From Content Creation to Growth: How DeepKOX Helped a Beauty Brand Increase Qualified Customer Inquiries by 300%

For beauty brands running KOS programs, producing more content is rarely the hardest part.

The real challenge is making better decisions at scale:

What kind of content actually works? Which message fits which advisor? And once content goes live, where should the brand invest additional traffic?

One leading Chinese beauty brand working with DeepZero operates more than 400 stores and over 3,000 beauty advisors, with an established network of experienced KOS creators.

Yet despite the scale of the program, more than 80% of content exposure remained concentrated among a small number of mature accounts. Successful content was difficult to replicate across the wider network, while decisions around paid amplification and customer inquiries in the comment section still depended heavily on fragmented manual judgment.

The brand introduced DeepZero DeepKOX to change that.

DeepKOX brings together eight AI agents that share brand knowledge, KOS profiles, and operational feedback across the entire workflow—from content insight and creation to compliance review, publishing, inquiry handling, performance analysis, and learning.

Rather than using AI only to generate posts, the system connects these decisions into a continuous agentic operating loop.

A Valentine’s Day makeup trial campaign shows how that loop works in practice.

AI Content Insight + AI Content Creation: Learning What Works Before Creating More

Before the campaign launched, the AI Content Insight Agent analyzed the brand’s highest-performing posts alongside popular content from the broader beauty category.

It broke those posts down across dimensions such as content format, usage scenario, customer pain point, and target audience, then compared those elements with the customer inquiries generated by the brand’s historical content.

The objective was not simply to identify what received the most views or likes, but to determine which content characteristics were actually associated with meaningful customer interest.

Those findings were passed directly into the AI Content Creation Agent, together with campaign information, brand guidelines, and the profile of each individual KOS.

As a result, advisors did not receive identical scripts.

One might introduce the makeup trial through a personal, everyday-use scenario. Another might focus on the steps involved in the service. A third might emphasize the finished makeup look and how it appears on the skin.

The content varied by creator, but the underlying decisions were grounded in patterns validated against real performance data.

AI Content Review + AI Publishing: Deciding What Can Go Live—and When

Once a draft was submitted, the AI Content Review Agent checked more than prohibited or sensitive language.

It validated product names, promotional offers, required campaign elements, and IP authorization periods against the brand’s rules, then determined whether the post could be approved automatically, should be returned for revision, or required human review.

Only approved content entered the publishing queue.

The AI Publishing Agent then considered the campaign window, account status, and publishing frequency to determine an appropriate release time for each account.

For the brand’s central operations team, this replaced the need to chase individual posts through group chats. Publishing status across the KOS network became visible and manageable within one operating workflow.

AI Inquiry Detection + AI Response: Distinguishing Engagement from Purchase Intent

After a post goes live, not every comment carries the same commercial value.

A customer asking, “Will this look cakey on my skin?” is different from someone writing, “Looks great.” And a question such as, “Which nearby store offers the makeup trial?” may indicate clear intent to visit a store.

The AI Inquiry Detection Agent evaluates each comment together with the context of the original post to distinguish casual engagement from product questions and higher-intent service inquiries.

It also connects each inquiry to the relevant KOS and store.

The AI Response Agent then draws on product information, makeup-effect knowledge, and store-service information to determine the appropriate response.

Straightforward questions can be answered directly. More complex situations—such as questions requiring skin-condition assessment or an in-store appointment—are escalated to the appropriate beauty advisor.

This gives the brand a clearer operating model for every inquiry: who should respond, what information is needed, and what should happen next.

AI Data Collection + AI Analytics: Identifying What Deserves More Traffic

DeepKOX continues working after content is published.

The AI Data Collection Agent gathers performance data across campaign tasks, individual posts, and accounts, including views, engagement, and qualified customer inquiries.

The AI Analytics Agent then evaluates those results alongside content characteristics to identify which posts are more likely to generate meaningful inquiries and which content structures are worth repeating.

Brand operators use those recommendations to make the final decision on paid amplification, directing additional exposure toward content with stronger commercial potential.

Performance after amplification—including incremental inquiries—is then written back into the system.

Over time, DeepKOX builds a richer understanding of what type of content works, which creator is best suited to deliver it, and which audiences are most responsive.

The output of one campaign therefore becomes an input into the next.

Analytics is no longer simply a post-campaign reporting exercise. It becomes part of the next content decision.

300% Growth in Qualified Customer Inquiries

The Valentine’s Day makeup trial campaign demonstrated the value of this operating model.

Qualified customer inquiries increased by 300% compared with the previous baseline.

But the more important result was not that AI helped the brand produce more content.

It was that AI became part of the operating process itself.

With DeepKOX, multiple AI agents work within a shared context across content analysis, creation, governance, publishing, customer engagement, and performance optimization. They continuously make decisions, execute tasks, observe outcomes, and use those outcomes to improve the next cycle.

This is what distinguishes agentic software from a standalone content-generation tool.

For DeepZero, it also reflects a broader principle behind AI for Growth:

AI should not simply help create marketing content. It should participate in the decisions that drive growth.