How Travel Tech Companies Identify the Fastest-Growing Accounts

Outbound is a critical growth lever for travel technology companies. However, prioritization is one of the hardest problems to solve.

Revenue potential in this market is not determined by headcount or firmographics alone. It is driven by booking volume, visitor traffic, seasonality, and real-world demand. Most of these signals are either fragmented, locked inside consumer platforms, or difficult to capture reliably at scale.

A global travel tech platform faced this challenge as it expanded outbound across SMB and mid-market operators. The team needed a way to understand:

• Which operators were commercially active and in demand
  
  
• Which booking and ticketing systems competitors were embedded on
  
  
• Where sales teams should focus to maximize ACV and GMV

Internal datasets and paid lists lacked accuracy and coverage. Manual research and outsourced enrichment slowed execution and introduced inconsistency. As outbound scaled, account prioritization became the primary constraint.

Turning Public Web Signals Into Revenue Intelligence

To address this, the team partnered with Landbase to build an agentic enrichment workflow capable of extracting high-signal data directly from the public web.

Rather than relying on static lists, Landbase deployed autonomous agents that analyzed each operator’s digital footprint and transformed unstructured signals into structured GTM intelligence.

A multi-agent approach to account enrichment

Landbase orchestrated several agents across 2,000 operator websites:

• AI research agents estimated annual website traffic using multiple sources with confidence scoring and attribution
  
  
• Web crawler agents scanned HTML and JavaScript to detect installed booking and ticketing systems
  
  
• TripAdvisor intelligence agents located matching listings and extracted review volume and ratings while navigating bot protection
  
  
• Google Business Profile agents enriched Google review counts and ratings at scale
  
  
• A data fusion layer unified all signals into a single prioritization-ready dataset

The workflow was designed for repeatability, allowing enrichment to run daily and expand easily as the addressable market grew.

Clear Signals. Sharper Focus.

The results changed how the revenue team approached outbound.

Enrichment coverage at scale

Review data revealed strong indicators of real-world demand, with thousands of customer reviews per operator on average. When combined with installed technology detection, this enabled automated prioritization based on both value potential and competitive context.

Key insights uncovered

• Most operators ran on WordPress or Wix with embedded booking plugins
  
  
• A previously under-recognized competitor appeared consistently across high-demand accounts
  
  
• Review volume proved to be a strong proxy for commercial activity

Sales teams could now rank accounts by where demand already existed rather than treating every prospect equally.

Scaling Outbound Without Scaling Manual Work

With Landbase, the platform replaced manual research with a system that continuously surfaced the most promising accounts.

Revenue teams were able to:

• Prioritize operators with proven demand
  
  
• Personalize outreach based on competitor technology in use
  
  
• Refresh target lists as market conditions changed
  
  
• Reduce reliance on external enrichment vendors

The result was a faster and more focused outbound motion built on signals that correlate with revenue.

Laying the Foundation for Predictive Prioritization

Beyond immediate gains, the enriched dataset created new modeling opportunities.

By combining website traffic estimates, TripAdvisor review volume, and Google review volume, the team identified a path to predict demand even where direct traffic data is unavailable. This approach could extend prioritization coverage to nearly the entire market.

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