About the Client

A growing hostel group operating properties across Spain — beach cities, Barcelona-and-Madrid urban locations, and two pilgrimage-route towns — with roughly 14 properties in scope and expansion plans behind them. Hostels live in hospitality's most price-elastic segment: their guests compare obsessively across Booking.com, Expedia, and Hostelworld, book late, and switch for a few euros. Yet the group's pricing ran the way most independent hospitality pricing still runs: a spreadsheet, a weekly meeting, and a revenue manager's instinct — against OTA competitors repricing daily.

The Client

The Challenge

Rate shopping is a like-for-like problem, not a price-grab. Each property needed ~10 tracked competitors across three OTAs — but a raw price list is useless in hospitality. A €38 dorm bed with free cancellation for one guest is a different product from a €38 private twin, non-refundable, for two. Honest comparison requires normalizing every observed rate by date, room type, cancellation policy, and occupancy — the four axes the client's brief specified, and the four axes that make hotel rate data genuinely hard.

The look-forward horizon multiplies everything. Retail monitoring watches today's price. Hotel monitoring watches a calendar: every check-in date up to 90 days out, per room type, per competitor, per OTA — turning "10 competitors" into hundreds of thousands of rate points per collection cycle across the portfolio.

Demand context lived in scattered public sources. Occupancy pace alone doesn't explain Spanish hostel demand — festivals do. Primavera Sound, Semana Santa, La Tomatina, regional trade fairs, football fixtures, and a dense calendar of national and local holidays (Spain's vary by autonomous community and even city) move demand violently. The brief's event-detection requirement was, in data terms, a multi-source public-events extraction problem with geo-matching to each property.

Recommendations had to be explainable to humans who own the decision. The client wanted AI recommendations in exactly the form their brief illustrated — "Increase Deluxe Room from €95 to €112 due to high demand and low competitor availability" — a number with a reason, feeding a revenue manager who accepts, adjusts, or rejects. Black-box price outputs were explicitly unwanted.

And the OTAs are hostile, changing surfaces. Booking.com and peers ship UI changes constantly and defend aggressively against automation — the environment where the brief's "seamless updates for site changes" requirement translates to our self-healing extraction layer as a standing capability.

The Actowiz Solution

The rate-shopping engine. Daily collection (06:00 CET, per the client's preferred schedule, with on-demand manual refresh) across Booking.com, Expedia, and Hostelworld: ~140 competitor properties, every check-in date 90 days forward, every visible room/bed type. Each observation lands as a normalized record — property, OTA, check-in date, room-type class (dorm 4/6/8-bed, private single/twin/double), cancellation class (free/partial/non-ref), occupancy basis, price, currency, and availability flag — with room-type mapping maintained per competitor so "6-Bed Mixed Dorm" and "Bed in 6-person room" compare as the same product. Historical storage from day one: the archive is the AI's textbook.
Change detection & rate-parity views. Every cycle diffs against history: competitor increases/decreases beyond thresholds, availability closures (a competitor's sold-out date is a pricing opportunity), and the client's own OTA rate-parity check across the three channels — a bonus finding of the normalization layer.
The event-detection layer. Continuous extraction from public event sources — city agendas, festival calendars, venue listings, sports fixtures, trade-fair schedules — plus the national/regional/local holiday calendar per property's location, geo-matched and scored for expected demand impact by category and scale. Events flow into the forecast and into recommendation explanations ("+ major festival weekend" appears in the reason string, not just the math).
The forecasting & recommendation engine. A 90-day demand forecast per property per room class, trained on the accumulating archive: historical occupancy and pace (from the client's booking data), competitor price and availability positions, day-of-week and seasonality curves, holidays, and detected events. On top, the pricing recommender: an ML core producing the optimal-price estimate, wrapped in an LLM explanation layer that renders each recommendation as the human-readable sentence the client asked for — with the top contributing factors listed, and guardrails (per-property floor/ceiling rules, max daily change) configured individually per property per the multi-property requirement.
Dashboard, alerts, reports. A responsive dashboard (React/Next.js over FastAPI and PostgreSQL — the client's preferred stack) with the KPI layer (occupancy, ADR, RevPAR, competitor averages, revenue trends), per-property competitor analysis views, and interactive historical charts. Alerts via email and WhatsApp on competitor price moves, demand shifts, and below/above-market positions; automated daily reports (market overview, recommended actions, competitor changes) and weekly rollups (occupancy, revenue, best performers, pricing opportunities). Architecture left deliberately integration-ready for the client's roadmap: PMS, channel manager, and booking-API connectors as future modules.
Compliance by construction. Public OTA rate and availability data only — no guest data, no personal data anywhere in the collection layer; GDPR posture documented (this is Spain), respectful request pacing per our ethical-load standards, and per-record lineage throughout.

Sample Structures (Illustrative)

Recommendation record (as delivered to the dashboard):

{
  "property": "hostel_bcn_01",
  "date": "2026-09-18",
  "room_class": "private_double",
  "current_price_eur": 95,
  "recommended_price_eur": 112,
  "confidence": 0.84,
  "reasons": ["forecast occupancy 92% (+11 vs typical)",
              "3 of 9 competitors sold out this date",
              "competitor median EUR 108 (+6% WoW)",
              "event: major concert weekend (geo-matched)"],
  "guardrails": "within +20% daily cap"
}

Daily market snapshot excerpt (sample, one property):

Date	Own Price*	Comp. Median*	Comp. Sold-Out*	Forecast Occ.*	Recommendation*
Fri +14d	€89	€104	2/9	88%	▲ €102
Sat +15d	€95	€112	4/9	93%	▲ €114
Tue +18d	€72	€66	0/9	54%	▼ €64

Sample data — illustrative of deliverable format. Actual feeds are per room class, per date, 90 days forward, daily.

Engagement Metrics (Representative)

Metric	Value*
Properties supported	14 (individual competitors, rules, dashboards)
Competitor properties tracked	~140 across 3 OTAs
Rate points collected daily	~350,000
Look-forward horizon	90 days
Room-type mapping accuracy (audited)	97%+
OTA layout changes absorbed, first quarter	11 (10 auto-repaired)
Recommendation acceptance rate by revenue team	~70% accepted or lightly adjusted
Time to first property live	4 weeks; full portfolio 9 weeks

Representative engagement figures — illustrative of project structure.

The Outcome

The group's pricing rhythm changed from a weekly meeting to a daily five-minute review: recommendations with reasons, accepted or adjusted per property before breakfast. In the client's own seasonal review, three effects stood out. Weekend and event-window pricing stopped leaving money on the table — the Table-row pattern (own €95 vs market €112 with half the compset sold out) had been happening invisibly for years; now it's an alert. Low-demand dates got braver — the system's ▼ recommendations, historically the ones instinct resists, filled beds that used to sit empty at aspirational prices. And the explainability earned the trust that made adoption real: a ~70% acceptance rate exists because every number arrives with its reasons, and the revenue manager remains the decision-maker — the human-in-the-loop design philosophy running through our monitoring work, applied to pricing.

The event layer produced the anecdote the client retells: a mid-size trade fair in a secondary city, detected from public sources weeks out, that their team had simply never tracked — priced properly for the first time. The engagement continues as the long-term partnership the client sought: PMS integration next, then channel-manager write-back, with the historical database compounding as the AI's training asset each season.

Why This Pattern Repeats in Hospitality

Independent hotel and hostel groups everywhere sit in the same gap: too small for enterprise RMS economics, too exposed to OTA price wars for spreadsheet pricing. The transferable architecture: like-for-like rate normalization (the four axes), a 90-day forward calendar as the collection unit, public event extraction geo-matched to properties, explainable recommendations with per-property guardrails, and self-healing collection on hostile OTA surfaces. The data engine is the moat — every season it runs, the forecasts get better and the switching cost grows.

Frequently Asked Questions

How is hotel rate shopping different from retail price monitoring?

It's calendar-shaped and like-for-like: every competitor rate must be normalized by check-in date, room type, cancellation policy, and occupancy, across a 90-day forward window — hundreds of rate points per competitor per cycle, not one price per SKU.

Can competitor availability really inform pricing?

Strongly — competitor sold-out dates are among the highest-signal pricing opportunities in the dataset, and they appear directly in recommendation reasons.

Is scraping OTA prices compliant?

The collection layer touches public rate and availability data only — no guest or personal data — with GDPR-documented posture, respectful pacing, and full lineage; program specifics are validated with counsel per our standing compliance framework.

How fast can a multi-property RMS data engine go live?

First property in ~4 weeks, a 14-property portfolio in ~9 in this engagement's structure. Contact Actowiz Solutions to scope your property set and compset.

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

▶

1 min

★★★★★

"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

▶

2 min

★★★★★

"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."

Iulen Ibanez

CEO / Datacy.es

▶

1:30

★★★★★

"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."

Febbin Chacko

-Fin, Small Business Owner