FA-301f · Module 1

Collection Timing Models

3 min read

Cash flow forecasting requires predicting not just how much revenue you will book, but when you will collect it. Collection timing depends on payment terms (net-30, net-60, annual prepay), customer payment behavior (on-time, habitual late payers), and billing structure (monthly, quarterly, annual). A robust collection model segments customers by their actual payment patterns, not their contracted terms — because a net-30 customer who consistently pays on day 52 is a net-52 customer in cash terms.

Collection Timing by Payment Behavior:
──────────────────────────────────────────────────────
Segment       Contracted   Actual     % of AR
──────────────────────────────────────────────────────
On-time         Net-30      Day 28      35%
Slight delay    Net-30      Day 38      30%
Habitual late   Net-30      Day 52      20%
Annual prepay   Upfront     Day 5       10%
At-risk/dispute Various     Day 75+      5%
──────────────────────────────────────────────────────

Weighted Average Collection: Day 37

Cash Forecast Adjustment:
  Revenue booked in March:     $510,000
  Expected collection schedule:
    April (35% on-time):       $178,500
    April-May (30% slight):    $153,000
    May-June (20% late):       $102,000
    March (10% prepay):         $51,000
    June+ (5% at-risk):         $25,500

Do This

Segment customers by actual payment behavior, not contracted terms
Apply collection patterns to revenue forecasts for cash timing predictions
Track DSO by segment to identify deteriorating collection trends early

Avoid This

Assume all customers pay on contracted terms — the average customer pays 7-15 days late
Use a single average collection period for all customers — the variance matters
Ignore the 5% at-risk segment — those are the dollars most likely to become bad debt