Business rules in the era of big data

The era of big data has brought unprecedented business opportunities to enterprises. In the era of big data, enterprises must learn to use big data to accurately analyze, import users, facilitate transactions, and organize production in the most efficient way. In the era of big data, enterprises must follow new business rules, otherwise they will be overwhelmed by the wave of big data.

Rule 1: Interpreting users' real needs Interpreting users' real needs is to tap users' inner desires through data collection and analysis, improve the success rate of enterprise product promotion, and turn them into enterprise orders.

Big data seems mysterious. In fact, the operational idea of interpreting users' needs is extremely simple, that is, to grasp users' personal information and attention information as much as possible. When the relevant information is aimed at individuals, users' needs can be defined relatively accurately.

In this process, there are two main operation modes: static radiation mode and dynamic tracking mode.

Static radiation mode

Analyze the data of static radiation pattern at a time node, expand the analysis object as much as possible, and screen the most likely users through labels. This is the most typical mode in big data applications. Since some large enterprises will actively manage user tags, enterprises that need big data to help marketing can "borrow a boat to go out to sea".

There are two relationships between labels and purchases: one is that the relationship between labels and purchases is very obvious. For example, a user who often browses economic management books must be a potential buyer of such books.

The relationship between another label and purchase is not obvious. This requires enterprises to analyze in advance, and sometimes it needs the help of the analysis results of third-party professional institutions.

For example, Sina Weibo will label users according to their usual browsing and expressions. However, the relationship between these labels and some purchase behaviors is not obvious. Mrs. King is a domestic wedding photography giant. First, they used their status as big customers of Baidu to obtain the survey and analysis data of wedding photography customers provided by Baidu for free, and found that users with labels such as food and cinemas were most likely to buy wedding photography products. Taking advantage of this cross-database result, Mrs. Jin targeted the group of "women aged around 20-35 in a certain area" on Sina Weibo platform, added labels such as food and cinemas, accurately targeted users with great transformation potential, purchased the "standardized" service provided by the platform, and pushed targeted advertisements to them. Generally speaking, if you push 50,000 to 60,000 users, you will get about 70 to 80 telephone consultations. This transformed telephone consultation customer is called "customer resource". From customer resources to the final order, the conversion rate is excellent, about 40%.

Dynamic tracking mode

The data analysis of dynamic tracking mode is carried out in a period of time, and the analysis object is narrowed as much as possible, and the user is constantly labeled through the user's behavior, waiting for an opportunity to find the time to push the product. Because this analysis is aimed at small groups, it is impossible for third-party organizations to provide unified large-scale services, so the threshold for enterprises is very high and they need to practice their internal strength. In this mode, enterprises should keep track of the new data generated by users and process it in the cloud at any time.

For example, Target supermarket is based on more than 20 kinds of goods that pregnant women may buy during pregnancy, and takes the purchase records of all users as the data source. By establishing a model to analyze the behavioral correlation of buyers, we can accurately infer the specific delivery time of pregnant women, so that the sales department of Target can send corresponding product coupons at different stages of each pregnant customer. In each case, they actually know her pregnancy information earlier than the user.

For another example, Amazon conducts precise marketing based on its own understanding of users, and the recommendation and mail push products on the website have become a weapon to promote transactions. According to the performance of other e-commerce websites, in some cases, the sales conversion rate recommended by Amazon website can be as high as 60%, according to Sucharita Murpuru, an analyst at Forrester, a research company. This conversion rate is much higher than other e-commerce websites. No wonder some observers regard Amazon's recommendation system as a "killer application". The latest news shows that Amazon has registered the technical patent of "no order, first delivery", more accurate demand forecast and more direct product push. Their application of big data is perfect!

Rule 2: Form socialized cooperative production arrangements

If we can rely on big data to promote product purchase, massive demand will surge from the Internet. This means more product data, more raw materials, and scattered orders from consumers ... This change has made the standardized product production mode in the industrial era subverted as never before, and the production side needs to form unprecedented flexibility based on big data to connect the flexibility of the consumer side.

The Internet business environment poses a new challenge to the value chain: in all links of the chain, such as procurement, production, logistics, distribution and retail, besides production, other links also need strong data processing capabilities, and the data processing systems and data in each link must be shared by the whole society, and these systems and contents must also be open to the whole society. To meet this requirement, it is obvious that the value chain network should be applied and big data should be used for production coordination.

Big data does bring opportunities to reshape the value chain. In the era of industrial economy, production gains more through "economies of scale", and large-scale standardized production minimizes unit costs. However, in the era of Internet economy, production should benefit from "scope economy, synergistic effect and reshaping learning curve", because diverse and small-scale production needs intelligent cooperation in the value chain.

Based on the Internet, all the links in the value chain can realize data sharing and centralized processing. In addition, due to the unified data architecture, there will be no data islands and valuable data will not be wasted. In this way, all links in the value chain can be seamlessly linked to achieve the most agile and reasonable production. Based on the Internet platform, enterprises can get sufficient information when they are shortlisted for cooperation, and will not encounter too high a learning threshold. More importantly, it is convenient for users to participate in the production, and the modular multiple-choice questions allow amateurs to send professional demand signals. In this way, from the initial producers of raw materials to the final consumers, they are all embedded in the value chain (or value network), and social cooperation can be truly realized. Before big data appeared, it was almost impossible!

Follow the law and win the future.

Unique big data business rules will lead to changes in future business structure. The winner in the future will belong to the representative who can adapt to the new business rules and new business logic.

In the world of big data nuggets, whoever has mastered big data and can use big data to change the above two business rules will win the future.

Therefore, we can definitely judge that resource integration enterprises that have mastered big data will become enterprise winners in the era of big data. This kind of enterprise is the "helmsman" in the business ecology (value network). By sensitively identifying market demand and guiding network members to cooperate in production, they gain the advantage of combinatorial innovation. Because they control the whole network, such enterprises have the residual claim of network income and often make the most profits. In the era of industrial economy, enterprises rely on brand, reputation and social capital to realize resource integration. In the Internet age, resources have become infinitely rich and collaboration has become extremely frequent. Enterprises need to rely on big data to discover needs and integrate resources. It can be said that after mastering big data, such enterprises will know "what users want, where there is something, and how to use resources to meet users' needs".

Future resource integration enterprises will operate based on big data. In the era of big data, Viktor Mayer-Sch?nberger and others divide the resource integration enterprises based on big data into three categories: the first category is the enterprises with data, which have mastered the port and ownership of data; Second, enterprises that master algorithms are responsible for processing data and mining valuable business information. These enterprises are called "data warriors"; The third is a thinking enterprise. They tend to find market opportunities first. They have neither data skills nor professional skills, but it is precisely because of this that they have broad thinking and can connect resources in series to the greatest extent to form a business model. They are equivalent to "pathfinders".

According to the value and scarcity of their respective production factors, it is hard to say which kind of enterprises will really benefit from the business model of big data. Each of the three types of enterprises has its own contribution and scarcity.

ITASoftware is one of the four major air ticket reservation systems in the United States, and it is a typical enterprise with data. It provides data to Farecast, which is an enterprise that provides forecast air ticket prices. It is a typical enterprise with algorithm and thinking, and directly contacts users. As a result, ITA software only made little profit in this cooperation.

Overture is the originator of the pay-per-click mode of search engines. If Google is regarded as the media, then Overture is equivalent to an advertising agency, which subdivides different browsing users through algorithms and provides the advertising company with paid clicks of target users (choose the users they need most). Overture is a typical enterprise that masters algorithms and thinking, while Yahoo and Google are enterprises that master data. In fact, Google's two major gold mines, AdWords and AdSense, have borrowed from Overture's algorithm. However, Overture could not directly contact users, had no data, lost the right to speak, and only got a small amount of income, so that it was eventually acquired by Yahoo.

The resource integration enterprise ecological chain based on big data will follow two laws.

Rule 1: Enterprises that contact users can always get the maximum benefit, which is highly consistent with the distribution principle in the value chain. The difference between the terminal price and the supply of raw materials is all obtained by the enterprises selling the terminal products.

Rule 2: Enterprises with data have the greatest bargaining power in this business ecosystem and are most likely to be the final winners. Algorithms can be conquered or purchased. In fact, there are not a few companies that have squeezed into this industry. In thinking, there is a phenomenon called "information paradox" by kenneth j. arrow, that is, information is extremely valuable before it is known by others, but it cannot be proved. Once it is publicly confirmed, it loses its value because everyone knows it. Therefore, no matter how fast thinking and algorithm companies go, as long as data companies can block data sources at any time, they still hold the "killer". Even some data companies release data to thinking and algorithm companies for trial and error when they can't see the business model clearly. Once the trial and error is successful, they will take back the ownership of the data and imitate their business model.

BAT's data empire

Therefore, we can say that in the era of big data, the competition of resource integration enterprises will determine the territory of the future business world.

When many people haven't figured out the business rules in the era of big data, the domestic Internet giants BAT (Baidu, Ali, Tencent) are already building their own "data empire" quickly.

In the big world of the Internet, users have many entrances and can upload data through different apps. The principle of BAT is that all service providers related to food, clothing, housing and transportation will win as long as they can increase the type and quality of data. Here embodies a typical "increasing marginal revenue effect of data accumulation", that is, every time a unit of data is added, its exploitable value will increase at an accelerated rate, and every time a type of data is added, its exploitable value will increase at an accelerated rate. Sometimes, BAT doesn't even consider whether the data can be realized as income at this stage, but just includes it and waits for future development.

The reality is that after several rounds of acquisitions, BAT basically covers data portals in various fields such as food, clothing, housing, transportation and social interaction. In addition to its original huge data portal, its advantages in data scale are unparalleled. In a short time, it is almost impossible for any enterprise to surpass them.

BAT is not only an enterprise that grasps data, but also an enterprise that grasps algorithms and thinking. On the one hand, big data with huge commercial user groups and user groups' consumption preferences can form transactions and gain profits as long as there is corresponding content. On the other hand, they can even open the application programming interface (API) and authorize their own data to others, so that the data can generate value repeatedly. In this regard, Alibaba's Hundred Rivers Plan is a typical example. Simply put, they open the data to other vendors' apps for free, but they don't charge, just need them to give back the data as a price. After the implementation of this plan, all apps will be their entrances.

It can be said that the empire of BAT is based on data. Some people even predict that data, as "off-balance-sheet assets", will be included in accounting standards at some point. Because, relative to intangible assets, the value of such assets is greater.

It is worth mentioning that people with traditional industrial economic thinking simply cannot understand the business logic in the era of big data. Some scholars once questioned Alibaba's acquisition (retail, culture, finance, etc. He cited the acquisition cases of Apple and Google, and thought that both of them made acquisitions in professional fields, which was conducive to enhancing competitiveness, but Ali's acquisition was diversified, which was not conducive to enhancing competitiveness.

In fact, this is a manifestation of not understanding Alibaba's business model. Most of the business models in the Internet era have long been separated from industry restrictions, and to some extent, they have moved towards "great unification", that is, "importing traffic+big data analysis to achieve traffic". In this mode, data is universal logic. No wonder when big data appeared, Viktor Mayer-Sch?nberger and others asserted that the light of industry experts and technical experts would be concealed by data experts, because the latter could listen to the voice of data without being influenced by old ideas.

Although BAT is so strong, there are still some business opportunities in their cracks, and enterprises can also build entrances, interpret demand and arrange production. If the magic of big data transforming business is beyond doubt, then why do many companies still can't afford the golden key? It is largely because these companies lack data genes.

Under the attack of big data and Internet economy, enterprises can only "passively connect to the Internet". In the face of massive potential demand, it is not only impossible to interpret, but also impossible to mobilize production for docking. This has led to a large number of enterprises being "swallowed up" by the massive demand of the Internet, and the supply chain is out of control.

In the era of big data, enterprise scale, capital and production technology are no longer important, and brands are no longer magical. The ability to acquire data, analyze and process data, and tap the value of data has become the foundation of enterprises. At present, most domestic enterprises have not realized that we have entered the era of big data, just as most of our consumers have not realized that our consumption behavior is being calculated at any time. In such an era, only enterprises based on data and those operating according to the business rules in the era of big data can survive better.

The above is the relevant content of business rules in the era of big data shared by Bian Xiao. For more information, you can pay attention to the global ivy and share more dry goods.