Editor's note: Yang Senlin, Vice President of C&S Jierou, will personally dissect this three-year-honed growth methodology at the closed-door session of the 2026 China FMCG Conference AI Application Forum.

Let's start with a number.

In 2025, C&S Jierou's annual profit grew 312% year-on-year; in Q1 2026, Jierou maintained its momentum, with profit continuing to surge 53% year-on-year.

This is not a prediction or an estimate; it is disclosed financial data.

In an era when the entire FMCG industry is struggling to grow, industry insiders can deeply appreciate the weight of Jierou's growth figures.

Behind this counter-trend performance lies a complete AI growth system that took three years to refine, spanning data, algorithms, organization, and execution.

This article marks the first public disclosure of this methodology. But frankly, it is only the skeleton of a growth strategy. The real flesh and blood will be at that half-day closed-door session in Hangzhou on May 28.

We Are All in the Same Predicament

Before explaining how Jierou broke through, let's first address a predicament that almost everyone in the FMCG industry faces but rarely admits openly.

Three sets of data, let's compare:

Over 60% of strategic errors stem from over-reliance on experience-based decisions rather than data-driven decisions;

The average sell-through rate in offline stores is less than 40%, with a large amount of distribution turning into inventory pressure;

Over 70% of strategic failures are ultimately attributed to execution deviation—the strategy is correct, but it deforms at the frontline.

When these three numbers are combined, they mean:

Most FMCG companies are, every day, using "emotional judgment" to drive "inefficient execution," burning resources into a bottomless black hole.

Before embarking on the AI growth path, Jierou was mired in the same situation: revenue growth lagged the industry average for multiple consecutive quarters, market share was eroded by competitors, costs rose while price increases were difficult, and profit margins continued to decline.

This is not just Jierou's story; it is the collective predicament of the entire FMCG industry.

Three Leaps in Three Years: How AI Rewrote the Growth System

Jierou's AI growth path was not achieved in one step but through three clear strategic leaps.

Understanding these three leaps is more valuable than any AI technical course—because it tells you not what AI can do, but how an FMCG company should walk this path.

First Leap: 1.0 Era—Smart Store Selection, Solving "Where to Go"

In the past, where salespeople went to develop new stores relied on the experience and intuition of regional managers. Good regional managers made accurate judgments; poor ones led their teams for six months and opened only inefficient stores, wasting resources.

The core action of 1.0 was replacing intuition with data. Jierou integrated nationwide POI data for tens of millions of stores, built algorithm models, and precisely targeted high-value areas and stores.

From then on, the question "which terminals are worth doing" had a more reliable answer than experience.

Second Leap: 2.0 Era—Strategy Matching, Solving "Where We Can Go"

Once "where to go" was known, the second question followed: for equally high-potential stores, what should we sell, how much should we invest, and how should we invest to maximize ROI?

In this phase, Jierou introduced population data—13.39 billion person profiles, 2,000+ tags, precise down to 440,000 districts nationwide—to build an intelligent matching engine for people, goods, and places:

Which districts have stronger purchasing power, and which products should be distributed there?

Which store types' customer profiles best match premium products?

Is this region worth increased investment, and what is the estimated ROI?

Resources shifted from "average distribution" to "focused breakthroughs," with 20% of high-potential stores contributing over 80% of incremental revenue. The traditional inefficient scattergun approach was thus ended.

Data shows that among the 3.62 million+ surviving stores nationwide, only 290,000+ truly met Jierou's high-potential criteria, accounting for less than 8%—but these 8% of stores had a closure rate of only 4%, far lower than the 34% for ordinary stores. Precisely finding this 8% is several times more efficient than scattergun coverage of 100%.

But at this point, Jierou hit a wall that was harder to break through.

Third Leap: 3.0 Era—AI-Driven Execution, Solving "How to Win"

Data became more accurate, algorithms more refined, and the list of high-potential stores was printed—but performance did not grow proportionally.

Jierou defined this phenomenon as: accurate calculation ≠ effective execution.

Bridging the "last mile" from data to performance is the true key to AI growth moving from theory to explosion. 3.0 was about solving this.

Why Is It Still Hard to Execute Even When Calculations Are Accurate?

This is the most counterintuitive and most valuable insight in the entire methodology—a lesson that most FMCG companies only understand after paying expensive tuition in AI implementation.

Let's reconstruct a typical scenario: a company digitizes, integrates algorithms, and prints out the list of high-potential stores—then what?

Salespeople get the list but don't know the order in which to visit; when they arrive at the store, they don't know which products to push or where to place them; after the visit, there is no feedback mechanism, and data doesn't flow back. Next time? Next time, it's back to gut feeling.

There is a huge gap between the algorithm's high-potential list and frontline execution capability. This gap is not a technical issue; it is an execution system issue.

Jierou's solution is called "AI-ifying the top salesperson."

Every FMCG company has one or two top salespeople—they have accurate intuition, good relationships, good judgment, and good communication; one person can do the work of three. But this capability cannot be replicated; once they leave, the team collapses immediately.

Jierou turned the top salesperson's decision logic into algorithms and their execution actions into standardized SOPs. Through AI-driven tools, every ordinary salesperson can fight the way the top salesperson does:

  • The algorithm automatically generates the optimal visit route, no longer relying on salespeople to prioritize themselves;
  • AI monitors attendance, price anomalies, competitor dynamics, and display standards in real time, making the execution process fully traceable;
  • Alerts automatically trigger task closures; out-of-stock, declining sell-through, and execution deviations all receive responses and reviews;
  • Data flows back in real time, strategies iterate dynamically, and every visit feeds new experience into the algorithm.

The result: after process standardization, personnel efficiency increased by 70%; after AI intelligent decision-making intervened, trial-and-error costs decreased by 60%.

Not Just Technology

More Importantly, a Top-Down Organizational Revolution

The most common misreading of Jierou's case is: after reading it, you think, "I just need to buy an AI system."

This misreading could cost you very expensive tuition.

Technology is only one layer of Jierou's system. What truly made the 312% growth possible is the simultaneous reconstruction of three layers:

Layer 1: KPI System Reconstruction

If the assessment baton doesn't change, AI implementation will inevitably fail.

Jierou changed its assessment system from "result-oriented" to "high-potential-oriented":

Result indicators account for 50%, process indicators for 30% (new customer acquisition rate, high-potential conversion rate), and strategic indicators for 20% (digital tool usage rate, new product contribution ratio).

The logic is straightforward: if you only assess sales revenue, salespeople will turn a blind eye to AI tools.

For AI to truly run, the assessment system must leave room for process and the future. Without this layer, all technical investment is money down the drain.

Layer 2: Organizational Structure Reconstruction

AI implementation is a CEO project, not an IT department matter.

Jierou established a cross-departmental growth special team, breaking down barriers between sales, marketing, IT, and data departments, delegating decision-making, and establishing an independent assessment system.

The core principle is simple: reduce approval layers and respond quickly to the market. If AI system recommendations still require five levels of approval to be implemented, half of their value is already lost.

Layer 3: Incentive Mechanism Reconstruction

Without strong incentives, even the best system is a decoration.

Honor (public high-potential achievement rate rankings and top salesperson leaderboards), threat (last-place elimination, breaking the "iron rice bowl" mentality), and material rewards (store output strongly linked to personal income) are used in tandem.

The core idea is one sentence: use results to force execution; if you don't act, you're out.

This Closed-Door Session Will Thoroughly Explain Jierou's AI Growth Methodology

The above is the skeleton of Jierou's AI growth methodology.

But anyone who has worked in industry knows that the skeleton of a methodology is the easiest part to talk about; the real difficulty lies in the unclear but crucial details:

When data accuracy is only 70%, what should you do? Endless verification or start moving? Jierou's choice is contrary to most companies' intuition.

The finance department halting an AI project on risk-control grounds—almost every company has encountered this. How did Jierou handle the "business-finance relationship"?

Without Jierou's data scale, where should small and medium FMCG companies start? How do you calculate ROI, and how do you take the first step?

Over the three years, what was Jierou's most regretted decision? If it could start over, what would it do first?

Which parts of this methodology can be directly replicated, and which parts are highly dependent on Jierou's own conditions—where is the boundary?

These questions cannot be answered in this article.

On May 28, 2026, in Hangzhou, at the closed-door session of the first China FMCG Conference AI Application Forum, Yang Senlin, Vice President of C&S Jierou and the core architect of Jierou's AI transformation, will fully unpack this methodology, including all the pitfalls, all the mistakes, and the less glamorous transformation process behind those financial figures.

The closed-door session is limited to 30 participants, all senior executives of FMCG brands. You can ask questions on the spot, debate, and present your own company's difficulties; the people present will help you think.

In an industry, very few people are willing to share their true experience, even their failures, in full. This kind of opportunity won't come twice.

Jierou's case has convinced us of one thing:

AI rewriting growth is not a distant future concept, nor a capital game only big companies can play. It is a methodology that can be understood, replicated, and implemented.

It requires not just technical investment, but also a cognitive upgrade for managers—cognition of AI, determination for organizational change, and judgment of the window of opportunity.

The window still exists, but it won't stay open forever. The companies that run through it first will build capability barriers that competitors will find hard to replicate in three years. The step you take now determines where you stand three years from now.