Airport retail is a strange business. The audience is captive but constantly moving, and the crowd at 7am on a Tuesday looks nothing like the crowd at 9pm on a Friday. Deciding which shops belong where is a long-term leasing decision made with short-term information.
A case study published on Fusionex Group’s own articles site in March 2019, and republished by Asian Data Science in January 2020, describes a proof of concept with Malaysia Airports to close that gap at KLIA. It is a smaller story than an award or a national platform, and in some ways a more revealing one.
1. The problem Malaysia Airports was trying to solve
The case study puts KLIA’s retail space at approximately 400,000 square feet, spread across the arrival and departure halls and the check-in areas. Shopper numbers swing by time of day and day of the week.
Malaysia Airports wanted to collect data near the shops more often and more accurately. The stated goals were practical: work out when to run promotions and what they achieved, find the hotspots where spending happens, and tell local shoppers apart from international travellers.
The last goal is the big one. According to the case study, better data would inform what the retail mix should be, whether the terminal needs more food, clothing, souvenirs or something else.
2. What was actually built
Fusionex’s business intelligence platform was combined with Internet of Things sensors. These were placed in the main terminal’s arrival and departure halls, at the contact piers for international and domestic flights, and in and around the retail areas.
The sensors were wireless and needed no lengthy installation. They included devices with Bluetooth and Wi-Fi detection, installed with the solution provider Tapway.
Alongside them, a mobile app collected basic demographic details such as gender and nationality, with a campaign to get travellers to sign up. Connectors pulled the different data sets into one platform, and automated dashboards let Malaysia Airports view revenue in near real time.
The case study says the work was supported and co-funded by the Malaysia Digital Economy Corporation (MDEC).
3. Why “near real time” is the real upgrade
Most retail landlords learn how a tenant is doing from periodic sales reports. This project aimed to put tenant performance next to passenger traffic that could be broken down by day and even by hour.
That changes the questions a landlord can ask. Not “did this shop do well last quarter?” but “did footfall past this shop rise when that promotion ran on Thursday evening?” It is the same shift we saw with Metrojaya’s move online: the technology is the easy part, and the value sits in whether the business changes its decisions because of what the data shows.
4. The part worth slowing down on
The case study lists demographic data, including gender, date of birth and nationality, among the information that would help Malaysia Airports understand which outlets appeal to which groups. It also describes a possible future step: push notifications offering a promotion when a traveller walks near a shop they might like.
To be fair, the app data came from travellers who signed up. The sensors are a different kind of collection, because they detect devices rather than asking people. The case study doesn’t describe how device data was anonymised or how long it was kept. That is normal for a vendor case study, but it is the first question any similar project would face today, especially as Malaysia builds a national data commission with authority over personal data.
5. What a proof of concept is, and isn’t
The project is described as a proof of concept, and neither source reports outcome figures: no change in revenue, dwell time or footfall. Neither says what happened after the trial.
That is worth stating plainly rather than filling in. What the case study does show is a design: sensors, an app, a data platform and a dashboard, joined up inside Malaysia’s main international airport.
6. What it actually means
Airports are among the few places in Malaysia where foot traffic, nationality and spending meet under one roof. That makes them a natural testing ground for retail analytics, and an obvious place for the hard questions about consent.
The map was the achievable part. Deciding which shops belong on it, and how much a traveller should be asked to share, is the work that never finishes.
Frequently asked questions
What was the Fusionex and Malaysia Airports project?
It was a proof of concept to build a business intelligence platform that would enhance Malaysia Airports’ retailer management system within KLIA, using IoT sensors and a mobile app to collect retail and traveller data.
What kind of sensors were used?
According to the case study, wireless sensors with Bluetooth and Wi-Fi detection capabilities were installed with the solution provider Tapway in the arrival and departure halls, the contact piers and around the retail areas.
Did the project report results?
No. The published case study describes the intended benefits, such as better timing of promotions and a better-informed retail mix, but gives no outcome figures.
Source: Airport data insights to optimize customer satisfaction, Fusionex Articles, 13 March 2019.
Source: Malaysia Airports develops data insights to optimize customer satisfaction, Asian Data Science, 10 January 2020.