Foot traffic misreads lead to poor QSR site choices

High volumes mislead when they fail to reflect target customers.

Southeast Asia’s quick service restaurant (QSR) market is projected to reach $91 billion by 2028, but rapid expansion is exposing a new competitive divide: brands that use location intelligence effectively are outperforming those relying on traditional decision-making.

The shift is being driven by a move away from raw data towards actionable insights. According to Tim Shaw, Global Director in Market Planning at GapMaps, “we're transitioning to insight creation rather than data availability,” as retailers increasingly prioritise decision-ready intelligence over data volume.

This transformation is underpinned by the integration of multiple datasets. Mobile device data, once used primarily to track movement patterns, is now combined with census and behavioural data to provide a deeper understanding of catchments. Shaw noted that data is “blended with many other data sets… to better describe a catchment area,” enabling more precise targeting of consumer demand.

As a result, expansion strategies are shifting beyond traditional retail hubs. Whilst major shopping malls remain important for visibility, they represent only a fraction of potential growth. “We're moving away from understanding the biggest shopping malls… to the full retail hierarchy,” Shaw said, highlighting growing opportunities in high streets, neighbourhood centres and community malls.

Location intelligence is also moving beyond planning into execution. Brands are now using mobile data to activate new sites, targeting customers in real time. Shaw said data is increasingly used “to activate those assets as they open,” including directing promotions to nearby competitor locations to drive traffic.

However, the growing reliance on data also introduces new risks. One common mistake is overvaluing foot traffic volume. “Busy is only better when it's busy with your target customers,” Shaw warned, noting that misinterpreting traffic patterns can lead to poor site selection.

Foot traffic data itself has limitations. As a subset of mobile data, it lacks full accuracy and should not be used in isolation. Shaw emphasised that “it should be used in combination with other data sets,” particularly given the complexity of real-world retail environments.

The industry is also seeing the rise of predictive tools that refine expansion decisions. New platforms can analyse location characteristics and forecast performance, with some models explaining “about 50% of revenue performance at a store level through catchment characteristics.”

As QSR brands scale across Southeast Asia, the ability to interpret and act on data is becoming a defining factor. With a growing gap between data-driven operators and traditional players, success will increasingly depend on how effectively businesses translate location intelligence into profitable growth.