The Passenger-to-Sale Conversion Crisis in Travel Retail — And How AI Can Fix It

Travel retail is experiencing a paradox that should alarm every operator, brand owner, and airport authority in the industry. Passenger numbers have fully recovered — and in many regions exceeded — pre-pandemic levels. Yet the money those passengers spend in duty-free and travel retail stores has not followed. The gap between footfall and revenue is no longer a temporary post-COVID anomaly. It has become structural.

The question the European Travel Retail Confederation (ETRC) posed in early 2026 captures the urgency perfectly: can this industry use AI in a meaningful way to become more productive, more competitive, and more attractive?

After researching the latest industry data from Kearney, the Moodie Davitt Report, TRBusiness, and DFNI — and drawing on my own experience building AI-powered retail analytics tools — I believe the answer is yes. But only if the industry moves beyond buzzwords and toward specific, measurable AI applications that directly attack the conversion problem.

The Numbers That Define the Crisis

The Travel Retail Conversion Funnel

Kearney's annual TFWA research paints a stark picture. Average spend per passenger has declined sharply since peaking, and the spread between traffic growth (8.5%) and travel retail value growth (3.0%) widened markedly in 2024. In Europe, ETRC data shows that while 2025 delivered record sales of €10.13 billion (+5.5%), passenger numbers grew faster at +5.8%, meaning average spend per passenger actually softened by -0.3% year-on-year.

Average Spend per Passenger — The Declining Trend

This is not a demand problem — it is a conversion and relevance problem. The passengers are there. They are simply not buying.

Why Passengers Walk Past: The Root Causes

The Kearney research identifies several interconnected drivers behind the conversion collapse, and none of them are simple to fix with traditional retail tactics.

1. The Price Promise Has Eroded

More than half of travellers do not perceive travel retail prices as competitive compared with domestic retail or online alternatives. In an era of instant price comparison on smartphones, the historic "duty-free = cheaper" equation no longer holds in many categories and many airports.

2. Assortment Misalignment

A third of non-buyers say the products available simply do not match what they want. Travel retail assortments have traditionally been driven by brand concession agreements and category conventions rather than real-time demand signals from the passengers actually in the terminal on any given day.

3. The Mid-Tier Squeeze

Consumer spending is polarising. The traditional mid-tier segment ($50–$299) is declining as shoppers bifurcate toward either impulse-priced items or premium purchases ($300–$999+). Retailers clinging to mid-range assortments are losing relevance at both ends.

4. Generational Shift

77% of Gen Z and Millennial travellers say experience is a key reason for purchasing duty-free — not price. Yet most travel retail environments remain transactional rather than experiential. The industry is optimised for a buying psychology that is ageing out.

5. Operational Blind Spots

On the operations side, shelf compliance gaps, reactive inventory management, and lack of real-time visibility into what is actually happening on the shop floor mean that even when there is demand, the execution often fails to capture it. A human auditing a 40-SKU section catches 60–70% of compliance issues; AI-powered computer vision catches 90–95%+.

Five AI-Powered Solutions That Can Close the Gap

The industry does not need more generic "digital transformation" roadmaps. It needs specific, deployable AI applications that directly improve conversion, spend per passenger, and operational execution. Here are five that can make a measurable difference today.

AI Solutions at a Glance

Solution	Problem It Solves	Key Impact	Complexity
Predictive Demand Forecasting	Stockouts & overstock from static planning	High Aligns inventory to real-time passenger mix	Medium
Real-Time Personalisation	Generic promotions that ignore passenger profiles	High 90%+ consumers say personalised offers increase spend	High
Computer Vision Shelf Compliance	Planogram gaps, misplacements, pricing errors	High 95%+ accuracy vs. 60–70% manual audits	Medium
Dynamic Pricing	Eroded price promise vs. domestic & online retail	Medium Competitive, data-driven pricing in real time	Medium
AI Staff Enablement	High turnover, knowledge gaps, inconsistent selling	Medium Better conversion in premium categories	Low

1. Predictive Demand Forecasting by Flight and Route

Traditional travel retail inventory planning relies on broad seasonality patterns and historical category averages. But the composition of passengers in any given terminal changes dramatically by the hour — a morning flight to Dubai carries a fundamentally different spending profile than an afternoon departure to Warsaw.

AI-powered demand forecasting models can ingest flight schedule data, route-level passenger demographics, historical POS data, and even external signals like exchange rates and local holiday calendars to predict category-level demand at the daily or even hourly level. This allows operators to dynamically adjust staffing, promotional displays, and stock positioning to match the actual passengers in the building — not a generic weekly average.

The impact is direct: fewer stockouts during peak demand windows, less overstock of slow-moving SKUs, and promotional timing that aligns with who is actually there to buy.

2. Real-Time Personalisation Through Traveller Segmentation

Kearney's research found that over 90% of consumers say personalised offers could encourage them to spend more. Yet most travel retail personalisation today is limited to broad loyalty programme emails sent days before travel.

The opportunity is in real-time, in-terminal personalisation. By combining anonymised Wi-Fi/Bluetooth signals, boarding pass scan data (where privacy regulations allow), and purchase history from loyalty programmes, AI systems can segment travellers into actionable clusters — high-value repeater, impulse beauty buyer, spirits gifter, first-time premium shopper — and trigger contextually relevant offers via digital screens, app notifications, or staff-facing tablets as passengers move through the retail zone.

This is not science fiction. Airlines and hotels have been doing dynamic pricing and personalised offers for years. Travel retail is simply behind in applying the same intelligence to the physical store environment.

3. Computer Vision for Shelf Compliance and Execution Intelligence

Planogram compliance — whether the right products are in the right position at the right price — is one of the most overlooked drivers of conversion in travel retail. When a promotional display is supposed to feature a limited-edition whisky but the shelf has been filled with a different SKU, that is a missed sale that no one even knows happened.

AI-powered image recognition platforms can now monitor shelf compliance across dozens of airports in near-real-time, identifying gaps, misplacements, and pricing errors with 95%+ accuracy. Research shows high planogram compliance correlates with an 8.1% lift in retail profitability.

Platforms operating in this space, including the AI-powered retail execution tools emerging in travel retail, are already providing SKU-level insights into category share, pricing, and promotional compliance across 50+ airports globally. This is not future technology — it is deployed and generating ROI today.

4. Dynamic Pricing and Promotion Optimisation

The duty-free price promise needs to be rebuilt with data, not assumptions. AI-powered pricing engines can continuously benchmark travel retail prices against local domestic retail, online marketplaces, and competitor airports, adjusting promotional depth and timing to ensure the value proposition is genuinely competitive on the products that matter most to the passengers actually passing through.

Combined with demand forecasting, this creates a feedback loop: the system predicts what products will be in demand, prices them competitively for the expected passenger mix, and measures conversion in real time to refine future predictions. The static "same promotion for three months" approach is replaced by responsive, data-driven pricing that adapts to market conditions weekly or even daily.

5. AI-Assisted Staff Enablement

Travel retail is still fundamentally a human business — particularly in premium categories like beauty, spirits, and fashion where sales associate interaction drives conversion. But the knowledge gap is significant. Staff turnover in airport retail is high, product assortments rotate frequently, and training programmes struggle to keep pace.

AI can bridge this gap through intelligent staff-facing tools: real-time product knowledge assistants, customer interaction guides that surface relevant talking points based on what the shopper is browsing, and performance dashboards that help managers identify which selling behaviours correlate with higher conversion. The ETRC has specifically called out AI for staff training as one of the biggest breakthrough opportunities for the channel.

The Integration Challenge: Why Point Solutions Are Not Enough

The travel retail industry's biggest risk with AI is not rejection — it is fragmentation. Deploying a standalone demand forecasting tool here, a shelf monitoring camera there, and a personalisation app somewhere else creates data silos that limit the value of each individual system.

The real unlock comes from integration. When the demand forecast informs the personalisation engine, which informs the pricing system, which is validated by the shelf compliance data, which feeds back into the forecast — that is when AI moves from incremental improvement to structural transformation of the conversion funnel.

59% of global operators now use advanced analytics to monitor purchasing behaviour, and those implementations have boosted conversion rates by 31%. But the industry is still in the early stages. IDC projects that by 2028, half of major retailers will deploy advanced tools to close the AI skills gap — meaning travel retail has roughly two years to build capability before it becomes table stakes.

What Comes Next

The passenger-to-sale gap is not going to close on its own. Geopolitical uncertainty, the China slowdown, the mid-tier squeeze, and the generational shift in buying behaviour are all structural headwinds that will persist through 2026 and beyond.

But the $107.6 billion airport retailing market projected for 2035 is not going to be captured by operators doing things the way they did in 2019. It will be captured by those who deploy AI not as a marketing talking point but as operational infrastructure — forecasting demand before passengers arrive, personalising offers in real time, ensuring shelves are compliant, pricing competitively, and enabling staff to sell better.

The question is no longer whether AI can help travel retail. The ETRC, Kearney, and every major industry body have already answered that. The question is which operators will move first — and which will be left explaining why their conversion rates are still stuck at 5%.