Understanding SPI
Understand how Rankability calculates the 0–100 Search Performance Index across traditional, video, AI mention, AI citation, and local visibility.
SPI is Rankability's 0–100 Search Performance Index. It combines the tracked search surfaces that apply to a project so you can monitor overall visibility and then drill into the evidence behind a change.
SPI is not traffic, conversions, market share, sentiment, or a prediction of future performance. Use it to compare the same tracking scope over time.
Standard category weights
Traditional search
30%
Video
10%
AI mentions
35%
AI citations
25%
Local-query category weights
When a project is configured as a local query and includes Local Pack evidence, SPI uses a local-first weighting:
Local
60%
Traditional search
10%
Video
5%
AI mentions
14%
AI citations
11%
How the calculation works
Rankability calculates a 0–100 score for each active category.
Results inside a category are weighted by platform and, where relevant, ranking or citation position.
Category scores are combined using the standard or local weights above.
Categories with no applicable selected data are removed and the remaining category weights are normalized to total 100%.
This means a missing category is not automatically a measured zero. By contrast, a completed check where the brand is absent can contribute zero inside an active category.
Traditional and video position credit declines as the result moves down the rankings. Positions below 30 receive no position score. AI mention credit is based on whether the visible answer names the brand. AI citation credit uses citation presence and position. Local scoring uses the center result and available grid positions.
Platforms inside each category have different weights. SPI is therefore not a simple average of every selected platform.
Selected surfaces change the scale
SPI uses the surfaces that the project actually measures. Adding or removing a platform, changing locations, or changing a per-platform AI tracking mode can alter the scoring scope even when existing results do not move.
Where AI tracking-mode controls are available:
Both includes the platform in AI mentions and AI citations.
Citations only removes its mention contribution.
Mentions only removes its citation contribution and can hide the AI citations tab when no citation-tracking AI platform remains.
When comparing projects in a client-level or portfolio view, check for mixed scoring scales. Two projects with different selected surfaces or tracking modes are not perfectly equivalent even when their SPI values match.
Video and Local Pack details
YouTube Search, Google Video Pack, and TikTok Search can contribute to the video category when selected and available. A confirmed absence of a Google Video Pack removes that pack from the video calculation instead of treating the missing surface as an owned ranking failure.
Supported video mention evidence can receive partial visibility credit even when the brand does not own the ranking video. That is different from position credit for an owned result.
For local projects, the Local category uses the configured Local Pack center and grid evidence. Grid coverage and rank are therefore more important to the local SPI than the standard organic category.
Score bands
90–100
Very Strong
70–89
Strong
50–69
Moderate
30–49
Weak
15–29
Very Weak
0–14
Extremely Weak
The label summarizes the calculated visibility score; it is not a judgment about the business, campaign quality, or reputation.
Diagnose an SPI change
Confirm the project, keyword, location, and comparison dates.
Check whether the selected platforms or AI tracking modes changed.
Open the category breakdown to identify which component moved.
Open the corresponding traditional, video, AI answer, citation, or local evidence.
Distinguish a completed absence from No scan, No history, Not tracked, stale data, or a failed platform.
Compare more than one completed run before treating normal volatility as a trend.
GSC clicks, impressions, and GA4 engagement can help explain business impact, but they do not directly become SPI. Compare those connected-data metrics separately instead of assuming an SPI increase caused a traffic or conversion change.
Common interpretation mistakes
Comparing projects with different platform or location coverage.
Treating an unavailable category as zero.
Treating an AI citation as a brand mention.
Treating positive sentiment as part of SPI; sentiment is a separate evidence dimension.
Comparing a local-query SPI with a standard-query SPI without considering the different category weights.
Reporting a one-run change as a durable trend.
Next steps
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