get_breakdown

Consolidated breakdown tool. Pick dimension: 'channel' returns per-channel sessions/revenue/RPS plus engagement (visitors, avg dwell seconds, bounce rate) and bot_excluded_count (bot sessions removed from human metrics; a channel with sessions=0 but bot_excluded_count>0 is bot-only traffic, kept so it is not mistaken for 'no traffic') and — when ad spend is connected (Path B) — spend/ROAS/saturation; plus an 'Unattributed' row (is_unattributed=true) for purchase revenue not tied to any channel, with a revenue_breakdown summary (total_event_jpy/attributed_jpy/unattributed_jpy); pass attribution_model ('last_touch' default / 'first_touch' / 'linear' / 'time_decay') to switch how purchase revenue and orders are attributed across channels — same models as the dashboard's attribution selector; revenue_jpy/rps_jpy/orders/aov_jpy/cvr change, while sessions/engagement/bot/spend/ROAS are model-independent [ad-platform rows, is_ad_platform=true, are entirely model-independent, revenue_jpy and rps_jpy included — they are the spend-basis view, so on them revenue_jpy ÷ spend_jpy always equals roas, up to the rounding of roas to 2 decimals; under a non-default model their revenue_jpy/rps_jpy keep the database_statement_time_window boundary shown for roas rather than the reference_timestamp_window that presentation_hints lists for rows[*].revenue_jpy], so compare models to see e.g. how much an awareness channel gains under first_touch vs last_touch. each channel row also carries orders (purchase orders counted by our own tracker), aov_jpy and cvr derived from it — withheld as null below 10 orders, and always null on ad-platform / Unattributed rows; do NOT divide revenue_jpy by conversions to rebuild AOV, conversions is the ad platform's self-reported count, a different denominator. pass filter.channel to drill into that channel's campaigns (utm_campaign) with RPS/AOV/CVR — the value must be a channel name exactly as this tool returns it, case included (e.g. 'Google Ads', 'Meta', 'Criteo', 'Google検索'), since the match is exact and a lowercase slug returns nothing. 'page' returns per-page pageviews/unique visitors/avg time/bounce plus the page title (what the page IS, so a row reads as an article name rather than a slug; null for pages with no visit since 2026-08-21 — it does not backfill) ranked by pageviews (limit default 20, max 200; query strings stripped, bots excluded; each row also carries GSC Google-search impressions/clicks/ctr/avg_position merged by normalized path — null when the page has no GSC row, and a DIFFERENT denominator from pageviews, see notes). 'session_attribute' returns the device / time-of-day (4h JST) / day-of-week (ISO) / new-vs-returning (with AOV) / country (top-15 by sessions + 'Other', ISO2 code, share_pct; from first-party session geo, 'Unknown' when IP unresolved) breakdowns in one call. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). filter only applies to dimension='channel'; limit only applies to dimension='page'. Pass optional country (ISO2, e.g. 'JP') and/or device ('mobile'/'desktop'/'tablet') to scope session-derived metrics across any dimension (omit = all). For dimension='channel' you can also pass visitor_type ('new'/'returning') to split the channel table by first-time vs returning visits — the split uses the DATE-based rule (no visit by the same visitor on an earlier JST day = new), the same rule the dashboard's KPI card uses, NOT the tracker's stored is_new_visitor flag; under it engagement fields, bot counts, ad-platform aggregate rows, Unattributed, and revenue_breakdown are omitted because their sources have no visitor_type dimension. Session rows keep ad_platform mapping metadata. Combining visitor_type with filter.channel is rejected with InvalidParams because campaign drill-down has no visitor-type axis. dimension='page' rows include pageviews_change (period-over-period % vs the previous equal-length window, null = new page).