Inputs
Outputs
FS Probability: —
Expected CTR
— %
Baseline: —% | If Win: —%
Expected Visits
—
Baseline: — | Uplift: —
Absolute CTR uplift: — pts | Relative: —%
Results Table
| # | Keyword | Volume | Pos | Device | Curve | Intent | FS Prob % | Baseline CTR % | If Win CTR % | Expected CTR % | Visits (Base) | Visits (Exp) | Visits Uplift |
|---|
Example Data
| # | Keyword | Volume | Pos | Device | Curve | Intent | FS Prob % | Baseline CTR % | If Win CTR % | Expected CTR % | Visits (Base) | Visits (Exp) | Visits Uplift |
|---|
Formula used
This tool estimates the probability of winning a featured snippet with a logistic scoring model. Signals are normalized to 0–1, multiplied by tweakable weights, and passed through a logistic function.
1) Probability model z = b0 + Σ w_i * feature_i, p = 1 / (1 + e^(−z)) Default weights (tuned): b0 = −1.8; rank 3.0, intent 1.4, answerTop 1.0, list/table 0.7, Q&A 0.5, schema 0.4, speed 0.5, freshness 0.7, authority 0.9, title 0.5, intro length 0.5, PAA 0.4. 2) CTR impact Baseline CTR from device/curve; Expected CTR = c + p*(c_win − c); visits = volume × Expected CTR.
Calibrate with your data: export, compare to reality, iterate weights/curves.
How to use this calculator
- Select a preset scenario or fill the form manually.
- Pick device and CTR curve; adjust baseline CTR if you have GSC data.
- Toggle on‑page signals and enter performance, freshness, and authority delta.
- Optionally tweak weights and manage custom curves; export/import JSON.
- Click Calculate, then Add to Results Table with a keyword label. Export CSV/PDF.
FAQs
It is the highlighted answer box above organic results for many queries. Owning it can redirect a significant share of clicks to your page.
We apply a logistic scoring model across ranking position, intent, content structure, speed, freshness, authority, and other heuristics to approximate win-likelihood.
No. Position 1 has an advantage, but snippets can be pulled from various top results depending on formatting, relevance, and freshness.
Informational and how‑to queries allocate more clicks to the snippet than transactional terms. We vary snippet click-share by intent when estimating uplift.
Beyond strong rankings, concise answers near the top, list/table structures, topical freshness, and matching user phrasing typically drive snippet wins.
Yes. Use the Curves Manager to define per‑device custom curves, saved to your browser; also export/import as JSON.
This is a heuristic model. Behavior varies by query, device, locale, and SERP features. Validate with live tests and refine settings with your outcomes.