How Long Deals Actually Take to Close, by Group

Ask when a deal will close and you get a date. Ask how a portfolio of deals will close and a single date per deal is the wrong instrument entirely.

At ORM each opportunity is grouped by a machine learning model, and for each group we predict a curve describing how long it will take to close.

What the curves look like

The curves run from 1 to 80 weeks. Most of the expectation happens before week 12, and very few groups carry meaningful expectation past 52 weeks.

Three things follow from that shape.

The bulk resolves fast. If most expectation lands inside twelve weeks, then a deal still open well beyond its group's curve is not simply slow, it is behaving unlike the population it was assigned to. That is a signal rather than a delay. The tail is long but thin. Curves extending to 80 weeks mean some deals genuinely take that long, so a rigid rule that closes everything past a fixed age will discard real revenue. The tail is real, it is just not where the volume is. Beyond a year is rare. Very few groups carry meaningful expectation past 52 weeks, which is the empirical justification for treating twelve months without movement as a stale threshold rather than an arbitrary one. See the 12-month opportunity aging rule.

Why grouping is the right unit

An individual opportunity does not contain enough information to estimate a distribution. It has one outcome, and it has not happened yet.

Grouping solves that. Deals that resemble each other on the dimensions that matter form a population large enough to fit a curve against, and any individual deal inherits the curve of its group. The model is doing the work of deciding which dimensions actually matter, rather than a human asserting that segment and stage are the right two.

The stage-weighting row is worth dwelling on, because it is the most common alternative. Applying one weight to every deal in a stage assumes new business, expansion and renewal behave alike, and that enterprise and commercial motions share a shape. They do not. That failure is covered in why stage-weighted forecasting misses.

What a curve changes operationally

A close date answers a question nobody should be asking, which is when exactly a given deal will close. A curve answers the question that actually drives decisions: what proportion of this pipeline is expected to close in each of the coming weeks.

That is what makes weekly forecasting possible rather than aspirational. It also means a deal can be behind without anyone having done anything wrong, and the model can say so, rather than a leader inferring it from a missed date.

Combined with the intra-quarter seasonality shape, the two produce a weekly expectation you can actually manage against. The deal groups say how quickly this pipeline resolves, and the seasonality index says how the quarter distributes those closes across thirteen weeks. See the 13-week quarter.

Building something similar

You do not need to reproduce a learned grouping to benefit from the idea. The first version is arithmetic.

1. Take three to four years of closed opportunities with their creation dates and close dates. 2. Compute weeks-to-close for each, and plot the distribution rather than the average. The average is almost always misleading here, because these distributions have long right tails. 3. Split by motion and segment and re-plot. If the shapes differ materially, and they will, you have just found the grouping dimensions that matter most in your business. 4. Use the distribution as an expectation, so a deal at week 20 in a group whose curve resolves by week 12 gets attention on evidence rather than instinct.

What you should not do is convert the distribution back into a single average cycle length and manage to that. Averaging a long-tailed distribution produces a number that describes almost none of the deals in it. For definitions see sales cycle length and close probability.

Frequently Asked Questions

How does ORM predict how long a deal will take to close?

Each opportunity is grouped by a machine learning model, and for each group a curve is predicted describing how long deals in that group take to close. The curves span 1 to 80 weeks, with most of the expectation occurring before week 12 and very few groups carrying meaningful expectation past 52 weeks.

Why group deals rather than score them individually?

Because a single deal provides too little history to estimate a distribution from. Grouping deals that behave alike produces a population large enough to fit a reliable close curve, and the individual deal inherits the curve of the group it belongs to.

What does a close curve tell you that a close date does not?

A close date is a single point and it is usually the rep's estimate. A curve is a distribution over time, which lets you express the probability that a deal closes in a given week rather than treating one date as certain.

Why not just use an average sales cycle?

Because these distributions have long right tails, so the average describes almost none of the deals in them. A curve expresses the probability of closing in a given week, which is what makes weekly forecasting possible.

What can I build without machine learning?

Take three to four years of closed opportunities, compute weeks-to-close for each, and plot the distribution rather than the average. Then split by motion and segment and re-plot. Where the shapes differ materially you have found the grouping dimensions that matter in your business.