your team.
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