If you have 25 closed-won deals and try to build a scoring model, raw statistics will lie to you. One matching feature gets 100% conversion. Another gets 0%. The model looks confident and performs terribly.
Laplace smoothing adds pseudo-counts to pull extreme estimates toward the overall conversion rate. Scores become usable immediately, even with small samples. As deals accumulate, smoothing weight decreases naturally.
It is a humility technique, not a permanent crutch. But it is the difference between a scoring model reps trust on day one and one they ignore for six months.