VMEG: What They Got Right to Generate 1M+ Impressions with Just $58

Fay leads influencer marketing at VMEG.AI. In her view, the team chose to focus on AI video translation early on not because the category felt new, but because it was specific and grounded in a real need.

“Video is now the primary way information moves around the world, but language is still the biggest barrier,” Fay said. “We saw a huge amount of high quality content that could not reach a global audience because of language limitations.”

That was the problem VMEG.AI set out to solve from the beginning.

Its target users were clearly defined: creators looking to globalize their content with lower cost and higher efficiency, cross-border brands expanding overseas, and organizations in entertainment, education, and related sectors.

Even in an already crowded AI video category, VMEG.AI has never positioned itself as just another tool focused on generation efficiency. What the team emphasizes more is the balance between technical depth and creative intuition. They bring strong underlying algorithm capabilities, while also understanding the rhythm, tone, and creative logic of content itself.

At the product level, the team cares about more than speed. They also care about whether the output feels natural, whether it is closer to true native expression, and whether it can genuinely fit into a creator’s workflow.

After validating PMF, VMEG.AI began treating influencer marketing as a much more central growth channel

During the product refinement stage, the team noticed that creators in certain verticals had already started using VMEG.AI on their own to distribute content across different language markets, and were seeing stronger than expected traffic results. For the team, signals like these were far more convincing than a one-off campaign, because they showed that video translation was not just a conceptual need. It was a tool creators were actively bringing into real growth scenarios.

Fay said it was exactly this moment, when users started adopting the product on their own, that gave the team confidence the product was ready to scale.

But having a product that works and having a growth engine that can scale quickly are two different things.

To this day, VMEG.AI still invests in SEO, SEM, social media content, and influencer marketing at the same time. But internally, these channels have never been treated as separate.

Fay made that point very clearly: “Search and influencer marketing do not operate in isolation. They reinforce each other. Influencer marketing drives more branded search exposure, and when branded search volume and clicks increase, that also creates positive feedback for SEO rankings and SEM performance.”

That is also why, in terms of resource allocation, VMEG.AI has consistently placed influencers in a very central role.

For AI products, the two most important values of influencer marketing are trust building and scenario reconstruction.

How VMEG.AI understands influencer marketing largely shaped the rest of its playbook.

Fay put it very directly. For products like this, what really drives conversion is usually something more concrete and immediate: whether influencers can create a wider range of use cases, introduce different ways of using the product, explain it in natural and localized language, generate real engagement in the comments, and respond sincerely on their own.

When all of that happens, influencer content also feeds back into the brand itself and strengthens trust in the brand.

That also explains why VMEG.AI does not prefer short embedded ad formats. Rather than forcing the product into an influencer’s existing video, the team would much rather see a dedicated review or tutorial, because users care more about how the product actually works.

Before using AhaCreator, the biggest challenges were information opacity and heavy communication overhead.

Before adopting AhaCreator, VMEG.AI mainly relied on its in-house team to manually search for influencers and handle outreach. The team also used some third-party plug-in tools and occasionally worked with traditional agencies.

For many teams, these are the most common paths. But Fay quickly realized that once influencer marketing needs to run continuously and at scale, the problems become much more obvious.

One of the core issues was the black box nature of traditional agencies.

Fay said that many agencies push collaborations as packaged bundles, offering a list of 100 influencers at a time, but the quality of those lists varies significantly, and brands have limited room to evaluate or make informed decisions. More troubling, the team also identified cases where some influencers appeared to be inflating performance metrics together, while still quoting unreasonable prices.

A different problem came from managing everything manually in-house. From sourcing and screening influencers, to sending emails, waiting for replies, and negotiating rates back and forth, the entire process was extremely time-consuming. It was manageable when the collaboration volume was small, but once the team wanted to scale consistently, they realized a huge amount of time was being trapped in repetitive operational work.

After comparing different options, Fay felt that what made AhaCreator the best fit was not any single feature, but the fact that it broke apart the traditional agency black box model. That transparency is not an abstract benefit. It shows up in specific parts of the workflow.

1. Matching and screening

In the past, whether the team was manually sourcing from scratch or waiting for agency lists, the front end of the process was always heavy. Now AhaCreator pushes matching and outreach much further forward, and VMEG.AI can evaluate potential partners based on standards that have become increasingly clear internally. That means the team no longer has to start from the most labor-intensive part of the workflow, while still keeping the final decision-making power in-house.

2. Outreach and rate negotiation

This is one of the areas where Fay felt the difference most clearly.

“AhaCreator’s influencer matching and automated outreach system has significantly reduced the amount of manual work on our side. In the past, we might only be able to close a few collaborations a month. Now, with systematic funnel management, both our outreach coverage and execution efficiency have increased several times over.”

For the team, rate negotiation is not just about convenience either. The platform helps establish more reasonable pricing ranges based on factors such as the influencer’s market, follower count, and average views, which cuts down a lot of the back and forth and guesswork. In the past, the team had to slowly feel out the pricing range through repeated email exchanges. Now that process is much smoother.

3. A much shorter collaboration cycle

Fay said that, compared with before, the biggest improvement has been the time from screening to initial intent alignment. That stage used to be full of fragmented, time-consuming tasks. Now the team can finally shift more of its energy back to content strategy and creative development.

As a result, they also achieved strong outcomes.

One million impressions for $58, and a clearer influencer playbook of their own

Fay said the team also went through a process of moving from a broad outreach approach to much more refined alignment. The more collaborations they ran, the more clearly they realized that what was worth accumulating was not simply getting a large number of influencers to post once, but gradually narrowing in on the right combination of influencer profile, platform, content format, and brief.

That methodology became clearer in large part because of the actual results they achieved on AhaCreator.

So far, VMEG.AI has worked with 40 mid-tier and long-tail influencers through AhaCreator, generating more than 2 million impressions, with an average of more than 50,000 impressions per influencer and CPC as low as $0.59.

Among them, one Instagram influencer generated 1 million impressions with a collaboration cost of just $58.

The video itself was very straightforward.

In the first five seconds, the creator quickly demonstrates themselves “speaking” Korean, Arabic, English, Chinese, and other languages. Then the video adds a hook along the lines of, “If you want the same result,” before moving into a more detailed tutorial on how to use VMEG.

For the team, the most valuable part of these results is not just that the numbers performed well. They also made it easier to see which kinds of influencers were worth continuing to work with, which content structures were more likely to drive search and clicks, and which platforms were better suited to conversion.

AhaCreator also made those signals increasingly visible. As more collaborations accumulated, VMEG.AI gradually built a clearer influencer methodology on the platform that better fit its own needs.

1. Prioritize content native fit, with a stronger preference for mid tier and long tail influencers

“We do not focus on follower count. We care more about whether the influencer’s content feels native and whether they have real audience stickiness,” Fay said.

In the AI tools category, the biggest account is not always the one that drives the best results. What matters more is who can actually make strong video content, who can talk about the product in a natural and sincere way, and whose audience is curious enough to search and learn more.

That is why the team leans more toward mid-tier and long-tail influencers in practice. These creators tend to be more specific, less commercial in feel than top tier influencers, and more like genuine product enthusiasts sharing something they actually use. They also feel closer to their audience. In tool-related content especially, viewers usually need to believe that the creator has really used the product before they become interested in the product itself.

2. Clear platform priorities: YouTube first, then Instagram and TikTok

After running round after round of collaborations, VMEG.AI’s view on platforms has become increasingly clear.

In Fay’s view, YouTube is the top priority, followed by Instagram and TikTok.

Video translation is not the kind of product that can be fully explained in just a few seconds. Users usually need to see a complete demonstration, a clear before and after comparison, and concrete use cases before they can fully understand the value of the product.

In VMEG.AI’s campaign reviews, YouTube has also consistently delivered the highest conversion efficiency.

3. Keep the brief focused on the goal, not on controlling the expression

VMEG.AI has also formed a very clear principle around briefs.

“We keep the goal clear and the process open.”

In practice, that means the brand clearly communicates the core selling points and key content priorities, but does not lock influencers into a rigid script. During content review, the team pays more attention to whether the hook in the first five seconds is strong enough, whether the product is integrated naturally, and whether the overall content still preserves the influencer’s original tone and style, rather than evaluating it line by line against a fixed “correct answer.”

This method was not there from the start. It gradually took shape through repeated collaboration. Over time, the team became increasingly clear that if a piece of content looks like a brand ad at first glance, viewers will usually scroll away quickly. The content that actually lasts tends to preserve the influencer’s own channel language while naturally explaining the product in depth.

4. The best performing content usually makes the before versus after contrast easy to understand

At the content level, one pattern has become especially clear for VMEG.AI.

Videos that visually show a clear before versus after comparison while solving a specific creative pain point are the easiest to make work.

That format is also naturally well suited to the product itself.

What changes before and after translation, what happens when a video enters a different language market, and how a video that could originally only circulate in one language can suddenly be understood by far more people, all of these are naturally persuasive when shown through comparison.

So for VMEG.AI, what AhaCreator helped produce was not just a more efficient workflow for finding influencers. It also helped clarify a broader set of judgments: what kind of creators are right for the product, which platforms are better for conversion, and what content structures are more likely to drive search and results.

There are still areas where in-house teams cannot be fully replaced.

Fay does not present AhaCreator as a universal answer.

She was very candid in saying, “For some top tier influencers, we still manage those relationships ourselves.”

Regular follow-ups during collaboration, staying in touch over holidays, and maintaining long term relationships are still areas where in-house teams do better.

For VMEG.AI, AhaCreator functions more as a capability multiplier for the team. By letting AI take over the heaviest and most repetitive parts of influencer marketing, the team has more time to focus on insight, iteration, and new creative work.