WallRank

SoV = domain's traffic score ÷ sum of all 6 domains' traffic scores. Always adds up to 100%, so a rival gaining means someone else loses.

Δ vs 7d ago shown next to each value in the real build. Group filter applies, so SoV can be read per Gold Price / Tools / Forecast.

One row per category (dim_keyword.category: coin, metal or product type), one column per tracked domain. Each cell is that domain's share of the category's traffic score, so every row adds up to 100%. Vol = summed GSV of the category's keywords (blue bar). Each domain column has its own colour (same as the charts) and gets stronger the higher the value is within that column. Our cell is outlined when we lead the row. The Date range filter applies: SoV is computed over every day in the range (traffic scores summed first, then divided, so it is not an average of daily percentages). A short range is noisier; change it to see the values settle. Click a header to sort; the last row is the volume-weighted total over all rows. Sample shares; wallex rows use the category names and volumes of the reference chart.

Same table one level down: one row per tracked keyword. Wallgold keywords and GSV are the real wallgold-keywords-gsv.csv rows (313); the shares are sample. In the real build the Group, Cluster · Asset, Device and Domain filters apply to B9 and B10; “+0%” means above zero but under 0.5%. Row click opens “Keyword detail”.

One pie per group, latest day. Each is the SoV formula run only over that group keywords, and slices add up to 100%. A pie has no trend: use the Group filter on B2 to see one group over time.

Gain = GSV × (CTR(3) − CTR(current)). Relative score, not a traffic forecast. Top 25. Not connected to the date filter (latest day).

Candidates for a title / snippet rewrite. Flag only, not proof of a click problem. The 7-day threshold is a parameter.

Table above + bar below: how often each rival replaced us in the date range (second question, count by rival).

A big one-day spike on many keywords at once suggests a site-wide change (an update or a technical issue) rather than single-page moves. Inference, not proof.

Shows which pages carry the traffic and which pages rank for few keywords.

Click a row to open Google's own rollout log for that update (released → completed, and any later notes). The last column compares wallgold visibility 7 days before the start with 7 days after the finish. Sample numbers; in the real build it is one SQL over fact_rank_daily. A before/after gap is a flag, not proof the update caused it: other changes land in the same weeks.

Names, dates and log text on this tab are real, fetched from status.search.google.com/incidents.json on 18 Oct 2026 (Ranking product only). “Data as of” for this tab = last successful sync of that feed.

Δ7d: ▲ green = improved, ▼ red = dropped, blank = unchanged. Clicking a keyword opens “Keyword detail” with the keyword filter pre-filled. Build as one native SQL question that pivots fact_rank_daily to one column per domain.

Sorted by GSV × gap desc.

Tab is reached by click-through. Keyword filter is mandatory here.

Source: fact_serp_result, all domains, not only the 6 tracked.