AEON Malaysia expands AI customer profiling

Financial activity can reveal signals absent from retail transactions.

AEON Malaysia’s data and artificial intelligence (AI) arm in Malaysia is moving beyond transaction-based customer segments to identify life stages using data from its retail, mall, and financial service businesses.

“Before this, retail personalisation was mostly built on transactional information, demographic information, segmentation, loyalty, and campaigns,” Miki Ong, general manager at AEON360, a newly set-up joint venture between AEON Co. (M) Bhd and AEON Credit Service (M) Berhad, told Retail Asia in an interview. “But now, with AI, we're looking at something further beyond that—the capability of looking at life stages.”

Ong cited parents with young children as an example. Rather than treating a customer who buys baby formula as a fixed segment, the platform can track signals during the first 1,000 days after the purchase and identify related needs, such as daycare.

“It’s no longer about just targeting a segment and communicating with it,” she said. “It’s about following these segments and evolving together with them.”

The recommendation engine combines transaction data, purchase frequency, engagement, and customer-submitted preferences rather than treating individual signals as conclusions, Ong said.

AI interprets those signals to identify patterns and generate recommendations.

Data from AEON’s retail, mall, and financial service businesses could also provide signals that would not appear in a single business line, Ong said.

A customer taking a personal loan or subscribing to a confinement centre around the same time as buying baby products, for example, could indicate a particular life stage.

The broader data pool could reduce the risk of a “filter bubble” in AI-driven personalisation, she said.

The platform also considers what a product can address for a customer instead of ranking products solely by transaction volume. This is meant to prevent high-volume retail categories from dominating lower-volume areas such as financial services.

Ong said customers would remain responsible for purchasing decisions as AI takes on more of the recommendation process.

“AI plays a stronger role in reducing complexity,” she said, adding that the technology would narrow options and surface more relevant recommendations rather than make purchasing decisions for customers.

“AI recommends, but the customer decides at the end of the day,” she said.