The client is a major online travel agency operating across multiple geographies, offering bookings for flights, accommodations, car rentals, and on-ground experiences. They have a dedicated vertical selling tickets for a wide range of activities and attractions — from historical monuments, famous buildings, and towers to amusement parks, gardens, museums, fairs, and curated cultural experience packages. Their activities portfolio spans thousands of listings across categories such as sightseeing, entertainment, adventure, and local experiences. worldwide.
The client required a dedicated competitor price monitoring team to track and compare ticket prices for activities and attractions listed on rival online travel platforms. The scope covered:
The project required a combination of manual price validation, structured data collection, and systematic reporting—essentially, comprehensive data support for a competitor price monitoring operation built from the ground up.
The obvious question with any competitor price monitoring project at this scale is: why not automate it entirely?
While automation works for standard e-commerce products with listed prices and universal identifiers, activity and attraction ticketing on travel agency platforms is a fundamentally different problem. For this project, "automation" wasn't just difficult to implement — it was dangerous to trust. The goal was to achieve a 1:1 price comparison across a fragmented global market, but the "real" price was not a static, scrapable data point but the result of multiple variables, buried under layers of non-standardized data, depending on the choices an end-user made during the booking flow.
Most competitor platforms did not display the final ticket price on the listing page. The actual cost a traveler paid only became visible after selecting a date, visitor category, ticket tier, and sometimes a time slot. On many platforms, service fees, booking charges, and dynamic discounts were applied only at the checkout stage. A scraper that read the listing page would have captured the wrong number, and pricing decisions based on that number would have been worse than no data at all.
Unlike retail, where a UPC or SKU resolves ambiguity instantly, these activity tickets had no standardized identifiers. A museum entry ticket on one platform might have been listed as "Skip the Line — Louvre Museum Entry," while a competitor listed the same access as "Louvre Priority Admission with Guided Introduction." These may or may not have had the same inclusions, access level, or cancellation terms — making this a judgment task, not a pattern-matching task.
The client needed prices for D1 (tomorrow) and D7 (seven days out). This meant the comparison was against a moving target, as availability, demand-based surcharges, and promotional windows changed the price of the same ticket on the same platform from one day to the next. Automated systems could have been configured for date-specific queries, but validating that the returned price reflected the correct date, ticket tier, and final cost after fees still required human intervention.
OTA websites redesigned checkout flows, restructured fee disclosures, and modified promotional mechanics regularly — often without notice. An automated pipeline built for one platform's structure could have broken silently when that structure changed, delivering outdated or incorrect data without flagging the error. A trained monitoring team noticed when a platform's pricing presentation had shifted and adapted the comparison methodology the same day.
If the client had adjusted prices based on inaccurate competitor data — because a scraper missed a fee, matched the wrong ticket tier, or pulled a stale price — the downstream impact would have been either lost revenue (pricing too low) or lost bookings (pricing too high). In a market where travelers compare across three or four OTAs before purchasing, even small pricing errors, compounded across hundreds of listings daily, can add up to huge revenue leaks.
To address the client's need for reliable, daily competitor price monitoring, we combined a trained monitoring team, a standardized comparison methodology, and a disciplined reporting schedule.
We assigned a specialized web research team trained specifically on the client's activity categories, ticket structures, and competitor landscape. Each team member was responsible for a defined set of categories, ensuring deep familiarity with how pricing worked for attractions, experiences, and entertainment venues. This eliminated the learning curve that rotating or generalist teams would face and kept accuracy consistent from day one.
As the client's portfolio evolved — with new activities added, seasonal attractions rotating in, and older listings retired — our team updated the tracking scope accordingly.
We developed a structured data matching and validation process to ensure like-for-like price comparisons across platforms:
The team executed a full comparison cycle every day and reported it to the client in a documented format, covering:
This research was compressed into a 5–6 hour "sprint" every day to capture pricing for popular tours and monuments before the next day's booking window closed, and to allow the client time to implement price adjustments.
We delivered structured, source-documented environmental data through our environmental research services, consistently meeting accuracy benchmarks and the client's 12-month coverage timeline. Following the initial engagement, the client expanded the scope to include data maintenance services with quarterly data refreshes.
98.5% Accuracy Rate Achieved in Final Ticket Cost ComparisonsValidated through dual-layer review across all tracked categories.
300+ Activities and
Attractions Tracked
DailyAcross multiple competitor platforms, covering the client's full portfolio, within 5-6-hour sprints.
35% Faster Pricing Decisions Enabled by Daily, Structured IntelligenceReplacing a process that previously took the client's internal team 3–4 days per cycle.
15–20% Reduction in Revenue Leakage from Mispriced ListingsIdentified through systematic gap analysis across the seven-day window.
When the real price hides behind booking flows, dynamic fees, and non-standardized listings, off-the-shelf monitoring tools give you incomplete data — and incomplete data leads to mispriced inventory.
SunTec India provides a managed "human-in-the-loop" solution that combines dedicated web research teams with structured data workflows — from web scraping to manual price validation, from data standardization to visualized reporting — and captures useful data reflecting market pricing reality. From semantic matching of non-standardized activities to final-cost extraction across varying checkout flows, we provide the full-stack data support you need to regain pricing control.
Stop losing bookings to pricing gaps you can’t see. Reach out to our team for competitor pricing visibility you can trust.