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
Section titled “Standard category weights”| Category | Weight |
|---|---|
| Traditional search | 30% |
| Video | 10% |
| AI mentions | 35% |
| AI citations | 25% |
Local-query category weights
Section titled “Local-query category weights”When a project is configured as a local query and includes Local Pack evidence, SPI uses a local-first weighting:
| Category | Weight |
|---|---|
| Local | 60% |
| Traditional search | 10% |
| Video | 5% |
| AI mentions | 14% |
| AI citations | 11% |
How the calculation works
Section titled “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
Section titled “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 report’s Citations view under AI 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
Section titled “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
Section titled “Score bands”| Score | Label |
|---|---|
| 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
Section titled “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.
An SPI score drops alert follows the same rule. Rankability compares the latest completed scan with the average of three to five comparable completed scans from the previous seven days, where comparable means the same location, brand identity, keywords, and measured platform units. The alert names how many scans it averaged and the dates they cover. When fewer than three comparable scans exist — a new project, a changed location or platform set, or runs that did not complete — no drop alert is raised, so a quiet period is not evidence that visibility held steady.
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
Section titled “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.