Beyond Demographics: Philip Tiongson on Data-Driven, Hyper-Personalized Marketing

For decades, marketers have defined audiences using familiar categories: age, gender, income, location, occupation, and socioeconomic class. These categories remain useful. But they don’t tell the whole story.

Two people of the same age and income can have completely different interests, motivations, media habits, and reasons for buying. Treating them as the same customer simply because they belong to the same demographic group can leave marketers working with an incomplete picture.

In this episode of DigitalPrimer, Ibarra Villaseran speaks with Philip Tiongson about what happens when marketers go beyond demographics.

Drawing from a career spanning traditional media, digital strategy, analytics, and marketing science, Philip makes the case for understanding audiences as people first. The conversation covers the evolution of digital measurement, the growing amount of customer data available to marketers, mobile behaviour, hyper-personalization, artificial intelligence, and why better technology makes strategic thinking more important rather than less.

From Traditional Media to Marketing Analytics

Philips’ career began in advertising in the mid-1990s as a media strategist, with McDonald’s as his first account.

It was a different advertising environment. Television, outdoor advertising, celebrity endorsements, and other traditional channels dominated media planning. One of his earliest campaigns involved McDonald’s and Sharon Cuneta.

His career later took him to Vietnam, where he headed the media department of McCann Erickson and worked with major FMCG clients including Nestlé and Coca-Cola.

The bigger change came after moving to Singapore.

There, he moved into consulting and eventually analytics, marketing science, digital media planning, and research. It required what he describes as a “mind shift.”

The familiar ways of measuring advertising were changing.

When Digital Was Just Another Medium

In digital advertising’s earlier years, marketers initially tried to understand it using the frameworks they already knew.

Television, radio, print, and outdoor advertising had established measures such as reach and frequency. When digital entered the media mix, those same ideas were initially applied to the new medium.

Then digital began producing something traditional media couldn’t provide at the same level: granular measurement.

Marketers could increasingly understand what happened after an advertisement was served. Clicks became measurable. Conversions could be tracked. Eventually, advertising could be evaluated against cost per acquisition and even revenue generated.

Digital became more measurable.

And with that came greater accountability.

Instead of asking only how many people may have seen an advertisement, marketers could begin asking what happened because someone saw it.

Social Media Added Another Layer

Social media complicated the picture further.

Before its rise, concepts such as sentiment tracking, social mentions, and share of social conversation weren’t standard parts of the measurement analyst’s vocabulary.

Suddenly, marketers had another source of information about how people responded to businesses, products, campaigns, and ideas.

The result was more data, but also more dimensions through which audiences could be understood.

That evolution sets up Philip’s central argument: Marketers should stop relying on demographics alone.

Demographics Show Only Part of the Person

Philip’s PANA presentation was titled Beyond Demographics.

The central idea is straightforward.

An audience described as “ABC1, female, 18 to 25” provides a marketer with some useful information. But it remains difficult to imagine the actual person behind those characteristics.

What does she care about?

What does she do in her free time?

What motivates her?

What does she want?

What problems is she trying to solve?

What communities does she belong to?

What does she watch, read, play, or listen to?

Once those questions are answered, the customer becomes easier to understand—and therefore easier to communicate with.

The Bigger Picture Matters

Ibarra recalls a visual Philip used during his presentation.

Initially, the audience was shown only a small part of an image. Based on that limited view, people could make assumptions about what they were seeing.

Then the image widened.

Suddenly, the context changed.

He used the exercise as an analogy for demographic targeting. Demographics aren’t necessarily wrong. They’re simply one part of a much larger picture.

Adding interests, attitudes, behaviours, motivations, and other characteristics gives marketers more context.

That context can lead to better media decisions and better creative work.

Consumers Have Become More Complicated

Part of the challenge is that consumer interests have become increasingly varied.

Filipino consumers are connected to global culture through entertainment, news, social platforms, and online communities. Something happening in another market can quickly become relevant locally.

Philip points to K-pop as one example.

Even describing someone as a “K-pop fan” only gets you so far. Within K-pop are numerous artists, communities, fandoms, behaviours, and interests.

One broad category can contain many smaller groups.

The implication for marketers is important.

A mass audience may still exist statistically, but that doesn’t mean everyone within it thinks or behaves the same way.

Marketers Have More Data Than Ever

Fortunately, marketers also have more ways to understand those differences.

Businesses generate information through websites, search behaviour, digital advertising, social media, and other customer interactions. Agencies have access to their own research and media data. Companies may also hold brand health studies, usage and attitude research, customer information, and other first-party data.

Philip argues that the challenge is increasingly about bringing these sources together.

The individual datasets provide pieces of information.

Combined thoughtfully, they can produce a more complete understanding of the audience.

The opportunity isn’t simply to accumulate more data.

It’s to make better sense of what is already available.

Your Phone Says More About You Than Your Age

The conversation then moves to mobile behaviour.

Philip proposes a simple way of thinking about it: look at the applications someone uses.

A person’s phone can provide clues about their interests and daily behaviour. Someone who regularly uses fitness applications may have different priorities from someone who spends significant time playing mobile games. Entertainment, productivity, finance, travel, shopping, and social applications can each provide signals about interests and behaviours.

Villaseran compares this to the old exercise of asking, “What’s in your bag?”

The contents tell you something about the person.

Today, the applications on a smartphone can provide another window.

From a marketing perspective, this creates opportunities to build audiences around behaviours and interests rather than relying exclusively on demographic assumptions.

From Mass Audiences to Audiences of One

For Philip, the direction this is moving toward is hyper-personalization.

In its most developed form, personalization moves marketing closer to an audience of one.

Instead of creating one message for a broad demographic category, technology makes it increasingly possible to adapt advertising according to the interests, behaviours, and needs of smaller groups—and eventually individuals.

AI accelerates that possibility.

Platforms are increasingly able to automate parts of creative production, targeting, media delivery, and measurement based on the objectives provided by advertisers.

That can make sophisticated advertising capabilities accessible to far more businesses.

But accessibility introduces another question.

If nearly anyone can use these tools, what happens to the role of the marketer or agency?

Better Tools Don’t Remove the Need for Strategy

Philip’s answer is one of the more useful observations in the episode. Technology can democratize execution. It can make creative production easier. It can automate media buying. It can identify audiences and distribute advertisements.

What it cannot automatically provide is sound strategic thinking. Someone still needs to ask:

• Who are the customers?
• What do they need?
• What is the business trying to accomplish?
• What should the brand say?
• What information should guide the campaign?
• How should the results be interpreted?
• What should happen next?

For him, this changes rather than eliminates the role of agencies. As execution becomes increasingly automated, agencies need to become better strategists, planners, and interpreters of audiences.

Knowing how to operate the platform becomes less differentiating. Knowing what to ask it to do becomes more important.

With Better Targeting Comes Greater Responsibility

More precise targeting also creates responsibilities. Philip raises privacy as one concern. The ability to understand and reach people more precisely shouldn’t automatically mean every available capability should be used without consideration.

There is also responsibility toward the businesses paying for the campaigns. If technology makes advertising easier to execute, marketers still have an obligation to use budgets thoughtfully and make decisions that support the client’s or employer’s objectives.

This is where strategy becomes a form of responsibility. The platform can execute bu the marketer still has to exercise judgment.

Start by Thinking About Customers as People

Asked for practical advice, Philip returns to his main point. The first step is to move beyond age, income, socioeconomic class, and occupation. Instead, think about customers as people with: interests, motivations, needs, wants, hobbies, behaviours, and lifestyles.

This gives marketers more material for both targeting and communication. Consider the difference between these two audience descriptions:

ABC1, women aged 25 to 34.

And:

Young professionals who spend weekends running, regularly use fitness apps, follow local running communities, and are preparing for their first half marathon.

Both descriptions may refer to some of the same people. But the second gives the marketer much more to work with. It suggests possible messages, content, partnerships, media environments, search behaviour, and customer needs.

That’s the practical value of going beyond demographics.

Combine What the Business Knows With What the Agency Knows

His second recommendation is collaboration around data. Clients hold information their agencies may not have. Agencies also have access to research, campaign performance, search behaviour, media data, and other signals that may not exist within the client’s internal systems.

Instead of looking at these sources separately, he recommends bringing them together to develop a richer picture of the audience. This requires cooperation between clients and their creative, media, search, and digital partners.

Again, more data isn’t necessarily the objective. Better understanding is.

Don’t Assume. Test.

His third recommendation is particularly practical: Test the idea.

Marketers don’t have to accept the argument for interest-based targeting on theory alone. Platforms such as Meta and Google make it possible to compare different targeting approaches.

A business could run a broader campaign alongside one built around specific audience interests and compare the results:

• Which audience engaged?
• Which converted?
• Which produced better-quality leads?
• Which cost more?
• Which assumptions turned out to be wrong?

The answers come from running the test. This leads to another theme shared by both speakers: iterative learning.

Better Marketing Comes From Iteration

Large companies can sometimes become cautious because there is more to protect: established brands, larger budgets, internal processes, and years of brand history.

Smaller businesses may have an advantage here. They can often test quickly, see what happens, adjust, and try again. That willingness to learn through action is useful regardless of company size.

The important distinction is between random experimentation and structured testing:

• Start with an assumption.
• Test it.
• Measure what happened.
• Understand why.
• Adjust the next campaign.
• Repeat.

Philip describes this as an iterative learning process. It’s a useful way to think about modern digital marketing more broadly.

Data Should Help Marketers Understand People Better

Philip is an analytics and marketing science practitioner arguing for more sophisticated use of data. Yet his central message isn’t: Follow the data. It’s closer to: Use the data to understand people. That distinction matters.

The objective isn’t to reduce customers to increasingly precise datasets. It’s to use available information to develop a fuller picture of the people behind those numbers. Demographics provide one part, behaviour provides another.

Search patterns provide another. Interests, attitudes, motivations, and media habits add more. Together, they help marketers make better-informed decisions.

Hyper-Personalization Still Needs Human Judgment

AI and automation will continue making personalization easier.

Campaign creation will become faster. Platforms will handle more decisions automatically. Businesses with smaller teams will gain access to capabilities once reserved for organisations with large agencies and media budgets.

But that doesn’t remove the marketer from the equation. It changes where the marketer adds value. When execution becomes easier, judgment becomes more important.

Marketers need to understand which audience matters, what problem needs solving, what message is appropriate, what data deserves attention, and whether the campaign is actually contributing to the business objective.

Technology can help answer how to reach someone. Strategy still needs to answer why.

The Takeaway

The conversation begins with demographics but ultimately makes a broader point about modern marketing. Customers are easier to measure than ever, yet harder to generalize. Marketers now have access to more behavioural data, audience signals, sophisticated targeting, and AI-powered tools, creating more opportunities to understand people and communicate with greater relevance.

Making good use of these capabilities still requires discipline. Marketers need to look beyond age, income, and socioeconomic class to understand the interests, motivations, behaviours, needs, and lifestyles that shape customer decisions. Bringing different sources of data together can provide a fuller picture, while testing allows assumptions to be measured rather than accepted as fact.

As targeting becomes more precise and campaign execution becomes increasingly automated, strategic judgment remains important. Technology can help marketers identify and reach audiences more efficiently, but understanding who matters, what to communicate, and why still requires thoughtful decision-making. Hyper-personalization may be where marketing is headed, but it begins with a basic principle: understand the person behind the data.