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How much is a like from a friend really worth? We asked 2,568 consumers for you

(This article is written by Growth Black Box, and published by Titanium Media with authorization)

By Growth Black Box Growthbox

Interaction reflects the depth of a brand's influence more than mere exposure, and this has become an industry consensus. Consumers seeing your advertisement and consumers identifying with your brand are two completely different things.

Under this logic, page views are no longer the sole standard; likes have become the most commonly used 'golden metric' in brand marketing. Open any campaign review report, and you'll see the number of likes/interaction rates neatly listed in the same table to evaluate content performance, compare delivery efficiency, and guide budget allocation for the next phase.

However, a key question arises: Can likes from different platforms really be compared when placed in the same table?

From our actual interactions with brands during seeding and content placement, only a few top-tier companies can establish clear platform-specific measurement mechanisms or even distinctly separate operational teams for different platforms at the organizational level. The vast majority of brands still place interaction metrics from all platforms in the same table without establishing differentiated value assessment standards.

For example, under the same theme, content on Platform A receives 100,000 likes, Platform B gets 50,000 likes, and Platform C only garners 5,000 likes. Faced with this data, many people instinctively think: 'Platform C’s performance is average; we should cut its budget next time.'

In reality, the gap between likes is much wider than we assume.

In 2024, a study by the McCombs School of Business at the University of Texas at Austin tracked over 710,000 social ad impressions, observing one variable: how the 'likes' displayed below ads affect user behavior differently depending on whether they come from users’ friends or strangers [1].

The results were surprising. For content-based ads, each additional like from a friend increases the probability of a user clicking on the ad by 21%; whereas every 100 additional likes from strangers only boosts click probability by 3%. To put it simply: one like from a friend influences user behavior approximately as much as 700 likes from strangers.

Even for hard-sell conversion ads, when users share similar interests with the friends who liked the ad, a friend’s like can increase click intent by 180%.

From the user perspective, on some platforms, liking is almost an unconscious action: double-tap the screen upon seeing interesting content, taking less than a second. On other platforms, before pressing that 'like' button, users may pause to consider: Will my friends see that I liked this content? Am I willing to let them know?

The same number of likes corresponds to significantly different user decision costs and subsequent impacts on brand behavior across different platforms.

Until now, few have systematically answered this question: What exactly are users expressing when they hit 'like' on different platforms? How does it differently affect brand perception? And how can brands earn more genuine, valuable likes?

Therefore, we focused on the consumer goods industry and conducted a cross-platform survey of 2,568 users who are active on multiple mainstream social platforms.

One set of data from the WeChat ecosystem caught our attention first: very similar to research findings from UT Austin's business school, a friend’s like has a stronger impact on brands.

In the consumer goods sector, after users see content liked by friends on WeChat, over half tend to identify more with the opinions expressed in the content, nearly half will actively click to watch it carefully, and over a third believe the content is more reliable as a result. More notably, 22% of users will continue to share it with friends, group chats, or their Moments feed. A friend’s single like doesn’t just make the user “see” the content—it activates a complete behavioral chain from trust to diffusion, with inherent viral potential.

When the content liked by friends involves a brand or product, this behavioral chain further extends to brand decision-making. Fifty-three percent of users develop a stronger impression of the brand, 41% will prioritize the brand, and 38% will actually try or purchase it. A lightweight 'like,' backed by a friend’s social endorsement, drives the entire brand decision path, from impression reinforcement to actual purchase.

The influence of friend likes on brands is already clear. But is this influence equally strong for everyone? When we further segment the demographics, an interesting generational difference emerges: younger people are the core group that “falls into the pit” because of likes. Those born after 2000 discover brands through likes, those born in the 1990s form deep attachments to them, while those born in the 1980s or earlier reinforce existing preferences through likes.

Influenced by friends' likes, users of different age groups establish relationships with brands in markedly different ways. Generation Z shows a much higher tendency to follow brand accounts (TGI 130), and their long-term attention to brand information is also prominent (TGI 114). Millennials are the main force turning likes into deep brand relationships, showing strong performance in following brand accounts (TGI 116) and becoming brand members (TGI 114). For those born in the 80s and above, they mostly stay at the level of content resonance and interaction, but brand impression reinforcement and continued repurchases still occur.

Although every social platform has a 'like' feature, likes on different platforms represent completely different user psychologies.

On some platforms, a like serves as immediate feedback to algorithms – “I find this interesting,” forgotten right after clicking. On other platforms, a like is roughly equivalent to bookmarking – “I want to save it for later,” aimed at oneself. Within familiar social ecosystems, every heart-shaped like can be seen by friends, along with your real avatar and name. It is not a lightweight interactive gesture, but rather a public statement towards one’s social circle – “I endorse this content, and I am willing for you all to know.”

When a like carries a user's real identity and social connections, its meaning evolves from “I like” to “I approve,” or even equates to “I recommend this to my friends.” For brands, this means that each like is no longer just an engagement metric, but a trust endorsement perceivable within friend circles. This is why likes truly influence brand perception and deserve careful evaluation.

It is precisely this difference that causes the same 'like' to generate significant divergence in brand perception value across different platforms.

Next, we will delve into the reasons behind these differences from two dimensions: platform ecosystem and user behavior, as well as what it means for brands.

Budget allocation between different platforms often becomes the most agonizing issue for brands. The underlying assumption here is that all platforms are competing for the same user demand, and brands only need to bet on the “most efficient” one. However, our research shows that over 80% of users simultaneously use two or more platforms within the last three months, and nearly 40% use three platforms at the same time (37%). The most common combination of two platforms is “WeChat + Douyin” (32%), while the combination excluding WeChat, “Xiaohongshu + Douyin,” accounts for only 6%.

More interestingly, the growth of the three platforms is not a zero-sum game. In the past year, the proportion of increased usage frequency of Video Accounts led among the four platforms (51%), but Xiaohongshu and Douyin also grew simultaneously. Meanwhile, 45% of users stated that the content they watch on Official Accounts and Video Accounts is completely different — instant short-video promotions coexist with in-depth graphical analyses, each finding its place within the WeChat ecosystem, allowing brands to address different user needs with two types of content formats.

Users have solved this puzzle for the brands, and the answer is to choose all options. Users have assigned differentiated scenarios and objectives to each platform. This is not a zero-sum game; rather, it resembles a clearly divided content consumption ecosystem. However, as the national social application, WeChat has become an irreplaceable foundational pillar — by 2025, its global combined monthly active users will reach 1.418 billion, growing 2% year-on-year, while total user time spent on Video Accounts has increased by over 20% year-on-year.

So, what roles do the three platforms play in terms of user scenario positioning?

We asked users to evaluate the content experience of the three platforms. The results show that user habits on the three platforms are clearly differentiated: Douyin's core labels are personalization-driven and entertainment stress relief, Xiaohongshu focuses on interest exploration and practical insights, while WeChat points towards in-depth content and information authority ('Following hot topics' ranked first, 'Profound and inspiring' received the highest recognition across the three platforms) — users come with higher information expectations, and brand content naturally finds itself in a 'taken seriously' reading context.

The three scenarios correspond to three types of content demands, with users looking for different things on each platform. Facing users who want 'everything,' ignoring any platform would leave gaps in the user journey for brands. The real question is how to develop differentiated strategies based on the characteristics of each platform.

Each of the three platforms has its role — but why do users come to WeChat, a social domain, to consume content? How do the triggers differ from those of other platforms?

We surveyed the scenarios that triggered users to engage with WeChat Official Accounts and Video Accounts. The top trigger was not algorithmic recommendations but social drivers — 51% of users were prompted to watch due to 'seeing friends liking' content, and an equal proportion entered because of 'friends or group chat shares.' Active revisiting ranks second (49%), and algorithmic recommendations rank third (46%).

The appeal of WeChat content to users lies primarily in social interactions and revisits, followed by algorithmic distribution. Another notable driving factor is the validation scenario: 43% of users, after seeing content on Douyin or Xiaohongshu, specifically come to WeChat to search and verify.

This further illustrates that, in order to meet users' actual demand for 'having it all,' brands need to place greater emphasis on the WeChat ecosystem in their full-domain marketing—not merely because of WeChat's large user base, but due to the differentiated mindset of its users.

For instance, after seeing a new smartphone recommendation on another platform, users return to WeChat to search for official accounts offering professional reviews and in-depth analysis; after encountering an advertisement for a robotic vacuum cleaner, they come to WeChat to search for the brand’s official account to learn about detailed configurations and promotional activities. WeChat is currently the only platform that simultaneously hosts certified official brand accounts and social relationship chains—users can not only find officially certified brand information sources but also see genuine interactions from friends around them. This dual endorsement of 'official + social' holds distinctive value.

More interestingly, there are generational differences: the TGI (Target Group Index) for cross-platform verification behavior among post-2000s users reaches 113, while for post-1990s users, it is 106—the youngest user groups are actually the most inclined to 'verify on WeChat,' challenging the stereotype that 'WeChat is a content platform for middle-aged and elderly users.'

Liking on any platform is designed to have a low barrier and be easy to operate, seemingly just a casual action. But do users like for the same reasons?

We conducted a cross-platform comparison of the motivations behind likes on mainstream platforms. Considering that both thumbs-up and heart-like interactions coexist within the WeChat ecosystem, we analyzed them separately as independent types, resulting in four sets of data for parallel comparison. A noteworthy phenomenon emerged: the likes on mainstream platforms can essentially be divided into two major categories.

The motivational structures behind liking on Xiaohongshu, TikTok, and the WeChat thumb-up are highly similar, all belonging to content-driven likes. The top reason for liking is consistently 'support/appreciation/endorsement of content'; when it comes to functional dimensions such as 'bookmarking/convenience for rewatching,' 'supporting creators,' 'increasing exposure for quality content,' or 'informing the system of preferences,' the three platforms differ slightly in focus but remain largely at the same level overall. Xiaohongshu leads in 'bookmarking,' TikTok stands out in 'informing the system of preferences,' and the WeChat thumb-up excels in 'support/appreciation.' Essentially, these platforms play the same role: providing feedback on content itself, whether it’s 'useful, mark it' or 'good, recommend it to more people.'

In the motivations behind using the WeChat heart-like feature, an option absent from the other three types of likes appeared: 50% of users chose 'establishing unobtrusive interactions with specific circles and expressing opinions or shaping personal image outwardly,' making it the second-largest driver for liking, second only to appreciation of the content itself. This is because expressions hold significance only in an environment where likes are visible to real-name friends.

The act of 'liking' across all platforms isn’t uniform; the friend-visible heart-like feature independently forms a new category—while responding to content, users also make a real-name statement to their social circle.

We have already discovered that there is a fundamental difference in the motivational structure between friend-visible likes and regular likes. However, what are users' specific psychological states and decision-making processes when facing these two types of likes?

The WeChat ecosystem provides us with an ideal observation window — it is currently the only mainstream platform that simultaneously has friend-visible likes (Heart Likes) and regular likes (Thumbs-Up). The same group of users, the same content ecosystem, and two parallel interaction methods form a natural controlled experiment.

Every click on a Heart Like appears in friends' information feeds in real-name form. This means that before clicking, users will make an additional judgment: 'Am I willing to let my friends see that I endorse this?' An intuitive clue is the usage frequency. On platforms like TikTok and Xiaohongshu, liking is the most frequently used interaction behavior; but on WeChat, the usage frequency of Heart Likes ranks only third — because social visibility raises the decision threshold for each like, making every like carry the value of social endorsement.

Thumbs-Up, however, does not appear in friends' information feeds, so the user’s decision becomes much simpler: 'Is this content useful to me?' When users find the content good but not worth showing to friends, Thumbs-Up provides an outlet for quiet recognition. Whether for saving or supporting the author, the motivations align closely with those of other platforms.

The dual-like design also makes WeChat the only content ecosystem that can distinguish between 'private domain recognition' and 'public domain recognition.'

Users aren’t just choosing whether to 'like or not,' but also deciding 'whether others should know I liked it.' For brands, behind every Heart Like is a user who has gone through this screening process and actively provided a public endorsement of the brand's content.

Each click on a WeChat Heart Like carries a social cost — users think twice before expressing their stance. But conversely, when a user actually presses the Heart Like, what does this action accomplish in their social life?

Let’s use a real-world scenario as an example.

Given that the types of likes vary, when users see high-like or high-heart content across different platforms, will their subsequent thoughts and actions differ? In fact, the top two ranked responses remain consistent: greater agreement with the content, and more attentive viewing. Differences only start to emerge from the third position.

On Douyin, the most easily triggered follow-up action for highly-liked content is 'liking' (ranked 3rd), significantly higher than on other platforms. High likes on Douyin create an immediate positive feedback loop: likes generate more likes, comments prompt more comments, aligning with Douyin’s algorithm-driven, personalized, and immersive platform characteristics.

On Xiaohongshu, the most easily triggered follow-up action for highly-liked content is 'saving' (ranked 3rd), one position higher than on other platforms. This also aligns with Xiaohongshu's self-positioning as a 'lifestyle guide' and 'interest-based community': users come here to find answers, and high likes validate the reliability of the answers, making saving a natural next step.

For WeChat, the most easily triggered follow-up action for high-heart likes is 'greater recognition of content authenticity/credibility' (ranked 3rd), leading Douyin by two positions and Xiaohongshu by one. Consistent with earlier survey results, WeChat content acts as a 'cross-platform trust anchor' in users' minds.

Additionally, WeChat heart likes trigger a unique behavior, namely 'curiosity about friends’ motivations for liking' (ranked 4th). When users see familiar friends liking certain content, a natural psychological response is triggered: do they usually pay attention to this topic? What’s special about this content? Users’ attention shifts from 'friends’ choices' to 'the content itself,' starting with curiosity about people before developing interest in the content. This person-to-content transmission path is precisely driven by social visibility.

Naturally, the intention to 'share with friends' ranks two positions higher than on Douyin and Xiaohongshu. On WeChat, users are more inclined to share high-heart like content with friends, who may then continue to like and share it, creating social circle layering effects.

What type of content do users like to upvote on different platforms? The lifestyle category is the only one with significant advantages across all four platforms, and the varying combinations of strengths on each platform reveal distinct characteristics of their content ecosystems.

Knowing which categories are more likely to get likes raises a more critical question: what specific content traits are more likely to drive users to press that like button?

A common foundation across the four platforms is that 'learning new knowledge or skills' serves as a fundamental element for attracting likes. Differences lie in what else is needed beyond this sense of gain.

Given the unique format of long-form text and images on WeChat Official Accounts, we have set some indicators that differ from those for video formats for users to choose from.

The 'likes' on different platforms indeed cannot be compared on the same table.

Over the past few years, the core metrics brands pursue in digital marketing have become increasingly homogenized: lower CPE, more likes. Algorithms have grown smarter, and the efficiency problem of attention allocation has been largely solved. However, machines cannot address a more fundamental issue: are users truly willing to incorporate their recognition of a brand into their social identity?

This research reveals three completely different user expressions behind the same 'like.' Instant emotional feedback aimed at algorithms, personal bookmarking for oneself, and public identity statements within social circles. Their value to brands is not a matter of 'more or less,' but rather a difference in 'dimensions.'

The heart-shaped like visible to friends may be the most underestimated dimension.

When a user is willing to 'endorse' a piece of content in front of friends with their real name, this action becomes more than just an ordinary interaction; it represents an expenditure of social credit. Its scarcity stems precisely from its high threshold: because every heart-shaped like can be seen by friends, users take an extra second to consider before pressing it. And this moment of hesitation is the true measure of a brand’s deep resonance.

For brands, this means that the metrics for content strategy may need an upgrade. 'Analyzing data by platform' is a necessary first step, but the more critical question is: what level of user recognition does each interaction we gain actually reflect?

When brands start evaluating content based on whether it's 'worth being endorsed by users,' decisions about what to focus on, what to invest in, and what deserves amplification will naturally become clearer.

In an era of attention surplus, being seen is no longer scarce. What holds more value is when a real person is willing to vouch for you with their social credibility.

Appendix —— Explanation of Research Methods and Sample

Glossary

• TGI Definition: TGI = (proportion of a group with a specific characteristic in the target population / proportion of the group with the same characteristic in the overall population) × 100. A TGI > 100 indicates that the group’s preference for the characteristic is above average; greater than 110 indicates significantly higher than average.

• Definition of High-tier/Lower-tier Cities: High-tier cities refer to first- and second-tier cities; lower-tier cities refer to third-tier cities and below.

Reference：

[1] Lee, Agarwal & Whinston, “The Role of Peer Influence in Churn in Online Social Advertising”, Information Systems Research, 2024.

[2] Tencent's 2025 annual earnings announcement (released on March 18, 2026). Total annual revenue reached 751.8 billion yuan, a year-on-year increase of 14%; marketing services revenue for the full year was 145 billion yuan, up 19% year-on-year; Q4 marketing services revenue was 41.1 billion yuan, a year-on-year increase of 17%. Total user time spent on video accounts grew by over 20% year-on-year. Combined MAU of WeChat and WeChat reached 1.418 billion.

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