Redefining the New Paradigm of E-commerce Marketing in the Age of AI

Author | Cynthia
Editor | Zheng Xuan

“Using AI to transform an industry” is a slogan the e-commerce world has repeated for years.

But for a long time, real action stayed fragmented—limited to single-point features like “You May Also Like” or image search. Useful, yes, but not deep enough to drive a true, system-level AI overhaul across the entire marketing chain.

The bottlenecks have been clear.

First, earlier AI capabilities simply weren’t mature enough to support end-to-end coordination at scale. Second, system-level transformation inevitably touches a platform’s core product logic—and piling on new tools and new workflows almost always raises the learning curve. For everyday sellers, complexity translates into higher labor and operational cost, making large-scale adoption hard to sustain.

Until 2025, when Douyin E-commerce took the first decisive step to break the impasse—pushing AI deeper while simultaneously lowering the threshold for merchants.

The “secret weapon” is Qianchuan Chengfang (千川乘方), unveiled as a major release at the recent Qianchuan Conference. Powered by AI and the platform’s precision user insights, Chengfang doesn’t just simplify merchant operations and elevate content experiences. It aims to anticipate demand, spark demand, and execute “a strategy for every user”—so merchants, users, and the platform can all win.

So what exactly is Qianchuan Chengfang?

And how does it tackle the so-called “impossible triangle” of high growth, better user experience, and better merchant experience in Douyin E-commerce?

01

Qianchuan Chengfang and E-commerce in the AI Era

Why is AI-driven e-commerce marketing taking off this year?

The logic is straightforward: AI explosions require two ingredients—enough data fuel, and a mature enough technical engine. Right now, Douyin E-commerce has both.

Start with the data fuel.

During the Qianchuan Conference, the platform shared a set of internal figures that illustrate the scale of its content-to-commerce loop: daily e-commerce short-video plays reaching 11.6 billion; UGC “experience sharing” video plays at 48.6 billion; and e-commerce live-room views at 4.4 billion. This flood of real, user-generated content has become a new seeding engine—creating fresh cycles of interest and conversion.

Even more telling: after watching e-commerce short videos, Douyin users trigger 110 million searches per day. And 74% of users purchase with coupons—proof that price sensitivity and content-driven interest can combine into a highly effective conversion mechanism.

Most importantly, Douyin holds a uniquely powerful dataset across three dimensions: behavior + transaction + content.

That combination allows AI to understand not just what a user clicked, but who they are, what they truly prefer, what they’re willing to pay for—and how the entire conversion path actually unfolds. This moves far beyond surface-level matching.

Now look at the technical engine.

Over the past five years, deep learning recommendation models have remained the backbone of e-commerce search and feed distribution. But many other AI technologies—multimodal AI included—often stayed at the “assistive tool” level, largely because the tech wasn’t ready for commercial-grade deployment.

That’s now changing, thanks to three critical breakthroughs that are turning AI from supporting actor into lead.

The first breakthrough is the integration of agent capabilities with reinforcement learning.

Historically, marketing tools ran on rigid workflows: delivery systems executed mechanically based on settings like budget, targeting, and bids. If merchants wanted better results, media buyers had to stay up late monitoring dashboards and manually tweaking parameters.

With reinforcement-learning-based agents, AI can start making decisions more like a human media buyer: monitoring ROI, click-to-conversion rates, repurchase rates, and other signals in real time, then automatically reallocating budgets. It can raise bids during peak traffic, reduce spend during low periods, and ensure every unit of budget goes where it matters most.

The second breakthrough is the maturation of model-to-tool control technologies, represented by MCP.

In the past, large models could only “call” external tools through surface-level APIs, without deeply operating inside a tool’s underlying logic. MCP functions more like a control center with “hands and feet,” enabling the model to coordinate multiple Qianchuan marketing tools, execute algorithmic decisions, and reduce the need for human intervention.

The third breakthrough is the commercial arrival of multimodal large models.

Text generation matured earlier, but image and video generation often struggled with issues that directly hurt marketing outcomes: blurry visuals, inconsistent style, or mismatch with product details. In e-commerce, those flaws don’t just look bad—they lower conversion.

Now, with self-developed multimodal models and a broader wave of industry multimodal systems, AI can generate high-resolution visuals, restore product detail with far higher fidelity, and support edits plus batch creation in a consistent style. What used to take a team half a day to shoot and cut can increasingly be produced in minutes—tailored to the visual language that performs on Douyin.

With abundant data and a matured engine, a new intelligent paradigm for e-commerce marketing becomes inevitable.

Qianchuan Chengfang is essentially the concentrated expression of these advantages—and it’s best understood through its three core components:

Qianxun (千尋): demand prediction. It breaks down the barriers between content, products, and users to enable integrated personalization—understanding not only current needs, but also potential needs, and even stimulating new intent.

Qiance (千策): strategy orchestration. It replaces complex and inefficient manual planning with automated “super plans.” Merchants set a total budget and target goal; the system finds the optimal allocation.

Qianyi (千意): dynamic generation and service. From creative production to customer support Q&A to intelligent diagnosis and recommendations, it generates, adapts, and optimizes based on real-time signals.

Together, these capabilities map to a full closed loop: precise demand prediction, global optimization of operations, and dynamic content plus service.

02

Reducing Entropy: How Qianxun Moves Recommendation from Chaos to Prediction

There is a fundamental rule in nature: without external intervention, systems tend to drift toward disorder—an idea commonly described as entropy increase.

Douyin is the largest short-video platform in China, and it has accumulated massive user behavior sequences that are highly relevant to commerce conversion. But as some users’ behavioral sequences have grown beyond ten thousand interactions, it has become nearly impossible for merchants to rely on personal experience alone to achieve truly precise delivery.

So how do you ensure that each unit of traffic reaches the right person—without raising the barrier for merchants?

To counter entropy, you introduce a new “external force”: large models.

Qianxun is that “entropy-reducing force” injected into Qianchuan’s recommendation system. Its core logic is to use large-model reasoning to evolve recommendation from noisy and reactive into predictive and intentional.

It aggregates multimodal inputs—video, audio, and text—into trillion-scale multimodal parameters, then leverages stronger world knowledge, platform commerce knowledge, and deeper inference over user preferences. The result is not only better content distribution, but also demand forecasting that can unlock new consumption intent.

Qianxun’s edge comes from three layers of technical breakthroughs.

First is full-lifecycle expansion of user sequences.

Earlier systems mostly worked with short-term signals and broad categories. Qianxun expands user sequences from hundreds or thousands to tens of thousands, lengthening the observation window while mining deeper preference structures—and the hidden relationships among them.

Second is the upgrade of model scale and architecture.

Qianxun is described as moving from “hundreds of billions of parameters” to a cooperative architecture combining “trillion-level multimodal understanding parameters” with “hundreds of billions of independent sequence reasoning parameters.”

In practice, this means AI can dissect key information across video frames, audio cues, captions, product pages, and review copy—then interpret a user’s behavioral sequence to understand intent rather than just actions.

If a user watches three videos about acne-prone oily skin, the system doesn’t merely recommend face wash. It can infer related needs—like salicylic acid products or moisturizers that support oil control—helping complete a more holistic solution.

Third is world knowledge plus deep understanding—arguably Qianxun’s most critical advantage.

By combining e-commerce knowledge graphs with user preferences, Qianxun can reason across product specs, industry trends, consumer habits, and real-life scenarios. If someone searches for “a dress suitable for a seaside vacation,” AI doesn’t just recommend beach dresses. It can prioritize sun-protective fabrics, quick-dry materials, and photogenic cuts suited to the seaside context.

Overall, Qianxun represents a qualitative leap in Douyin E-commerce recommendation.

For users, it means content feels more relevant—sometimes even surfacing needs they hadn’t articulated. For the platform, it means distribution efficiency improves as traffic flows with greater precision under AI guidance.

But solving distribution is only one piece.

For merchants, the bigger questions remain: how much to invest, where to invest, and how to invest. That’s exactly where Qiance and Qianyi come in.

03

Efficiency Gains: How Qiance Evolves Marketing into One-Click Global Operations

In today’s e-commerce landscape, the era of “pure traffic competition” is fading. The new keyword is global operations.

But executing global operations is hard.

For SMEs, the problem is capability: they often can’t produce enough high-quality creative, can’t afford professional media buyers, and lag far behind the industry’s average traffic management ability.

Large merchants have budget and manpower, but face another pressure: declining marginal returns. As competition intensifies and user data expands to ten-thousand-level dimensions, it is no longer realistic to rely on media buyer intuition to achieve precision at scale.

Qiance was built to break this deadlock.

By automating strategy, it turns marketing planning from a professional, multi-parameter operation into something close to “one-click.” The threshold drops dramatically, and merchants can refocus on the product itself.

The concept is “strategy managed delivery.”

Merchants no longer need to juggle complex distinctions—marketing vs. ads, creators vs. ads, commission vs. ads—nor configure complicated targeting, bidding, and budget splits. They input only three things: a total budget, a comprehensive ROI goal, and the product to promote.

The system then generates a “super plan” that covers all touchpoints across the full customer journey—before purchase, during purchase, and after purchase.

Under the hood, Qiance is described as being powered by reinforcement learning plus MPC (Model Predictive Control) as the dynamic bidding “brain,” paired with MCP as the flexible “limbs” that execute tool operations.

Reinforcement learning helps Qiance learn optimal strategies from data, including which targeting combinations, bid approaches, and channel mixes produce the best ROI across similar products. It also keeps testing and adapting in-flight—like a marketing expert continuously improving through feedback.

MPC complements this by predicting outcomes across many possible combinations, then selecting the best bidding inputs aligned to the merchant’s goals—running a real-time loop of “calculate outcomes → choose optimum → execute inputs.”

With MCP enabling the AI to operate tools directly, strategy doesn’t stop at recommendation—it gets executed.

The result is a fundamental change in the marketing game: participation becomes far more accessible. Whether big or small, merchants can pursue efficient growth without needing a large specialist team.

04

Speed and Scale: How Qianyi Sets a New Standard for Dynamic AI Service

For merchants, high operating cost isn’t only on the traffic side—it’s also on the creative side.

Content creation sits at the heart of e-commerce marketing, but it’s often the most painful part. Human production is expensive and slow, and it’s difficult to chase trends. A professional short-video team may need one to two days to produce a high-quality seeding video, with costs easily reaching hundreds or thousands. SMEs that shoot themselves often struggle with rough visuals and unclear selling points, limiting conversion.

Qianyi targets this problem directly.

Built as an intelligent service agent system driven by large models plus an “X system,” it helps merchants generate marketing assets quickly, while also adjusting in real time based on performance feedback. Its scope extends beyond creative into a broader operational layer: creative generation, asset production, customer support Q&A, and intelligent diagnosis.

On the production side, Qianyi uses multimodal generation to turn basic input materials—product images or model photos—into higher-quality product visuals and short videos.

For example, it can match an input product to a “commuting professional” template, pair it with fitting music and subtitles, and produce a Douyin-ready seeding video. It also internalizes common live-commerce structures—hook, selling points, promotion explanation—so output aligns with proven conversion logic.

Beyond creation, Qianyi addresses service and optimization.

Through a function described as an agent cluster (such as “Zhitouxing”), it can detect issues and diagnose causes. If a merchant’s ROI drops suddenly, it can analyze whether the problem comes from creative fatigue, competitor price cuts, channel shifts, or other factors—then provide actionable recommendations.

These capabilities are supported by two platform advantages: deep multimodal model accumulation that helps preserve quality and style consistency, and a large-scale content knowledge base built from years of data that allows Qianyi to track trends and user preferences accurately.

05

Closing Thoughts

Taken together, Qianxun, Qiance, and Qianyi significantly reduce the operational threshold of e-commerce marketing.

SMEs no longer need to fear that they “can’t build plans” or “can’t create content.” They can concentrate on product and fulfillment. Larger merchants can save time and manpower, shifting resources into product innovation and brand building.

This aligns with Douyin E-commerce’s intended direction: bringing business back to its essence, so strong products can stand out more naturally.

Looking back at the broader history of e-commerce, the pattern is consistent.

Technology has always been the underlying engine behind each wave of marketing transformation. At the same time, marketing demand forces AI to evolve toward real-world usability and scale. The combined urgency from platforms, merchants, and users accelerates the progress and adoption of AI services.

Marketing 1.0 was about channel dividends: win by occupying quality online channels at low cost.

Marketing 2.0 was about precision traffic dividends: win by obtaining sharper user profiles and targeting.

Marketing 3.0 is about AI dividends: win by using AI tools to drive global growth.

In that sense, Qianchuan Chengfang is positioned as a defining representative of the Marketing 3.0 era—using AI to reframe e-commerce marketing and deliver a three-way win.

The platform improves traffic efficiency.

Merchants reduce marketing cost and expand profit margin.

Users receive more precise, higher-quality content experiences.

This may well be what the next decade of e-commerce looks like: marketing handled by the platform and by AI, merchants returning to production and quality—and growth becoming far less exhausting.