Travel and hospitality brands are drowning in guest data. Every flight booking, loyalty redemption, mobile application activity, and call center interaction generates actionable signals about customer behavior. Yet instead of fueling innovation, the data creates bottlenecks. Records are in silos, identifiers don't match, and preparing datasets for machine learning requires weeks of manual cleansing.
The result is a frustration familiar to many: data scientists spend more time parsing and manually merging data together than training models. Campaigns that rely on predictive insights are delayed, and AI investments struggle to show measurable value.
Amperity changes this trajectory. By applying machine learning to the challenge of Identity Resolution, our platform unifies fragmented records into complete and accurate customer profiles. The profiles are made available in formats appropriate for modeling so that they can be applied directly in platforms like Databricks. Through Amperity, airlines, hotels, and resorts are able to move from raw data to predictive insights in a portion of the time it would take conventional approaches to achieve, usually within minutes to days depending on the data environment.
The issue: too much data, too little value
For most travel brands, the challenge isn’t a scarcity of data but data sprawl. An airline may collect passenger details in a booking engine, loyalty activity in a member database, service interactions in a call center system, and digital behavior in its website and app. Each source has value, but together they create a patchwork of incomplete records.
The same traveler might show up as three different people: one in loyalty, one in bookings, and one in the app. Data teams spend weeks just untangling duplicates before they can even start modeling. Data teams spend weeks de-duplicating, correcting errors, and re-forming fields just to build a training dataset. And even then, the result is often incomplete, with major portions of a customer's history absent or out of alignment.
These delays hold up more than the analytics pipeline. They hinder the ability to initiate timely retention campaigns, personalize offers, or gauge the impact of AI projects. When modeling relies on manual prep, innovation stalls.
Why Identity Resolution matters first
At the core of this challenge is identity. When there’s no reliable way to confirm that “J. Smith” and “Jonathan Smith” are the same traveler, data remains fragmented, and any predictive modeling built on it becomes unstable.
Amperity resolves this with Stitch, our patented, machine learning–based Identity Resolution tool. Rather than relying solely on deterministic rules like exact email matches, Stitch evaluates probabilities across multiple attributes such as names, booking references, loyalty IDs, and phone numbers. It detects patterns in how data is entered (even when incomplete or inconsistent) and unifies records that belong to the same person.
Importantly, Stitch combines deterministic and probabilistic matching. Users can define explicit rules (for example, ensuring that records with different loyalty IDs are never clustered together) while allowing the ML model to make informed matches when deterministic data is missing. These rules and thresholds are fully configurable, giving brands control over how matching logic is applied to their data.
Stitch is also transparent. Data teams can audit match scores, review which attributes influenced each decision, and adjust sensitivity to align with their business needs. This blend of ML-driven automation, configurable rules, and human oversight ensures accuracy at scale without sacrificing control or visibility.
The result is a foundation of clean, complete, and continuously improving customer profiles; an ideal starting point for data science and analytics teams alike.
Feeding ML models with Amperity profiles
Value accumulates quickly as soon as customer identities are resolved. Amperity organizes unified records into structured Customer 360 Profiles that include attributes such as predicted lifetime value, churn likelihood, or other behavioral traits aggregated and computed from both batch and real-time data. These profiles are delivered in standardized formats that can feed directly into machine learning environments.
Instead of spending cycles on deduplication or feature engineering, data teams can start training models on complete guest histories much earlier. Profiles can be exported into platforms like Databricks, Snowflake or Google BigQuery, where upgrade propensity models, rebooking probability models, or loyalty churn models can be developed.
This creates a feedback loop. While results are being measured, Amperity ingests new behavior data, Stitch refreshes the profiles, and models improve with each iteration. Long term, the system is not just a data pipe but a dynamic AI foundation, evolving from each guest interaction.
Example: an airline’s first weeks with Amperity
Consider a worldwide carrier that for a long time has fought with messy data. Customer information is fragmented across its booking system, loyalty database, and digital assets. Analysts spend weeks merging spreadsheets, writing transformation scripts, and applying brittle rules to reconcile duplicates. By the time the data is actionable, the opportunity for a retention campaign often has passed.
With Amperity, the airline does this differently. Raw files are pulled in directly from each system with little up-front preparation. Amperity’s semantic tagging quickly matches fields between sources, and Stitch begins analyzing the data, taking into account the probability that “J. Smith” in a booking record, “Jonathan Smith” in loyalty, and “Jon S.” in Gmail are the same individual. Within hours to days, depending on data volume and source complexity, these records are consolidated into actionable customer profiles.
What stands out is not just the speed, but the transparency. The airline’s data team can see how Stitch arrived at every decision, including the scores and attributes that drove the match. If they want to tighten or loosen thresholds, they can. Instead of questioning the integrity of the inputs, the team has confidence in the data foundation.
At the close of week one, the airline possesses what it's never previously had: a single, complete view of its travelers poised to fuel machine learning efforts without manual data prep's heavy lifting. The data science team exports enriched profiles to Databricks and begins exploring churn models on a solid foundation.
The heavy lifting (once the drudgery of manual cleanup) is now on the shoulders of Amperity's ML engine. The airline's team is free to advance to modeling, strategy, and campaign execution.
Results you can expect
Projects that used to stall out in data prep finally move into production. Models trained on unified profiles are more accurate because they're trained on whole guest histories, not fragmented records
As a consequence, retention campaigns target the correct travelers, loyalty offers are more effectively targeted, and the results can be measured with more certainty. Data science teams can conduct experiments round the clock, speeding up the rate of innovation, rather than having to wait weeks to try out new models.
Why Amperity
Amperity is built from the ground up for travel and hospitality brands, where Identity Resolution is challenged by the complexity of scale. Our Stitch technology has been trained on billions of records, enabling it to match with accuracy across different systems.
Transparency is what sets Amperity apart. Every matching decision is explainable, auditable, and tunable—so teams understand exactly how profiles are built. Out-of-the-box Stitch Benchmarks provide visual QA of model results, helping users see how matches perform and guiding them through the levers they can adjust to improve accuracy. This gives brands both confidence in the results and a clear path to optimization.
Because Amperity was designed from the beginning with AI and ML use cases in mind, not only are the profiles it creates clean, but they are also shaped for direct use in modeling pipelines. Airlines, hotel chains, and resorts already rely on Amperity to accelerate predictive analytics without compromising compliance with international privacy laws.
Practically speaking, this means less time is spent on data preparation and more time on AI deployment and driving real outcomes for your guests.
Request a demo to discover how quickly Amperity's machine learning can start delivering ROI through cleaner, more complete customer intelligence.