Your API Returns Perfect Data. Users Still Can't Book

Modern hotel APIs have become remarkably capable. We deliver millions of properties, real-time pricing, detailed amenities, reviews, comprehensive imagery. The data quality problem that plagued the industry a decade ago has largely been solved.

And yet, according to Accenture's 2024 Consumer Pulse Survey, 73% of travelers abandoned a hotel booking in the last three months. The data is there. The inventory is available. But something is breaking down in the decision process.

The Abundance Challenge

I think the industry has been so focused on delivering comprehensive data that we haven't fully addressed what happens downstream.

A typical search in a major city returns hundreds of hotels. Each property includes dozens of attributes. Expedia's EPS Rapid API alone provides 13 million property images, 75 million traveler ratings, and 58 million reviews. Sabre's Content Services enables filtering by room size, breakfast options, and specific amenities.

The data is accurate and real-time. It's also overwhelming.

Travel platforms recognize this. They invest heavily in filtering systems, recommendation engines, and personalization layers to help users navigate the abundance. But every platform is solving the same "too much good data" problem independently, building custom logic on top of similar datasets.

A Different Approach

This raises an interesting question: what if APIs could participate in the decisioning layer?

Not to replace what platforms build, but to provide tools that make building better experiences faster and simpler.

With advances in AI, this is now technically feasible. An API could return curated recommendations with explanations alongside the complete dataset. Platforms would have the flexibility to use the recommendations directly, incorporate them into their own algorithms, or ignore them entirely and build from scratch.

The consumer behavior suggests there's demand for this shift. The same Accenture survey found that 60% of travelers are open to using AI advisors to help find and book stays, and 65% are open to AI agents that can take a more proactive role in managing their trips.

Travelers are increasingly asking for guidance over endless options.

The Open Questions

There are legitimate challenges to work through.

Where should decisioning actually live—in the API or on the platform? Both approaches have merit. API-level decisioning provides consistency and allows improvements to benefit all customers simultaneously. Platform-level decisioning enables customization for different user segments and use cases.

The answer might be offering both options.

There are also questions around liability and transparency. If an API provides recommendations, who is responsible when something goes wrong? How do you ensure users understand why they're seeing specific suggestions?

And there's the fundamental question of product-market fit. Some platforms may prefer full control over their recommendation logic. Others might welcome decision support that reduces their engineering burden. The answer likely varies by company size, technical capability, and specific use case.

What This Could Look Like

An API response might include:

• Recommended properties with natural language explanations of relevance

• The complete dataset for custom implementations

• Configurable decision criteria that platforms can adjust

• Clear transparency on recommendation logic

The key would be flexibility. Platforms could adopt the layer that makes sense for their product while retaining full access to underlying data.

The Broader Question

Travel APIs have solved genuinely difficult technical problems. Multi-supplier aggregation, content deduplication, real-time availability, global coverage. This infrastructure remains essential and isn't going away.

But as data quality becomes table stakes across providers, differentiation may shift to the next layer up.

Decision support might be one answer. Better personalization tools might be another. There are probably other approaches worth exploring.

What's clear is that comprehensive data creates downstream complexity. Platforms are allocating significant engineering resources to manage that complexity. If APIs can help address this at the infrastructure level, it could create meaningful value.

The technology to enable decision compression exists now in ways it didn't even two years ago. The question is whether it's the right capability to build, and whether it's something customers actually want from their API provider.

Worth considering as the market continues to evolve.

What's your perspective? Should APIs remain focused purely on data delivery, or is there value in decision support capabilities?