Creator marketing has become a significant part of the digital advertising mix, but evaluating creators and attributing business outcomes remain difficult. Follower counts and engagement rates are easy to compare, yet they often say little about audience trust, creator-brand fit or whether a campaign actually influences behavior beyond the platform.
Those measurement problems become more complicated as campaigns span TikTok, Instagram, YouTube, marketplaces and social-commerce channels, particularly across Southeast Asia, where languages, platforms and consumer behavior vary considerably between markets. Brands are also contending with fake followers, engagement manipulation, AI-generated content and increasingly fragmented attribution.
Davidson Chua has experienced those challenges from both sides of the ecosystem. Before co-founding Singapore-based creator intelligence platform Influencees, he built Autosave, an automotive community that grew to more than 18,000 members, working with brands, marketing teams and PR agencies on campaigns and community activations. He is now CEO and co-founder of Influencees, which focuses on helping brands evaluate creators beyond basic audience-size metrics.
In this TNGlobal Q&A, Chua discusses what brands should measure beyond follower count, where creator-marketing attribution still breaks down, how companies can assess trust and creator-brand fit, and why Southeast Asia may require more flexible approaches to creator data and campaign measurement.
Creator marketing still often begins with follower count, even though audience size does not necessarily indicate influence, trust, or commercial impact. Which signals should brands prioritize instead when evaluating a creator, and how should those signals differ depending on the campaign objective?
Follower count is useful as a rough indicator of potential reach, but it should rarely be the first or only decision-making signal.
I would look at creators across four main areas: relevance, audience quality, consistency, and the ability to drive action. Relevance means whether the creator actually talks about the category and whether their audience expects that type of content from them. Audience quality includes the nature of comments, repeat interaction, and whether the community appears genuine. Consistency matters because one viral post can distort an otherwise weak track record.
The weighting should then change depending on the objective. For awareness, reach, views, and content resonance matter more. For traffic or consideration, click-through behavior and historical response to calls to action become more useful. For conversion, tracked links, codes, leads, or sales should carry more weight.
The mistake is trying to reduce every creator to one universal score. A creator who is excellent for awareness may not be the right person for conversion, and vice versa.
Attribution remains one of the hardest problems in creator marketing. What can brands reasonably measure today across awareness, engagement, traffic, consideration, and conversion, and where does the data still become too fragmented or indirect to support confident attribution?
Brands can measure more than they sometimes realize, but they need to be clear about what each metric actually proves.
Awareness can be measured through views, reach, and frequency, while engagement tells you whether people reacted to the content, but it does not necessarily mean they considered purchasing.
Traffic, on the other hand, is more concrete because links and UTM parameters can show whether someone took the next step. Conversion is strongest when a creator can be tied to a purchase, lead, redemption, or other measurable action.
The difficulty, however, comes in the middle. For instance, consideration is often indirect. Someone may see a creator today, search for the brand tomorrow, and buy through another channel a week later. That journey becomes fragmented across social platforms, browsers, marketplaces, and offline purchases.
So brands should avoid claiming certainty where there is none. Attribution works best when campaigns use a combination of direct signals and directional evidence rather than trying to force every outcome into last-click attribution.
Smaller and mid-sized creators are often described as delivering stronger engagement or community trust, but those advantages can be difficult to compare consistently. How should brands assess whether a smaller creator is genuinely influential within a niche rather than simply generating a high engagement rate on a small base?
A high engagement rate alone is not enough. On a small audience base, a few dozen interactions can produce a much more attractive percentage without necessarily representing real influence.
For instance, I’d look at the quality and repeatability of the interaction and ask questions such as: Are the same people returning? Are comments substantive or mostly generic reactions?
Another useful signal is whether the creator can move people beyond the platform. Even modest amounts of website traffic, event attendance, enquiries, community participation, or redemptions can demonstrate that the relationship goes beyond passive consumption.
For niche creators, though, depth can matter more than scale. Someone with 8,000 followers who is consistently trusted within a specialist community may be far more valuable to a relevant brand than a creator with 200,000 followers and little connection to that category.
The key is distinguishing audience activity from audience trust.
Your original pitch cited a four-week Creator Creates Series involving 20 creators, more than 1,700 community votes, and 2,300 link clicks. Without turning the discussion into a product case study, what did that campaign teach you about which metrics are useful, which can be misleading, and how brands should interpret participation or click data alongside actual business outcomes?
The biggest lesson was that no single metric tells the whole story.
In our Creator Creates Series, we tracked creator content alongside community votes, engagement, and unique link clicks. The campaign involved 19 creators and generated more than 1,700 community votes and 2,300 tracked link clicks over four weeks.
But each metric can also be misleading in isolation. A creator can generate strong engagement without meaningful traffic. Another may have fewer likes but drive a disproportionately high number of clicks.
The practical lesson for brands is to interpret metrics as a funnel rather than a leaderboard.
Views tell you exposure, engagement suggests resonance, clicks indicate intent, and commercial outcomes tell you whether that intent ultimately created business value.
That is also why campaign objectives should be defined before selecting creators, not after the results arrive.
Supporting campaign reference: Influencees Creator Creates Series 2026
Creator campaigns are often managed through spreadsheets, messaging apps, platform dashboards, agencies, and separate reporting tools. Where do the biggest information gaps or workflow failures typically occur, and which parts of the process most need common standards or better infrastructure?
The biggest problems usually happen at the handover points throughout the workflow.
A brief may begin in an email, move into a spreadsheet, be discussed with creators over several messaging apps, then have results copied manually from different social platforms into another report. Every transition creates an opportunity for information to be lost, duplicated, or interpreted differently.
I saw this first-hand while running Autosave and working with marketing teams and PR agencies. A relatively small team could be managing multiple brands, multiple campaigns, and many creators simultaneously, all of whom had different communication styles and reporting formats.
The industry would benefit from common standards around creator profiles, campaign objectives, deliverables, and reporting. Brands should be able to understand what was promised, what was delivered, and what happened afterwards without reconstructing the campaign from screenshots and spreadsheets.
The greatest opportunity is not necessarily replacing every existing tool, but creating a consistent information layer across the workflow.
Credibility is increasingly important as brands deal with fake followers, engagement manipulation, undisclosed sponsorships, AI-generated content, and inconsistent audience data. What forms of verification are useful without creating an overly restrictive system that disadvantages newer or smaller creators?
Verification should help establish confidence without turning creator marketing into a closed club.
I think brands should separate identity verification, audience verification, and performance verification. Identity verification answers whether the creator is who they say they are. Audience checks look for unusual growth patterns, suspicious engagement, or obvious manipulation. Performance verification asks whether historical campaign claims can be supported by platform data or other evidence.
Importantly, credibility should not mean requiring a creator to have years of commercial history. That would disproportionately disadvantage newer creators who may have highly relevant communities.
There is also a regulatory dimension. In Singapore, for instance, sponsored social-media content is already expected to comply with advertising disclosure rules, and the government has reported cases involving undisclosed paid influencer content.
The goal should be progressive trust. The more claims a creator makes about audience, performance, or commercial results, the more evidence should be available to support them.
One of the tools we already have is “Trust Check,” which brands and creators can use to check whether a video was generated by AI by pasting the video link into the system. We can then run it through our system to provide a breakdown of the AI model that was used, together with an AI similarity score.
How should brands think about creator-brand fit beyond demographic matching? For example, how important are community interaction, historical content, topic consistency, audience overlap, brand safety, and the creator’s ability to drive action, and which of these are hardest to quantify reliably?
Demographics are only one layer of fit.
Historical content is often more revealing because it shows what the creator genuinely talks about, what their audience expects from them, and whether a brand partnership would feel natural.
In my opinion, topic consistency matters for the same reason. Community interaction can indicate trust, while audience overlap helps brands avoid paying repeatedly to reach the same people through different creators.
Brand safety is important but should be contextual rather than purely keyword-based. A creator can mention controversial topics responsibly, while another creator can appear completely “safe” yet have an audience that is irrelevant to the campaign.
The hardest thing to quantify is probably trust. You can measure proxies such as comment quality, repeat engagement, clicks, and conversions, but trust itself is relational and contextual.
That is why creator evaluation should combine quantitative signals with qualitative judgment rather than pretending the entire decision can be automated into one number.
From what you are seeing in Singapore and the wider Southeast Asian market, what is different about building creator-marketing infrastructure here compared with larger markets? Are language, platform fragmentation, market size, local communities, cross-border campaigns, or uneven data access creating distinct measurement challenges?
Southeast Asia is especially interesting because it is not one homogeneous creator market.
Singapore is small but commercially sophisticated, while neighboring markets can be much larger and behave very differently in terms of language, platforms, creator pricing, and commerce behavior. A campaign that works in Singapore may require a completely different creator mix and measurement approach in Indonesia, Vietnam, or Thailand.
Platform fragmentation also matters. Creator activity is spread across TikTok, Instagram, YouTube, marketplaces, livestreaming platforms, and increasingly social-commerce environments, and the available data is not always consistent between them.
At the same time, the opportunity is growing. Singapore’s influencer advertising spend reached about US$119 million in 2025, according to We Are Social’s Digital 2026 Singapore report.
More broadly, APAC influencer marketing is increasingly being treated as an outcome-driven channel rather than only an awareness tool.
For Southeast Asia, good infrastructure therefore needs to be flexible enough to account for local context while still creating common ways to compare creators and campaign outcomes across markets.
Davidson Chua is the CEO and co-founder of Influencees, a Singapore-based creator intelligence platform helping brands discover, evaluate, and understand creators beyond follower counts. Before Influencees, Davidson built Autosave, an automotive community that grew to more than 18,000 members, working closely with brands, marketing teams, and PR agencies across campaigns and community activations. He graduated from the National University of Singapore with a BSc in Business Analytics.
Editor’s note: This Q&A has been lightly edited for clarity and style. The substance of the interviewee’s responses has been preserved.
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