OpenMinds founder says, “Malaysia doesn’t lack MarTech—we lack translation”

• Malaysia’s MarTech efforts fail from poor execution, not tools—clear goals and clean data are key.
• Despite heavy digital spending, many Malaysian firms see little return.

Malaysia’s digital sector is moving fast. In the second quarter of 2025, digital investments rose 125% to RM29.47 billion (US$6.95 billion) under the Malaysia Digital initiative. That level of spending shows clear intent: more firms want to use data, AI, and modern marketing tools to win and grow. Yet many still struggle to turn that spend into profit.

“Malaysia and ASEAN more broadly doesn’t lack MarTech. Instead, we lack translation. The ability to turn data, tools, and spend into repeatable commercial outcomes,” Wong said. “Most MarTech investments fail not because the stack is weak, but because the operating model is.”

Wong sees the same traps across the region. “Stacks are bought before use cases are defined, objectives are media metrics, not margin, data quality is assumednot measured and experimentation is sporadic,” he said. The end result is familiar: case studies that look good on paper, dashboards that glow, but profits that stall.

Turning AI into outcomes

When used well, predictive analytics and AI can close that gap. Wong points to a set of direct uses tied to value, not vanity. “For example, predictive targeting finds persuadable customers so that offers land where they change behaviour leading to higher conversion rates,” he said. AI can also raise the odds that a customer stays. Early warning signals “trigger save actions at the right time that can reduce churn.”

AI’s reach goes beyond ads and email. Wong notes the link between demand and supply. Smarter forecasts “allowing inventory forecasts to be synced with marketing so that you can promote what you can deliver, reduce stockouts/markdowns, improve promise-date accuracy that can improve service alignment.” In short, “predictive analytics and AI helps companies match the right message, moment, and product to the right customer to improve conversion, lower churn, and have smarter inventory that will lead to happier customers.”

Why investment is surging now

The jump in digital investment is not random. “This surge is not by accident,” Wong said. Several shifts are at work. Retail media and commerce networks—instant payments, logistics, and marketplaces—now shorten the loop from a post or search to an actual sale. First-party data is growing, and with e-invoicing on the horizon, the quality of the signal should improve. Local cloud and clean rooms “cut latency and ease privacy concerns, enabling on-shore clean rooms and near real-time decisions and more.”

With the base in place, Wong advises firms to move fast on “data related plays.” What once sat in the “best practices” bucket now needs to be core. That list includes hyper-personalisationserver-side data collection, and consent receipts. Start now, he said, and businesses “will not need to play ketchup especially when e-invoicing is implemented for all in 2026.”

What each stakeholder should do in 2025

Wong splits the next steps across government, industry, and universities.

• Government: Introduce clauses for privacy safe data collaboration locally and in the region, fast-track sandboxes for specific industries eg retail media and mobility, reward outcome-based pilots and not just initiating them.
• Industry: Implement interoperable schemas across products, stores, and campaigns, and start looking into on-shore clean rooms with auditable outputs.
• Universities: Start implementing more practice-led curriculum that is co-taught with the industry. MarTech has been evolving very quickly thus industry partnerships are a great way to stay abreast of things.

Building regional models while guarding privacy

A key growth path lies across borders. But data rules differ in each ASEAN market, and users care about privacy. Wong argues that the system has to be private by design. “Cross-border data collaboration can only be unlocked if privacy is the architecture, not an afterthought,” he said.

The model keeps raw data in each country. “Every country will need to play an active role by keeping data in respective countries and moving only processing models and aggregates.” He points to federated learning, data clean rooms, and consent receipts with purpose limits—“train-but-don’t-see.” Shared taxonomies for events, SKUs, and timestamps align features across markets. Do that, he said, and “we will all have access to a regionally trained model that captures cultural nuance and seasonality that will outpace centric defaults without breaching privacy and ethics.”

The gaps that slow AI marketing

Three areas hold firms back: talent, infrastructure, and policy clarity.

On talent, Wong sees too many tool users and too few translators. “There are plenty of tool users today, but too few translators who can frame business questions, design tests, and productionise models that are aligned with business objectives,” he said. The pool is young, with siloed skills across data analysts and AI engineers.

On infrastructure, IDs are still split across systems, client-side tracking remains commonand consent logs are weak or missing. That “make predictions brittle and un-deployable,” even with rising spend on cloud and AI.

On policy, most SMEs do not face a hard gap, they face doubt. It is “policy clarity,” Wong said—the fear of non-compliance, the question of what to do, and the sense that any rule will be hard to meet—that blocks work and slows collaboration.

What to prioritise now

Wong’s closing advice is simple and direct. Stop renting reach. “Stop ‘renting’ your audience by purchasing them but to start building consented, portable first-party profiles across web, app, retail, and chat.”

Then set the right goals. “When implementing MarTech tools, don’t get caught up with vanity metrics, instead, measure incrementally based on your business’ objectives. From there, scale what works.”

You do not need the “next big thing” to progress, he said. Focus on real problems and steady gains. And keep guardrails in place. “Beneath all of these, remember that data governance is key. Codify consent state, set purpose limits, and implement audit trails so you move fast within regulatory guardrails.”

The money, tools, and platforms are here. The winners will be the teams that turn those inputs into repeatable outcomes, and do so with clear goals, clean data, tight tests, and respect for the user.

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