Destination pricing intelligence: a visitor price index for your destination

Basket item

Hotel, rental, activity, attraction ticket, transfer or route, with its segment and your fixed ID.

Stay or visit date

The date being priced and the lead time at which it was observed.

Source market

The exit country, standing in for where the visitor books from.

Price and components

Total, taxes and mandatory fees, currency as shown, and your converted value with the rate used.

Availability

Available, sold out or not retrieved; a destination that sells out is a signal, not missing data.

Event flag

Whether the date overlaps a known event in the destination.

Competitor destination

The same basket built for the destinations you compete with, on the same dates.

1. 01

   Define the basket with stakeholders

   Agree segments and weights with the people who will use the index: perhaps a mid-scale hotel night, a short-term rental night, two paid attractions, an airport transfer and a return flight from each key market.

2. 02

   Fix panels and dates

   Choose specific properties and products per segment and keep them for the year. Price a fixed set of dates, such as a weekday and a weekend night in each of the next twelve weeks.

3. 03

   Collect per source market

   Run the same basket from each key source market and store each run as its own series.

   One basket, three source markets17 linescopy

   import secrets, requestsBASE = "USERNAME_pool-arcfast_country-de".split("_country-")[0]MARKETS = ("gb", "us", "de")def proxy(country: str) -> dict:    user = f"{BASE}_country-{country}_session-{secrets.token_hex(4)}_sesstime-10"    url = f"http://{user}:PASSWORD@proxy.arcproxies.io:8000"    return {"http": url, "https": url}def price_basket(basket: list[dict], date: str):    for market in MARKETS:        with requests.Session() as s:            s.proxies = proxy(market)       # one visitor per market and run            for item in basket:                r = s.get(item["url"], params={**item["params"], "date": date}, timeout=30)                yield {"market": market, "item": item["id"], "date": date, "status": r.status_code, "body": r.text}

4. 04

   Compute and publish

   Normalise prices, compute segment indices against a base period, then the weighted total. Publish with the method, the panel size and the source markets stated.

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