Journey to Success (vol VIII)

Please introduce yourself and kickstart us with your career journey so far.

I’m the Director of Market Insights at Measurable AI, a global tech company based in Hong Kong. We collect receipts from about 3 million users worldwide, mainly in Southeast Asia and Latin America. With these receipts, we provide valuable insights into consumer behaviour and competition to e-commerce, ride-hailing, fintech companies, and investors. It’s a unique approach because, unlike traditional surveys, we offer actual transaction data, which is more accurate and insightful.

I’m originally from Indonesia, but I’ve lived in the US, Singapore, and now Hong Kong, with a move to the Netherlands coming up. I studied at Cornell University, where I majored in Global Development. Initially, I wanted to “change the world” by understanding why some countries are developing while others are mature economies. This led me to question the whole concept of development and whether economic growth is necessary for happiness. I realised my passion lay in data, particularly social science data—understanding why people behave the way they do based on various factors like origin and gender.

After Cornell, I joined Agility Research and Strategy, focusing on luxury market research. It was a funny switch from studying developing countries to understanding luxury consumers and brands like Chanel and Gucci. I was intrigued by the concept of luxury and how it evolved from being a status symbol to representing identity and freedom for millennials and Gen Z.

From there, I moved to the Economist Intelligence Unit, shifting from luxury to healthcare research and consulting. We helped medical device companies understand market size, hospital count, and market entry strategies. It was a big change, driven by my curiosity about different industries. The Economist has a big name, and working there was like a dream job.

After my master’s at ESSEC Business School, I joined Grab in Singapore, a Southeast Asian super app. My role involved data insights on industry trends and competition, which was instrumental in my current role. At Measurable AI, I work with clients in e-commerce, ride-hailing, and food delivery, providing them with transaction-based data. It’s fulfilling because our data is more valuable than traditional survey-based data, and my previous experiences have prepared me well for this.

What is a day in the life of somebody working in Market Insights?

At Measurable AI, my role involves making sense of the vast data we collect from receipts. These receipts provide detailed information about purchases—what was bought, the price, and the timing. When combined with demographic data, this information helps create a clear picture of consumer behaviour.

Over time, the process of collating data and insights has evolved significantly. While all methods of data collection are valid, my preference, given my industry, is for transaction data like receipts, which we consider the best source of insights. However, other methods have their own strengths. Think of data collection methods as four quadrants: primary vs. secondary and quantitative vs. qualitative. Primary research means you collect the data yourself, while secondary research means you get it from other sources. Quantitative data provides statistics and numbers, whereas qualitative data is more descriptive. In the past, primary quantitative research was best done through surveys. Big players would send out online surveys to gather data. However, now we have an abundance of secondary quantitative data that provides the same information without needing to survey people. For instance, instead of asking people what they bought last week, we can look at their email receipts to know their purchases, frequency, and value.

Similarly, on a business-to-business level, methods have advanced. For example, instead of surveying farmers about corn production, we can now use satellite imagery to estimate the amount of corn being grown. These satellites are so advanced that they can zoom in on specific areas, measure field sizes, and calculate production accurately.

Survey companies have also adapted by diversifying their methods and exploring new business avenues. On the qualitative side, methods like interviews, focus group discussions, and observations remain largely the same because qualitative research is deeply human and descriptive, making it harder for technology to fully capture.

While quantitative research can tell you the “what,” “when,” “where,” and “how much,” qualitative research is essential for understanding the “why” and “how.” This blend of evolving technologies and traditional methods shapes the current landscape of data collection and insights. Therefore, clients often need help interpreting this data due to its volume and complexity. I act as a bridge between our product team and our clients to ensure the data is accurate and meaningful. For example, if an e-commerce company sends multiple emails about a single purchase—like confirmation, shipping updates, and delivery details—we make sure these emails are counted as one transaction, not several. This helps maintain clean, accurate data for understanding consumer behaviour.

Additionally, I explore new use cases for our data, especially in emerging areas like fintech. Although we currently focus on e-commerce, ride-hailing, and food delivery, I see a lot of potential in fintech. For instance, we can track spending and saving behaviours through data from digital banks and financial apps.

I ensure that our data collection complies with GDPR and CCPA regulations, making sure our methods are both legal and ethical. My goal is to help clients not only compete effectively but also gain a comprehensive understanding of their industries and consumers. I see my role as a knowledge provider, helping companies navigate and leverage the data landscape for strategic advantage.

How would AI offer secure data management at a global scale?

At Measurable AI, we leverage AI to manage the massive volume of receipts from our 3 million users worldwide. AI helps us process these receipts efficiently, enabling us to extract valuable insights while staying compliant with GDPR and CCPA regulations. This technology allows us to handle far more data than would be feasible manually.

However, AI can be a double-edged sword. For example, I recently experienced a situation where my credit card was compromised. A hacker used AI to guess my card details at scale. While it’s alarming, the good news is that AI was also used to detect and prevent this fraud. My bank’s AI system flagged an unusual transaction and blocked my card before significant damage occurred. This experience highlights both the power and risks of AI.

Regulation is crucial in managing these risks. Proper regulation ensures that AI is used ethically and responsibly. The EU is leading in this area, setting a benchmark for AI regulation that I believe is essential for safeguarding privacy and security. At Measurable AI, we ensure that our data collection methods are legal and that we never use personally identifiable information (PII). We focus on aggregated data to respect user privacy.

So, while AI offers tremendous data analysis and protection potential, its responsible use is critical. Regulation plays a key role in balancing its benefits and risks; adhering to these standards is vital for success in this field.

Do you always feel confident in gathering the information and forming a strategic direction for the business? If so, how did you build that confidence while overcoming fears and concerns?

In our industry, we’re not just selling data—we’re selling trust. Business leaders rely on our insights to make critical decisions, like setting discount strategies for e-commerce. These decisions can significantly impact their profits, so accuracy is crucial. We ensure data trustworthiness by sticking to solid math and stats principles. First, we need a sample size that is large enough. Data from 100 people isn’t as reliable as from 10,000 or more. The sample size should match the population size; for example, representing China requires a much bigger sample than Denmark.

Equally important is having a representative sample. The world is diverse, and we need data to reflect that diversity. If our sample mainly includes older individuals, it won’t accurately represent younger groups. We use techniques like weightage to correct for any gaps.

Understanding our stakeholders’ concerns is also vital. Many decisions are emotionally charged because they impact people’s jobs and business outcomes. I’ve learned that honesty and empathy are crucial. I’ve made mistakes in the past by not being fully transparent in trying to protect the integrity of our methods. But openness builds trust.

While I mostly work with Gen Z and millennial-driven companies, these principles of transparency and empathy apply everywhere. Balancing solid data practices with genuine, empathetic communication helps us build trust and deliver valuable insights.

Where do you see AI in the consumer world in the next 5-10 years?

AI is really changing the game when it comes to handling data and making recommendations. It can process and analyse information on a scale that’s beyond human capability. Take smart refrigerators, for instance. They can track your grocery items through manual input or sensors and even reorder products when you’re running low. That’s a neat example of how AI can work on a personal level.

AI is also a game-changer for businesses, especially e-commerce companies. It can predict what products people might buy, helping companies plan better, stock up efficiently, and manage deliveries. This predictive capability also reduces waste, such as FMCG companies that need to balance stock levels to avoid unsold goods.

Looking ahead, we’ll see AI’s role in improving convenience and personalisation for consumers. It’s not just about having the tech; it’s about how it can make experiences smoother and more tailored to individual needs.

However, balancing this with a strong focus on regulation and privacy is crucial. AI can offer incredible insights and enhance connections, but it needs to be managed responsibly to avoid privacy concerns. The aim is to use AI to replicate and enhance the human connection between brands and consumers, making interactions feel more personalised and meaningful. This balance between tech and personal touch will define AI’s future in consumer business.

How do you define success?

This conversation seems to be circling the importance of human connection, doesn’t it? While we’re discussing AI, it’s clear that human connections matter deeply to me. For instance, I’m moving to the Netherlands for my family, and having the flexibility to work remotely has been a huge benefit. To me, success isn’t just about financial gain—it’s about trust and respect from those around you because of the knowledge and support you provide.

I’ve made my share of mistakes, and I believe they’re invaluable teachers. These experiences have reinforced for me that human connection is crucial, especially as AI becomes more prevalent. AI can help us work more efficiently, which ideally means we’ll have more time to foster relationships—whether that’s through better team communication or mentorship.

In many organisations, people are so busy that they don’t have time to connect with other teams, which means missing out on the bigger picture. For example, someone in finance might not interact with HR and miss out on how their roles intersect. If AI can give us back some of that time, we might finally get to improve these essential connections. So, as I look at success, I see it in building and maintaining those meaningful connections.