pls add toreportsV2/report_pricing_v1.php the following reports as tabs. to understand the data structure use only the f...

pls add toreportsV2/report_pricing_v1.php the following reports as tabs. to understand the data structure use only the first 2 tabs, the rest may be inacurate. You can also check the tables structure. If you are unsure on data structure pls ask. Pls act both as a senior php developer but also as a senior and very experienced business owner when deciding how the reports look like. Report 2: Market Compression Heatmap Purpose: Visually identify which future dates are filling up across the entire market — not just your property. What it shows: Calendar grid (next 90 days) Color-coded by market fill rate (% of competitor rooms sold) Overlay: your property's fill rate vs market average Key metrics: Market occupancy % by date Your occupancy % vs market delta "Compression events" flagged (dates where market is >80% full) Decision it drives: On compression dates → aggressive rate increases. On soft dates where market is also soft → targeted discounts won't help much, need demand generation. Report 3: Competitive Rate Positioning Matrix Purpose: Know exactly where you sit in the market's price ladder at all times. What it shows: Grid: Competitors (rows) × Future dates (columns) Cell values: their nightly rate Your position highlighted: cheapest / mid-tier / premium Rate rank (1st cheapest, 2nd, etc.) Key metrics: Your rate rank by date Rate gap vs cheapest competitor Rate gap vs closest quality competitor (your true comp set) Average market rate by date Decision it drives: If you're cheapest but not filling → problem isn't price, it's visibility/conversion. If you're most expensive and filling → raise more. Report 4: Search Position Performance Tracker Purpose: Understand the relationship between your Booking.com search rank, your pricing, and your bookings. What it shows: Time series: your search position (every 2h) over past 7/30 days Overlay: your rate changes Overlay: your booking events Competitor position movements Key metrics: Average daily position Position volatility (standard deviation) Position-to-booking correlation coefficient Rate change → position change lag analysis Decision it drives: Find the "sweet spot" — the price point that keeps you in top 5 positions while maximizing rate. Identify if position even matters for your conversion.

I see the query taking a long time but also incorect. checkin/checkout are irelevant in properties table should be only used from room_details

pls also create the repor below: Report 1: Daily Pickup & Net Pace Report Purpose: Understand demand momentum — are bookings accelerating or slowing? What it shows: Net bookings (new bookings minus cancellations) per arrival date, captured daily Comparison vs same day last week / last month / last year Pace variance: are we ahead or behind where we should be? Key metrics: Pickup by arrival date (next 7 / 14 / 30 / 60 / 90 days) Pace index (current bookings ÷ expected bookings at this point) Booking velocity trend (accelerating / stable / decelerating) Decision it drives: When pace is slow → lower rates or run promotion. When pace is hot → raise rates or tighten restrictions.

we need a position tracker for other competitors. it must use the filter from header. as there are a lot of queries / day and many hotels, which would be the most meaningfull way to display it? a graph, few graphs ../

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