What will agricultural e-commerce rely on to dominate the market in the future?
Not long ago, Chen Lei, chairman of Shenzhen Dabaicai Technology, gave an example: "Last year, when Hami melons hit the market, the price at the Jiangnan market was around two yuan, while at the Nanning market it was two yuan and three jiao. As a result, within two days a large volume of Hami melons flooded into the Nanning market, and two days later the price of Hami melons in Nanning dropped to one yuan and six jiao." Chen Lei explained the reason as "insufficient planning and excessive market speculation."
Can this phenomenon be avoided?
What agricultural e-commerce sells is not just agricultural products
As a trader, selling goods at a good price is the hard truth of survival. However, whether this truckload of goods can make money depends not on data and technology, but on personal experience and luck, relying on the business wisdom of "mere practice makes perfect." Suppose a trader could know in advance the incoming shipments and sales situation of Hami melons at a given wholesale market, and could predict the price fluctuations of Hami melons during that period -- could that reduce the probability of this phenomenon occurring? Of course it could.
"Dabaicai+" aims to build the underlying layer of bulk agricultural product circulation data for 43 agricultural wholesale markets in China. Through the physical markets of nationwide bulk agricultural product circulation, it links tens of thousands, even a hundred thousand, professional wholesalers, and thereby grasps 150 billion in transaction data. When this data runs on the same platform, we can fully predict the transaction situation of a certain single product at a certain time in a certain region. This is precisely the core service that merchants and markets need, and I suspect this is also the core dream that "Dabaicai+" wants to realize -- what is called data mining and value recreation.
Data is the foundation of enterprise survival in the internet era. An enterprise without data is like a human without a brain; without a brain, one cannot think or predict. This is true for enterprises, and why not for society as well? The "unsalable goods" situation common in our agricultural production -- can the situation improve? Of course it can. The key to the problem is: what do the vast number of farmers rely on to guide production and operation? Unfortunately, although we already live in a highly developed intelligent society, agricultural production and operation still remain in the traditional era of "experience." What to plant, how much to plant, and how to plant all mainly rely on personal experience. Whether government or enterprise, none of us can obtain precise data, let alone speak of scientific analysis. "Unsalable goods" thus becomes the inevitable result of farmers misjudging the market, and ultimately it is still the farmers who pay for the losses, and this kind of vicious cycle repeats endlessly.
Today, the market is no longer lacking in agricultural e-commerce companies with diverse models; what is lacking are internet enterprises that can capture agricultural data and make good use of that data. But then again, if an enterprise does not control bulk orders and transaction data, talking about "big data" is a lie. Only when data is built on the basis of dynamic transactions can it bring its data value into play.
"Dabaicai+" is the first internet enterprise in China attempting to establish underlying data services through agricultural wholesale markets, and it is rapidly linking the transaction data of dozens of domestic agricultural wholesale markets. Judging from its current stage of development, rather than calling it an agricultural B2B enterprise, it would be better to call it an agricultural big data company. On the surface it sells agricultural products; in essence it sells agricultural data. This is precisely the blue ocean market of the future.
In recent years, fresh food B2C has risen rapidly, agricultural B2B is in fierce competition, and various community O2O models are dazzling. Who will ultimately have the last laugh? I think it will certainly be those enterprises that control agricultural big data, and certainly those enterprises that are good at mining and building data value.
The insurmountable 4B transaction chain of large-scale agricultural product circulation
It is currently the season for eating apples. Let us look at what this apple must go through before it reaches your mouth. If we break down the apple's transaction chain, it can be summarized into four types of transaction entities:
1) B1, the producer, mainly fruit farmers, cooperatives, and production enterprises.
2) B2, the processor, mainly fruit companies engaged in sorting and premium packaging.
3) B3, the distributor, mainly traders, WeChat merchants, wholesalers, etc.
4) B4, the retailer, mainly e-commerce, supermarkets, community stores, fruit shops, etc.
The founder of Wangku once spoke of a similar 4B theory, but agriculture is rather special. In actual transactions there are also B5, B6, and B7, and the transaction model is even more complex.
Taking apples as an example, B1 is the grower of primary apple products, B2 is the brand operator with grading, sorting, premium packaging, and storage capabilities, B3 is the various merchants or entrepreneurs doing apple business, and B4 is the various common retail channels at the consumer end.
The sales path of this apple is as follows:
Scenario one: B1 to B2 to agricultural wholesale market to B3 to B4 to C (consumer)
Scenario two: B1 to B2 to B3 to B4 to C (consumer)
The main difference between scenario one and scenario two is that the former goes through the wholesale market, while the latter goes directly to distributors or terminal retailers without going through the wholesale market.
Wholesale markets are usually the main distribution centers guaranteeing the dining table needs of a city's residents, such as the Shenzhen Haijixing Wholesale Market and the Chengdu Agricultural Products Wholesale Market. The agricultural products circulating here guarantee the daily living needs of nearly 80% of the city's residents. However, transactions in traditional wholesale markets are almost entirely completed offline by upstream and downstream enterprises, and traditional wholesale markets do not control transaction data. This is precisely the market gap of agricultural wholesale markets. The above "scenario one" is precisely the online transaction model that "Dabaicai+" strives to realize. It hopes to build an online version of the agricultural wholesale market, providing more precise commercial predictions for upstream and downstream enterprises through data services, and helping merchants expand additional market opportunities.
Agricultural B2B, fresh food B2C, community O2O, and so on -- no matter what name they are given, in essence they are still doing retail in the internet way. Combining the 4B transaction model above, let us review together the development of fresh food e-commerce in recent years.
Looking at the competitive advantages of future agricultural e-commerce from the perspective of the transaction chain
1) Fresh food B2C drives the origin-direct procurement model
2012 was quite ordinary, but the appearance of one orange made that year exceptionally dazzling. What "Chu Orange" reflected was not just Chu Shijian's own craftsman spirit; from the perspective of the industry's positive significance, what was more important was that it drove the development of the origin-direct procurement model in fresh food e-commerce.
The success of "Chu Orange" was inseparable from the "buyer" system of Benlai Life Network. "Buyers" can go deep into the place of origin to discover good goods and integrate the supply chain. This counts as an innovation of fresh food e-commerce.
The transaction chain of fresh food B2C is: B4-2-C. Benlai Life is B4, but B4 needs origin B1 to realize origin-direct procurement. The actual transaction chain is B1-B4-C.
In reality, when you open a fresh food e-commerce website or APP, the goods are dazzlingly displayed, but many SKUs actually include groups such as B1, B2, and B3. So this transaction chain is still the online version of the traditional B1-B2-B3-B4-C, and we are still some distance from genuine origin-direct procurement.
Fresh food e-commerce, although it already has several dark horses, upon careful comparison actually has no more core differences. Your family sells Aksu apples, and his family sells them too; the difference lies in different production areas and prices, as well as different copywriting stories.
Against this background, how do we realize the "disintermediation" advocated by internet thinking and truly realize the "B1-2-C" direct procurement model? Only by bringing into play the value of big data. When the order volume for a certain single product is large enough and stable enough, we then have the ability to bring orders and join with upstream bases to carry out contract farming. Product standardization is controllable, and the upstream and downstream supply chains are controllable, so the enterprise then has the ability to achieve lower transaction costs and higher transaction efficiency.
Using consumption data to reverse-transform the production activities upstream in agriculture -- this will be the core strategy for fresh food B2C to build competitive barriers in the next three years.
2) Distributors help drive the transformation and upgrading of the agricultural industry
In September 2014, Alibaba was listed on the New York Stock Exchange. The next day, Jack Ma couldn't wait to announce the "Thousand Counties and Ten Thousand Villages" plan, saying he would invest 10 billion yuan in rural e-commerce development. Those few days I was in Hohhot, Inner Mongolia, making a special trip to attend the opening ceremony of the Taobao.com Specialty China Inner Mongolia Pavilion. Alibaba president Jin Jianhang flew from the United States to the scene to congratulate and announced Jack Ma's plan.