Recently, while discussing channel transformation with an FMCG company, they raised a very practical question: in first- and second-tier cities, how can we continue to increase coverage?
This question sounds familiar. For many years, when FMCG companies talked about channels, they couldn't avoid two words: coverage. Cover more blocks, cover more outlets, and as long as you occupy enough shelf space, growth will naturally happen.
But in that discussion, we actually formed a sharper judgment: the word "coverage" might already be a product of the previous era.
The current situation is likely that the more you think about "coverage", the harder it is to truly "cover".
Why?
Because today's problem is not whether there are enough outlets, but whether outlets have value; not whether products are placed, but whether products match people, scenarios, and business formats.
In the past, companies asked "how many stores have I covered?" Today, they should ask "where are my target consumers, in what scenarios do they purchase, and what products, through what channels and outlets, do I use to satisfy them?"
In my previous article, I mentioned that channel competition is shifting from "coverage competition" to "matching efficiency competition."
So, how is matching efficiency achieved? Not by more salespeople, but by stronger regional operational capabilities.
"Coverage" Fails Because Outlets Have Changed
First- and second-tier cities do not lack outlets.
In cities like Beijing, Shanghai, and Hangzhou, convenience stores, fresh food stores, community small shops, discount stores, and instant retail warehouses are only increasing in density.
But outlets are differentiating.
- Some outlets have changed their operational logic. They proactively manage categories, looking at turnover, sales per square meter, and consumer repurchase. If something sells slowly, they replace it; if supply is unstable, they switch sources.
Their purchasing paths no longer rely solely on the traditional distributor chain—wholesale markets, B2b platforms, supply chain service providers—they source from wherever conditions are suitable, flexibly switching based on sell-through speed.
I visited a community fresh food store in Hangzhou. The owner told me that for their core categories, part comes from the nearby wholesale market, part is ordered through a B2b platform, and part is delivered regularly by a supply chain service provider. Payment terms, minimum order quantities, and delivery frequency all differ. Restocking is very flexible.
This store is not in the traditional distributor coverage path of brand owners. But its monthly sales are not small.
- Another part of outlets has not changed their operational logic. They still wait for salespeople to deliver, and still rely on old relationships to receive brand policies. These outlets are doing worse and worse. Consumers are leaving, sell-through is slowing, and products on shelves are increasingly hard to move.
This differentiation poses two layers of difficulty for brand owners.
Covering shrinking outlets means resources are spinning in vain—products placed sell slowly, and expenses are wasted. Meanwhile, outlets with changed operational logic have their own product selection standards and purchasing channels; when distributor salespeople visit, they may not even get a foot in the door.
This is why the more you think about coverage, the harder it is to truly cover. It's not just a resource issue; the key is that supply and demand are not matching.
Why Sales Targets Are Not Met
The Answer Lies in Structure
Facing sales pressure, the first reaction of sales leaders is almost fixed: distributors aren't working hard enough, salespeople aren't diligent enough—distribution rate is insufficient.
These directions are not entirely wrong, but they easily keep people on the surface.
Once, I talked with a regional manager of an FMCG brand. In his region, core category sales in a certain city had been below expectations for three consecutive quarters. His first reaction was that distributors were slow and salespeople's visit frequency was insufficient.
But when we dug deeper, we found the real problem: in this city, community fresh food stores had sprung up in large numbers over the past two years, and a significant portion of consumer purchase scenarios had shifted there, but the brand's local distributor had not covered these outlets at all.
It wasn't that salespeople weren't diligent; it was that they were visiting the wrong places.
If sales problems are not broken down further, and you only stay at urging distributors and adding salespeople, you can at most solve short-term stocking, not structural sell-through issues.
But to break it down, what do you use?
In the past, this was hard because information was too fragmented. Distributors said one thing, salespeople said another, outlet feedback was another, and system data was yet another. Branches relied on people to aggregate, headquarters relied on reports to judge, and in the process of information being passed up, many details were distorted.
AI is changing this. Which outlets have long-term no sell-through, which distributors have abnormal inventory, which business formats are growing fastest—this information now has the opportunity to be organized by systems, rather than passed up layer by layer by people.
But AI provides information, not judgment.
After seeing the structure clearly, where exactly are the growth opportunities in this region, and where should resources be allocated—this still needs humans to answer.
This is the first thing about regional operational capability: seeing the market structure clearly.
After Seeing the Structure Clearly
Allocate Resources to the Right Places
After seeing clearly, then what?
Many regional leaders in companies can sense that the market is changing, but they don't know how to adjust their resources.
This involves the second thing: allocation.
In a region, a brand has many resources: distributor networks, sales teams, budget, and external channels like B2B platforms, instant retail, wholesalers, and warehousing and distribution service providers. In the past, these resources often worked independently, and no one knew whether the entire region's resources were allocated reasonably.
But consumers don't buy according to a company's internal organizational structure.
The same consumer might buy on a whim at a fresh food store today, order through instant retail tomorrow, and take an item from a community small shop the day after. For them, these are just different purchase scenarios. For brand owners, they might correspond to completely different channel teams, different distributors, and different expense pools.
Regional operators need to be able to judge: which outlets should be served by traditional distributors, which scenarios should access B2B, which SKUs are suitable for instant retail, and which distributor has the capability to serve emerging small shops.
This judgment was hard to make in the past because data from various channels was fragmented, and no one could look at the entire region's resource usage together. This situation is changing, but it hasn't been universally solved—most regional leaders still rely on experience and intuition for this allocation.
This is not a problem that can be solved by visiting more stores. How to use AI to help regional operators connect data and see resource distribution clearly is a topic that both enterprises and individuals must seriously address.
When Expenses Are Invested, Results Must Be Tracked
The most important part of resource allocation is expenses.
In the past, channel expenses often followed tasks: to boost volume, give policies; to push inventory, give rebates; to expand distribution, give expense support. In the era of growth, this logic could still work—the market was still rising, and it could absorb some of it.
In the era of surplus, it's different.
Channels won't automatically generate real sell-through just because you give expenses. Some expenses only transfer inventory from the enterprise to distributors; some activities are executed but don't lead to consumer purchases; some displays are done, but the position is wrong, the scenario is wrong, the product is wrong, and output is extremely low.
Regional operators must be able to answer one question: where does money go to truly bring results?
In the past, this was hard to track. After expenses were invested, sell-through data was too scattered to correspond to specific expense actions. The result was that everyone knew half of the expenses were wasted, but didn't know which half.
This situation is being changed, but it hasn't been universally solved. Enterprises that can answer "did I spend my money correctly?" are still a minority.
Seeing the structure clearly, allocating resources, and tracking expenses—these three things sound clear, but truly capable regional operators are still a minority today. The reason is not complicated: achieving these three things requires a completely different capability structure than in the past.
In the AI Era
Qualified Regional Operators Need Three Capabilities
Recently, I saw a diagram about how people in enterprises will be divided into three layers in the AI era: the outermost layer, those who connect with the real world; the middle layer, those who master AI; and the innermost layer, those who make judgments.
At first glance, it seemed insightful. But I quickly found a problem: if this diagram is taken literally, it's wrong. Enterprises won't really be divided into three layers of people. A regional leader must both visit outlets, use systems to view data, and make regional judgments. He alone lives in all three layers simultaneously.
So the correct interpretation of this diagram is not to describe three types of people, but three types of capabilities.
The ability to connect with the real world. Being able to enter outlets, sense the market, and obtain first-hand information that systems can't get. Why the community fresh food store owner changed supply sources, why the distributor is unwilling to accept goods—AI can't give answers to these; only people who are on the scene know.
The ability to master AI. Being able to use AI to organize regional sales structure, identify sell-through anomalies, and analyze expense input-output. Sooner or later, this will become a basic skill for regional operators, just like "being proficient in Excel." Currently, most regional teams in enterprises have not yet built this capability.
The ability to make judgments. AI can provide analysis and suggestions, but the final decision is made by humans, and only humans can make it. This capability is the core value of regional operators and the hardest part to replace.
Missing any one of the three capabilities will cause problems. Only connection without mastery means judgments rely on old experience; only mastery without connection means getting second-hand information; only judgment without the first two means the decision-making basis itself is wrong.
What I observe in many enterprises now is: the front line is executing, but no one teaches them how to use AI; management is making judgments, but still based on old experience; the middle layer that truly knows how to use AI for analysis hasn't even grown yet.
All three capabilities are missing, but no one realizes this is a capability structure problem.
As FMCG channel competition reaches this point, the logic of competing on coverage has come to an end. In the next stage, what truly differentiates is whether regional operators have these three capabilities—being able to sense the market, use AI, and dare to make judgments.
This combination of capabilities cannot be achieved by adding more people.
We will delve into these issues at the "CFC AI Application Forum" in Hangzhou on May 27-28. If you are also thinking about how FMCG channel organizations should change and what AI can really do in regional operations, you are welcome to join us on site.
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