Business class fare tracking: premium cabins, fare brands and sell-up gaps

Cabin requested and cabin per segment

Separates true business itineraries from mixed-cabin ones.

Fare brand and booking class

Explains price jumps within a cabin and keeps saver and flexible fares in separate series.

Base fare, taxes and surcharges

Origin-specific charges are large on premium tickets; the base fare is the pricing decision.

Total price and currency

What the shopper in that market is asked to pay.

Sell-up gap

Difference to the next cabin down on the same flights, the figure deal desks and upgrade products watch.

Aircraft and seat type where shown

A lie-flat seat and a recliner sold as business are different products at different prices.

Change and refund conditions

Premium travellers pay for flexibility; two equal prices with different rules are not the same offer.

Routing and connection time

Premium buyers weigh a nonstop heavily; store the routing that produced the price.

Point of sale and observation time

Market, edition, currency and timestamp, so the row can be compared and re-checked.

1. 01

   Pick the premium routes and cabins that matter

   Start from the long-haul and business-heavy short-haul routes your clients or readers book. For each, list the cabins actually sold: some routes have premium economy, some only business, some first.

2. 02

   Search each cabin separately, from the market you sell in

   Run one search flow per cabin with a sticky session in the selling market, then open the fare detail for the cheapest rows per brand. Match site edition, currency and language to the exit as described under airline fare scraping by point of sale.

   One sticky flow per cabin and market18 linescopy

   import secretsimport httpxBASE_USER = "USERNAME_pool-arcfast_country-de"            # pool + country from the dashboard generatorCABINS = ["premium_economy", "business", "first"]def flow_client(market: str, minutes: int = 10) -> httpx.Client:    user = BASE_USER.replace("_country-de", f"_country-{market}")    handle = secrets.token_hex(4)    proxy = f"http://{user}_session-{handle}_sesstime-{minutes}:PASSWORD@proxy.arcproxies.io:8000"    return httpx.Client(proxy=proxy, timeout=45)for cabin in CABINS:    with flow_client("gb") as client:        offers = search(client, route="LHR-JFK", date="2026-12-03", cabin=cabin)        for offer in offers:            detail = fare_detail(client, offer)          # cabin per segment, brand, taxes            store(cabin_requested=cabin, segments=detail.segments, brand=detail.brand)

3. 03

   Classify each itinerary

   Mark an itinerary 'pure' when every segment is in the requested cabin, 'mixed' otherwise. Report pure and mixed separately; most products only want pure premium fares in their headline numbers.

4. 04

   Compute sell-up gaps on matched flights

   Join cabins on the same flight numbers and date, then store the gap per brand pair. A gap that narrows suddenly is often a better deal signal than a falling business fare.

5. 05

   Store history, then sample by lead time

   Premium fares are often booked closer to departure than leisure economy, so keep dense sampling in the last weeks. Storing price time series covers an append-only schema that keeps this cheap.

Prices are list prices in EUR, excl. VAT. Volume discounts apply automatically at checkout — full ladder on the pricing page.