How it is computed
For each source, count the leads in the period and the deals that came from them, and divide. The sources are the ones the CRM records; the discipline is logging every opportunity to its real source, because a walk-in logged as a web lead moves both numbers in the wrong direction.
Why it matters
It is how lead spend gets judged. A paid source that closes one lead in twenty may still be the best money the store spends if its deals carry gross; a referral source that closes one in three costs nothing and deserves to be fed.
It is how the floor gets coached fairly. A salesperson working the web queue and a salesperson working the showroom door are not in the same game, and judging them on one close rate punishes whoever drew the harder source.
How operators read it
- Read the spread between sources before the average. The average is an accident of mix; the spread is the store's real shape.
- Read each source against its own history. A web close rate that fell by a third when the store changed lead providers is a provider conversation, not a floor conversation.
- Read the lead counts. A source with twelve leads will swing wildly month to month, and the swing is noise, not news.
Questions people ask
Why do showroom close rates run higher than internet close rates?
Because a walk-in has already chosen to spend an afternoon at the store, and an internet form may have gone to six stores at once. Different intent, different odds. That is why the two should never be averaged.
What counts as a lead for a walk-in?
Whatever the store decides and then logs every time. Stores that log only the customers who sat down will post a flattering showroom close rate; stores that log every door swing will post an honest one. Pick the honest one and keep it.
How A.D.A.M. reads it
A.D.A.M. computes the lead funnel and close rate by source for the store and for each salesperson, from the sources the CRM records, and never serves a blended close rate without the sources behind it. He reads it beside the numbers it trades against, names what the gap is worth, and leaves the decision where it belongs. What an AI advisor should do, and refuse to do.