Open opportunities belong in a sales-cycle analysis

If finance estimates the sales cycle from opportunities that have already closed, it can miss the longest-running opportunities because they are still open. The completed-deal average describes that selected set. It does not automatically describe how long the entire entering cohort will take to reach a decision.

Keep open opportunities in the duration dataset with their age at the reporting cutoff. Their final duration is unknown, but the fact that they have remained open for that long is information. Treating them as completed at the cutoff is also wrong; it replaces an unknown later date with a date chosen by the analyst.

Statistics calls this right censoring: observation stops before the event has occurred. NIST explains methods such as Kaplan-Meier estimation that retain censored observations without assuming a particular lifetime distribution. Applying those methods to sales requires a clear event definition and careful treatment of why observation ended.

Suppose ten opportunities enter a defined qualified stage on the same day. Four reach a recorded decision after 20, 30, 40 and 50 days. Six remain open at a day-60 reporting cutoff. For this illustration, a decision can be either a win or a loss; the timing question is when the opportunity leaves the open state.

The four completed durations average 35 days. But the other six have each already waited at least 60 days. The average of the observed time before decision or cutoff is 50 days: the four completed durations sum to 140, and six times 60 adds 360, giving 500 across ten opportunities.

That 50-day figure is still not the eventual mean duration. The open cases will add further time before a decision. The cohort has also not reached its median decision time by day 60, because only four of the ten decisions have occurred. Reporting 35 days as the cohort's typical completed cycle would conceal that evidence.

A cycle measured from first inquiry differs from one measured from qualification, proposal or procurement approval. Choose the stage relevant to the financial forecast and preserve its actual entry timestamp. Changing the starting definition between cohorts can create an apparent improvement without any faster customer decision.

Decide whether the event is any final decision, a signed contract, a first delivery or collected cash. Those are different milestones. A won opportunity may still require implementation and payment, so its sales-stage duration should not be loaded directly into a cash forecast without the remaining steps.

Record genuine reopening and stage reversals. A deal moved to lost and reopened the next day should not silently become two independent opportunities. Keep the commercial identifier and a policy for analyzing recurring proposals so the duration population matches the underlying customer process.

A Kaplan-Meier-style analysis tracks how the observed population remains without the defined event as time passes. An open opportunity contributes information through its observed age. It then leaves the observed risk set at the cutoff without being counted as a completed decision.

The method needs an appropriate censoring assumption. An administrative reporting cutoff is different from losing track of difficult opportunities because staff stopped updating them. If missing follow-up is related to the likelihood or speed of a decision, the remaining observed cases may not represent those that disappeared.

Show the number of opportunities still under observation at later durations. A curve based on only a few long-running cases can look precise despite limited evidence. Avoid extending an estimated average far beyond the available follow-up without an explicit additional model and a reason to trust its tail.

The time until any decision does not establish the probability of winning. A cohort can decide quickly because customers reject proposals quickly. Keep win and loss outcomes alongside the duration view, with contract value and stage definitions where they affect the forecast.

For time-to-win analysis, a recorded loss is a competing commercial outcome, not simply an opportunity still waiting to win. Treating every lost deal as ordinary independent censoring can produce a misleading interpretation of eventual conversion. Use an analysis that represents the outcome structure, or present separate transparent cohort counts and timing distributions.

Do not multiply an unconditional average cycle by an unrelated win rate and assume the result describes receipt timing. The probability of winning, time to decision and time from contract to cash can depend on deal size, segment and stage. Model the relevant connections explicitly where they materially affect the forecast.

A recent cohort has had less time to resolve than an older one. Compare its observed status at the same elapsed age, or use an appropriate censored-duration analysis. Declaring improvement because the newest completed deals were fast selects exactly the deals able to finish early.

Keep commercial changes visible: new qualification criteria, customer segments, pricing, procurement requirements or sales capacity. A faster cycle after excluding complex deals may reflect a changed population rather than a more efficient process. Both can matter, but they lead to different operating conclusions.

Use the information in a staged forecast. For an existing open opportunity, its current age and stage may be more relevant than the average duration of a brand-new opportunity. Preserve the evidence for that conditional estimate and avoid assuming every old deal will close next month merely because it is overdue against an average.

Save the opportunity extract, stage-entry definition, event dates, reporting cutoff and missing-data treatment. Reconcile counts of open, won, lost and excluded records to the commercial system. An attractive duration curve cannot compensate for duplicated opportunities or retrospectively edited close dates.

The useful conclusion is often specific: completed cases averaged a certain duration, several opportunities remain unresolved, and the current evidence supports only part of the future timing distribution. That gives finance a stronger basis for a cash range than a single average that silently drops the deals still taking time.