A creator roster can contain several perfectly reasonable engagement rates that should not be averaged together. One may use followers as its denominator, another may use views, and a third may cover a completely different reporting period.
Shane’s Influencer Analytics lets you organize those figures, compare creator records and keep source notes. Before using the Average engagement card in a report, check what each percentage means and which records the card includes. A short review can prevent a tidy-looking number from telling the wrong story.
Define the percentage before entering it
The creator form accepts an Engagement rate (%) value. The tool does not calculate that creator-level percentage from followers or average views. It stores the rate you supply.
Enter a percentage as a percentage: use 4.2 for 4.2%, rather than 0.042. If your source uses a decimal fraction, convert it before entering or importing it. For CSV imports, keep the engagement cell numeric, without a percent sign. The current importer can turn text such as “4.2%” into zero instead of rejecting it.
Record how the source defines engagement. Does it include likes and comments, or also saves and shares? Does it divide by followers, impressions, reach or views? Does it report a ratio across a period or an average of individual post rates? Identical labels can hide different calculations.
Use the Notes / source field for a compact reference. The entry form limits it to 240 characters, so keep a fuller methodology and supporting report in your project files. For example: “Source: creator report, checked 2026-11-04. Last 10 organic posts. Likes + comments divided by followers. Evidence: campaign folder, report A.”
That is a fictional note format, not a claim about any real creator. Use only information you are authorized to keep.
Understand the card’s simple average
The current Average engagement card takes a simple arithmetic mean of the stored rates it converts to finite numbers. Each included creator record receives the same weight. A creator with 1,000 followers counts just as much as one with 100,000.
With two supplied rates of 6% and 2%, the card shows 4.00%. That describes the mean of those two rates. It is not automatically the engagement rate of their combined audiences or campaigns.
Consider a deliberately simplified example with matching definitions and reporting periods:
- Creator A has 60 engagements and a denominator of 1,000, giving 6%.
- Creator B has 180 engagements and a denominator of 9,000, giving 2%.
- The simple mean is 4%. The combined ratio is 240 divided by 10,000, or 2.4%.
The two calculations answer different questions. The combined ratio is meaningful only when the underlying counts are compatible. It still does not establish unique audience reach, because audiences may overlap. The dashboard does not perform this weighted calculation for you.
Decide which question your report needs to answer, then label the statistic clearly. “Mean of two recorded creator rates” is more precise than “campaign engagement.”
Keep missing values separate from measured zeros
There is an important current behavior to allow for: a blank engagement field appears as a dash in the roster, but is converted to zero when the summary average is calculated.
If your workspace contains rates of 6%, 2% and one blank, the card shows 2.67%, rather than the 4.00% mean of the two known rates. The missing observation lowers the displayed average even though no zero rate was measured.
Keep an unknown value blank and record why it is unknown. Do not invent a rate to make the card look right. For reporting, calculate a reviewed statistic from the known, comparable observations in a separate analysis file, and state how many records lacked a rate.
CSV imports need the same care. A blank cell under an existing engagement column remains blank in the roster, but still contributes zero to the summary. If the column is absent altogether, the current importer stores a numeric zero. Keep the column in your import and check the resulting rows before relying on the summary.
These notes describe the current implementation checked on November 4, 2026. Recheck the behavior if the tool changes.
Do not mistake roster filters for a new reporting group
Search, platform and contact-status filters narrow the visible creator table. The Average engagement card continues to use the complete stored roster. Sorting the table does not change its calculation either.
Suppose you filter to YouTube and see one creator with a 6% rate. If an Instagram creator with 2% remains elsewhere in the workspace, the card still shows 4.00%. A screenshot of the filtered table and summary can therefore suggest a platform-specific average that was never calculated.
Use the “Showing … of … creator records” note to keep the visible group in context. If you need a platform, campaign or reporting-period comparison, select that group explicitly in your analysis file. Do not remove valid workspace records just to force the summary card to match a shortlist.
Check the rows before handing off the result
Export data downloads a JSON file containing both creators and campaigns, including the stored engagement values and creator notes. It exports the whole workspace, not just the filtered roster. Review its scope before sharing it, especially if notes contain information unrelated to the recipient’s project.
The current import control accepts creator CSV files; it is not a JSON restore control. Keep the JSON export as a useful snapshot, but do not assume that uploading it will restore the workspace.
Repeated CSV imports append records rather than updating existing creators. Duplicate rows can change the average, particularly when some creators are repeated more often than others. Review names, handles and platforms before importing a file again, and preserve an export before cleaning up records.
For a concise reporting handoff, include:
- The creators or records included, and the rule used to select them.
- The engagement definition and observation period.
- The number of known rates, missing rates and duplicate records removed from the analysis.
- Whether the result is a simple mean or a ratio calculated from underlying counts.
- The source and review date needed to check the figures again.
Shane’s Influencer Analytics helps organize the evidence you bring. It does not fetch live metrics, verify creator profiles or predict results. A well-labelled calculation and a clear evidence trail make that workspace more useful than an average presented without its denominator.