What this calculator does
The average collection period (also called days sales outstanding, or DSO) measures how long, on average, a business waits to collect cash after making a sale on credit. This tool applies the standard accounting formula: it divides your average accounts receivable by net credit sales for the period, then multiplies by the number of days in that period. The result is a number of days, and lower generally means you are turning credit sales into cash faster.
The formula
The calculation is:
- Average collection period = (Average accounts receivable / Net credit sales) × Days in the period.
- Because receivables turnover = Net credit sales / Average accounts receivable, the period is equivalently Days in the period / Receivables turnover. Both give the same answer.
- Average daily credit sales = Net credit sales / Days in the period, so the period can also be read as Average accounts receivable / Average daily credit sales.
Use figures from the same window: if net credit sales cover a full year, use 365 days and the average receivables balance over that year. For a quarter use roughly 90 days, and for a month use about 30. Net credit sales should exclude cash sales, returns, and allowances, since only credit sales create receivables to collect.
A worked example
Suppose average accounts receivable is $50,000 and net credit sales for the year are $600,000. Receivables turnover is 600,000 / 50,000 = 12 times per year, so the average collection period is 365 / 12 ≈ 30.4 days. If the business sells on Net 30 terms, collecting in about 30 days means customers are paying close to schedule.
Interpreting the output
Compare the period to your credit terms. A collection period at or below your net terms suggests customers pay on time and cash flow is healthy. A period running well above your terms points to slow-paying customers, looser enforcement, or accounts that may need follow-up. A very low period is usually good, but if it reflects unusually strict terms it can also signal that tight credit policy is holding back sales. Track the trend over several periods rather than reading a single number in isolation.