- Set the reporting basis. Use one consistent campaign or reporting period.
- Enter the source figures. Copy the requested counts from the same analytics view.
- Check units. Enter percentages as whole percentage values when a percent field appears.
- Review the live result. Compare the headline metric with both supporting figures.
- Reset for another scenario. Use Reset to restore the sample values.
LinkedIn Reach Calculator
Estimate unique LinkedIn reach from total impressions and average frequency. The result approximates the number of distinct people exposed and the share represented by repeat impressions.
It is useful when a report supplies impressions and frequency but omits a direct reach figure. Because frequency is an average, the output should be treated as an estimate.
LinkedIn inputs
Result
—
Estimated unique reach
Repeat impressions—
Reach / impression share—
Formula
Estimated reach = Impressions ÷ Average frequency; Repeat impressions = Impressions − Estimated reach
All counts must cover the same period. The calculator retains full precision internally and rounds only displayed results.
What the result means
Use the headline metric with the supporting figures to understand both scale and efficiency.
Results depend on the definitions, date range, and attribution settings used in the source data.
Given and calculation
With 96,000 impressions at an average frequency of 1.6, estimated reach is 96,000 ÷ 1.6 = 60,000 people. The remaining 36,000 impressions are repeat exposures.
Result
The displayed values provide a planning or reporting estimate based on the inputs supplied.
What reporting period should I use?
Any period works if every input covers exactly the same dates. Monthly periods are often convenient for trend comparisons.
Can I compare separate campaigns?
Yes. Calculate each campaign separately so different audiences, budgets, and attribution windows are not mixed.
Why can this differ from the platform dashboard?
Dashboards may apply rounding, privacy thresholds, attribution settings, or delayed processing. Match definitions and export times before investigating a gap.
How should zero values be handled?
A genuine zero can be entered for optional counts or costs. A denominator required by the formula must be above zero.
Does the result predict future performance?
Only projection-style outputs extend an observed rate, and that assumption may not persist. Treat the result as a scenario, not a guarantee.