Tmall's nonlinear thinking
Maybe Tmall didn't realize that it was using big data to break linear thinking. Regardless of causality, the ability to speak only data is the future commercial power of Tmall.
"At that step, what do you think Tmall would be like?"
"I can't help you."
Liu Bo (flower name: Jialuo), general manager of Tmall marketing platform, affirmed the tone and did not hesitate. On the wall behind him, there is a "6·18" arrangement of Tmall, with a tight rhythm and no gasping gap.
At this time, there are still 5 days from "6·18", no one can predict the accurate result of "6·18". But Jialuo seems to have a well-thought-out.
Five days later, Tmall's “6·18†data could not be sent from various platforms: 200 tons of milk powder in 5 minutes, 20 million pieces of diapers in 7 minutes, 160 tons of Thai durian in 14 minutes, and 45 million pieces in 10 hours. Mask.
Only 10 minutes after the zero point, the overall turnover of Tmall apparel exceeded 1 billion yuan. During the “6·18†period, the transaction amount of the instalment only exceeded 4 billion yuan.
There are 20% of 00 boys, the first time I placed an order at Tmall International, I bought a make-up...
These data were uploaded to Tmall's system, in the form of 0 and 1, along with hundreds of millions of other transaction data, became the big data that Tmall used to observe the next round of trends.
Naifei, Curry and linear thinking
In the past two years, there have been two stories of big data that have been talked about:
The popular American TV series "House of Cards" is a masterpiece of big data. Using big data analysis, Netflix found that users who like to watch the 1990 edition of "House of Cards" also like to watch the works of director David Finch and Kevin Spacey. Naifei then decided to be directed by David Finch, with Kevin Spacey starring in the remake of "House of Cards."
This is the first story. The second story happened in the NBA.
The Golden State Warriors, who just won the NBA championship, were terrible six years ago. Lacob, a new investor in 2011, is a staunch supporter of data analysis. Using data analysis, the team is aware of the NBA's traditional competition for air-to-air play, which looks good but inefficient and turns to more efficient play. That is to improve the three-point shooting rate. So the Warriors sold the big stars, trained the newcomers, and guided the people and tactics of the game to get the championship.
These two seemingly reasonable stories are more like summing up after the results are reversed, with big data to completely rule the mystery of the world. The reason for reversing the result is linear thinking in chronological order. A occurs before B, A is the cause, and B is the result. It seems logically self-consistent, but it is not necessarily the real reason.
The big data story of "House of Cards" has been proven to be a marketing tool, and the team that uses data in the NBA is not only the Golden State Warriors.
In the era of e-commerce, there are many such stories. The user received a "Congratulations to Daddy" greeting card and an endless baby product push because he clicked on the advertisement of the paper diaper several times. But the switch that triggers all of this, the diaper clicks that may be just a user's misoperation. The boy who just finished the college entrance examination actually wants to click on the advertisement of "King of the King" next to him.
People trading and trading people
The reason for triggering user behavior is often just a change between thoughts.
Most of the brands that are looking for users fall into the trap of linear thinking. Has the user purchased a brand of shampoo six months ago, which is why he continued to purchase after half a year? Will it be the reason for him to buy other shampoos?
Even if you can grasp the most real reason for this user, you can't deduce the purchasing motivation of 1,000 users, let alone millions of users. The biggest headache for the brand is to let those who have never bought it, pick up the shampoo, put it into the shopping cart, go to the cashier, and pay the bill.
When the new product is launched, the advertising words are often “customized for high-end people†and “the choice of young peopleâ€. These seemingly precise positionings are speculations on the characteristics of users' preferences and habits. Even if they have a questionnaire survey, the answer to the questionnaire is “the real needs of users†or “users think they need itâ€, which has been widely questioned.
Before the era of big data, the cost of data acquisition was high and the cycle was long. It was very difficult to find the rules. For the regular acquisition, it is like the process of verifying the theory of relativity: hypothesis - proof - then hypothesis - and then verify.
On the way to find users, the brand is facing more headaches: bold assumptions, can not be verified.
The way to verify in the past is mainly sales data, and everything is judged around the results of the transaction. Use the last trading model to guide the next transaction.
In the era of e-commerce, the data available for brand reference has one more dimension: conversion rate, click-through rate, page time... For a long time in the past, e-commerce platform used this as a weapon to combat traditional retail, providing brands with Compared with shopping malls, cash, TV commercials, street research... More accurate user data.
Precision is a relative concept. 3.141 5 is more accurate than 3.14, 3.141592 is better than 3.141 5 . It depends on your computing power and the need for precise data.
Before the end of the year, Tmall was also positioned to do so. At that time, Alibaba did not propose new retail, and Tmall was the place where the goods were mainly sold.
In May of this year, Tmall replaced the brand positioning of “God Cats bought†and replaced it with “Imperial Life Tmallâ€. Previously, Tmall’s goal was to arrive at “buyâ€, but now it wants to reach “people (ideal life)â€, and the focus shifts from “person’s transaction†to “transactional personâ€.
“In the past ten years, we have helped brands to sell goods, and they have done very well. But at this point, we found that it is not enough to just sell them. If this is the case, the competitiveness of other brands is rising. We need other The level of things.†The “other dimension†in Jialuokou is to find a way to use the data again.
"Before the end of last year, we did not make specific data precipitation for the brand, just to do data feedback, for example, the result of the data is like that, the conversion rate is like this."
Geese and "people"
As mentioned before, the individual's behavior is only "between thoughts." Unless the chip is implanted in the brain, the individual's "reading" is difficult to judge. What's more, using data to judge an individual's mind will inevitably involve privacy.
If the accessible individual data is aggregated to form a group with commonalities, the behavior of one group can find certain rules and trends. Flying in the geese, we can see the law of "people"; flying alone, only flying itself.
Tmall wants to use the data to find the geese, and there are "people" in the geese.
Thus, the group is deconstructed into data. The data surrounding a group is difficult to estimate on the “quantityâ€. To get data, Tmall needs to rely on the "professional ability" that appears repeatedly in Jialuokou.
This kind of profession, on the one hand, is on the "quantity", on the other hand, on the "force", both of which are based on the Ali ecology.
Tmall accumulates in the data "quantity" from two places: the Tmall platform itself and the place outside the Tmall and within Ali.
The first is the Tmall platform itself. This type of data includes two aspects:
First, direct interaction between users and brands. Buying, browsing, collecting, and buying during the weekdays... The data of interaction between all user groups and brands will be precipitated by Tmall. Every time the data is infiltrated into the next time, the data on the platform is getting thicker and thicker. Activities including poly-costing, fan 趴, super brand day, even if the user just browses and does not generate a purchase, such behavior will be converted into data that will assist the next decision.
Second, the data generated by the user on other related behaviors on Tmall. The user who just purchased the Refa massage instrument clicked on the Philips razor page and after browsing the 20-minute detail page, he quit without placing an order. This serial is hard to say that there is a causal relationship, but the correlation between them will naturally show value after a large amount of data is precipitated.
Secondly, except for Tmall, all places within the Ali ecology, "the geese will leave traces." Alipay's record of Beijing's Xiao Zhang ate Haidilao yesterday and paid the utility bill. Zhengzhou's Xiao Li bought a feather duster in Taobao today, and bought the iPhone7 in a row with the flower buds... The behavior of the user on the Ali ecology, from Data is precipitated in multiple dimensions.
From the perspective of "power", the maturity of Alibaba Cloud has a self-evident effect on the improvement of the data professional ability of each link in the Ali ecology. Now Tmall has the ability to move from "3.14" to "3.141 5", closer to the truth.
At the press conference in May, Tmall released five trend words around the concept of ideal life: Lohas green, lofty, free, innocent, and playful. This is the "person" that Tmall sees from the "geese group" based on big data.
Among these five trends, we can no longer see the direct presentation of “purchasing†and “commoditiesâ€, but instead emphasize the “crowdâ€. Tmall will send the "crowd" to the potential energy in the hands of the brand. It is a beautiful dishwasher that has been exported for 16 years and decided to launch products for the domestic market. In 2016, on Tmall, Midea's dishwasher sales exceeded 40 million yuan, and sales increased by 1 900%.
All non-B, which is the reason for B
If we can enter the digital space of the Internet, we may find that it is the same as the universe, there is no margin, no distinction between the top and bottom, no distinction.
Here is the digital reflection of the real world.
Our behavior in a limited physical world allows the data in this infinite space to swell. All platforms like Tmall, which mine data and precipitate data, are doing what they are doing to wake up the sleeping data in this space and then observe and gain insight into the data.
The linear thinking that begins with Newton's classical mechanics needs to be broken in this space, and everything is discontinuous. In the past "A→B", A is the reason for B, not only A, we also found "A1, A2 ... A20", more and more reasons, but we are still looking for answers under linear thinking.
What we have to face is probably "all non-B, both are the reasons for B."
There is no definite reason, only data that can be inspected. This is the most awesome place in the era of big data.
From the linear feedback data in the past to the stereoscopic precipitation data, Tmall is doing the technical support to try to break the linear causal chain and precipitate a stereoscopic user group.
Regardless of cause and effect, only talk about data.
It’s not just Tmall, the ability of China’s e-commerce services users, no need to rumor. However, if the value of the brand is only at the level of the transaction, then the three best e-commerce companies in China should be the discount network, the group purchase network, and the rebate network. Low prices and discounts are a good medicine for triggering transactions, but such transactions are not sustainable, let alone brand value. Continuous transactions that are not abducted by discounts require brands to find premium capabilities in terms of service and brand value.
The brand wants to find its own audience, the user needs the brand that suits him best, and the two ends are attractive to each other. What Tmall used to do is one of the most basic functions of the Internet: matching. Matching the right brand for the user and finding the user for the brand, this matching ability comes from the data force.
If the brand is a tree, the thing that Tmall does is supported by data, so that the tree grows high enough on the Tmall platform, so that people far enough to see it and come over.
The interview of “6·18†five days ago, in the Ali Park, the general manager of Tmall Marketing Platform, Jia Luo, talked about the power of technology. He believes that technology will shift from the “satisfying business needs†of the past to the driver of the business model. When the technology of excavating data and precipitating data is more powerful, the imagination of Tmall will be large enough to understand user trends and deliver value to brands.
"omnipotent."
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