The client’s global travel marketplace serves leisure and business customers across multiple geographies. Their product catalog extends beyond transport and lodging to include a dedicated vertical for activities and experiences, encompassing theme park tickets, museum passes, guided tours, and seasonal events. The activities vertical represents a high-margin, price-sensitive segment with thousands of active listings spanning several key international markets.
The client required a structured competitor pricing research program operating daily across the full breadth of their activity listings. Their team needed reliable, comparison-ready pricing data extraction from rival platforms to drive real-time adjustments across thousands of active SKUs.
The client wanted to capture final transaction costs reflecting all applicable fees, promotional discounts, and tiered pricing structures.
The structural design of OTA booking platforms created a core operational issue: users could access final pricing information on rival platforms only after moving through multiple selection steps designed for buyers, not observers. Each challenge in competitor pricing research stemmed from this platform structure, and resolving it required human judgment at critical points in the data collection cycle.
To support accurate daily competitor price monitoring at scale, we built the solution around a structured data validation process, a consistent equivalency-checking method, and a fixed reporting cycle. This ensured that the data captured each day reflected true like-for-like pricing, included all relevant fee components, and reached the client's pricing team in a decision-ready format.
We built a specialized web research team. Each member handled a defined set of attractions and experience categories rather than working across the full portfolio indiscriminately. This design meant team members built genuine category depth — understanding how pricing varied by venue type, what inclusions were standard versus premium, and how specific platforms structured their booking flows for different attraction categories. Training covered competitor platform navigation, ticket equivalence assessment, and the precise pricing variables the client's pricing team needed to make sound listing decisions.
Before recording any competitor prices, the team applied a structured matching framework to each client listing to identify the nearest equivalent ticket type on rival platforms. Every comparison was aligned by activity type, access or benefits, ticket tier, and visitor category (for adult, child, or group tickets). This ensured that the client was comparing true equivalents across platforms, rather than drawing conclusions from offers that differed in structure or value.
Our team recorded prices only after completing the entire booking journey on each competitor platform, so the final figure reflected the actual payable amount after all visible discounts, offers, and fee components had been applied. To capture both short-notice and forward-booking behavior, each comparison was completed for two purchase windows: D1 and D7. Before finalizing the dataset, a second review layer checked every captured price against the live listing, which kept the overall error rate below 1.5%.
We ran a full comparison cycle every day and delivered the findings in a documented reporting format built for direct pricing action. Each report showed current competitor prices alongside the client's listed prices, highlighted gaps where the client was either underpriced or overpriced, tracked active promotions and discount activity on rival platforms, and surfaced pricing shifts between D1 and D7 windows to reveal demand-driven movement. The reporting also identified categories and listings where the client already held a competitive pricing advantage, quantified those margins where relevant, and flagged specific listings where prices could be increased without weakening the competitive position. This work was completed within a 5–6-hour daily research sprint, so pricing for high-demand tours and monuments could be captured before the next booking window closed, and the client still had time to implement price adjustments.
We replaced what had been a fragmented, slow-moving internal pricing process with a daily competitor price monitoring operation running at scale across the client’s full activities vertical. The program produced measurable improvements on every dimension the client had defined as success criteria: accuracy, coverage breadth, decision speed, and commercial impact. The results validated our expert-verified competitor pricing research approach over the automated alternatives considered and trialed in prior cycles.
98.5% Accuracy Rate Sustained across all Final Ticket Cost Comparisons Verified through a secondary review on every captured price point, across all monitored activity categories.
300+ Activities Tracked Daily with Full Competitor Platform Coverage The team covered the client's complete experience portfolio across rival OTAs and direct supplier sites within a 5–6-hour daily sprint.
35% Faster Pricing Decisions Driven by Daily Intelligence Achieved by replacing an internal cycle that previously took 3–4 days to produce comparable data.
15–20% Reduction in Revenue Leakage from Out-of-Position Listings Daily gap analysis surfaced both underpriced and overpriced listings across key activity categories, reducing revenue leakage by 15–20%.
Keeping up with competitor pricing in the online travel and experiences category is not a set-and-forget problem — it requires fresh, accurate data delivered every single day. Our specialized web research teams build and run competitor price monitoring programs that are accurate, scalable, and free of the automation fragility that makes consistent daily intelligence so difficult to maintain.
If your pricing team is working with stale data or struggling to embed automation while maintaining accurate outcomes, we can change that. Get in touch to discuss your requirements.
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