"Subversive Marketing: Business Revolution in the Big Data Era": Big data "more is less, less is mor

Various marketing methods have long been dazzling, but their essence is to study customers (consumers), study what customers think and needs, and make products or services targeted. The era of big data has given it a new term: precision marketing. The first applications of big data are mostly customer-facing industries, and most of the first applications are precision marketing.

"If the wine is good, I am afraid that the alley is deep." The information about the product or service must be delivered to the customer to facilitate the transaction. It is generally believed that the delivery of product or service information to customers depends on advertising. Advertisements have existed since ancient times, and the guise of "three bowls but no post" is the advertisement. In the era without the Internet, what we are familiar with are TV advertisements, radio advertisements, print advertisements, outdoor billboards, etc., of course, including yelling. But in the past, advertisements were one-sided and did not distinguish between audiences. Later, the merchants collected customer information to have CRM. After customer classification, they can better serve different customer groups. The era of Internet + big data has given CRM a new development opportunity. Managing customers is no longer a simple digital statistics and non-individual (or simple clustering) direct mail and fixed investment. As merchants know more about their customers and have a deeper understanding, they have the opportunity to provide customers with personalized marketing plans, further improve customer experience, and become personalized marketing or precision marketing. In the era of big data, many of the impossible in the past have become possible, and marketing activities have also won new development opportunities.

Different times, the form of business operations will change, but the essence is two things: increasing income and reducing expenditure. Open source is to open up new customers and discover new business opportunities; to reduce expenditure is to reduce internal operating costs and improve resource utilization efficiency. To achieve all of this requires data-based decision-making. In the past, people also collected and used many strong data related to business activities in long-term business activities, which also formed the criteria for selecting customers. In view of the technical bottleneck at the time, the cost of data collection and data analysis for large samples was too high to be widely used. In the era of big data, people have the possibility to collect and store data cheaply, and cheap computing resources make data analysis possible.

Behind big data precision marketing is to use multi-dimensional data to observe customers and describe customers, that is, to portray customers. It is not an exaggeration to say that "relying on big data can enable marketers to understand customers better than in the past, and understand their needs better than the customers themselves". Marketers do not want to know who their customers are, where they are, what their consumption habits are, what they need, when they need them, and how to deliver information to them is more effective. Answers can be found through data collection and data analysis. Precision marketing can not only help businesses open up sources---find potential customers, but also help businesses reduce expenditure---discover potential risks. When we learn more about our customers, we will know which customers may be at risk in their operations.

If you ask whether each operator will use his experience in marketing, most of the answers are yes. But if you ask the business operators whether they will use data for marketing, I am afraid that the answer is all kinds of things. It is generally believed that the application of data for marketing is a matter for large companies and has nothing to do with small companies. In fact, large multinational companies and small street vendors who use data for marketing will receive unexpected results. Don't believe it? Street vendors pay attention to the weather forecast (wind, rain, or exposure) to know what business opportunities there are tomorrow, and then know how to stock up. It is recommended that people in small and medium-sized companies do not reject the concept of precision marketing, and may wish to learn the thinking methods of precision marketing. Even if the operator has a wealth of experience, digitizing the experience will be very helpful to the operation.

The book "Subversion Marketing" is teaching readers how to use big data for marketing. The book contains rich cases and strong language readability. It is worth reading for friends from all walks of life who are concerned about big data marketing.

I agree with many points in the book: "Big data redefines the rules of industry competition. It is not compared to the scale of data, statistical technology, or powerful computing power, but the ability to interpret core data." Today, when many people are struggling with the definition of big data, we really should pay more attention to the understanding and application of the core value of data. The "ask the right question" mentioned in the book is also very important. Operators must have a lot of problems in normal times, but when they ask what is going on, deviations may occur, leading to "the slightest error is a thousand miles." The improvement of the ability to ask questions involves methods of thinking and needs to be improved in exercise. Verifying whether the question is asked correctly is precisely where the data analyst can contribute.

This book also raises two questions worthy of more in-depth consideration:

It is not enough to just discover the consumption habits of different customer groups and remind them to consume in a timely manner. For example, a consumer's normal and rational consumption for a month is at the level of two thousand yuan, usually in two stores A and B. Store A uses the concept of precision marketing to allow consumers to spend these two thousand yuan in store A. As store B catches up, consumers may return to store B to spend two thousand yuan again. With oversupply and insufficient demand today, the distribution or migration of existing consumption among different businesses cannot bring about an increase in the total social consumption. The higher-level application of big data marketing is to know in advance that customers' unmet needs, or even undiscovered needs. The value mining of big data has the opportunity to connect merchants (including manufacturers) and customers, allowing them to provide more products or services that meet the individual needs of customers, and increase customers' willingness to consume. This is a new challenge faced by data value mining workers.

Is the more data the better? Many big data companies are keen to use crawler software to "crawl" all kinds of data on the Internet. However, the value density of the same data set in different application scenarios is not the same. For a specific application scenario, the more data dimensions the better, the data must be collected and used around the application target. Increasing the dimension to collect more data must help describe things in more detail, but it will undoubtedly increase the complexity of processing data. Every technological advancement brings new imagination to mankind. It is inevitable that desires are swollen and confident, and the cognition of the world is also upgraded, even uncontrollably. Later, it was discovered that the upgrade brought about the occupation of resources, and the wisdom could not keep up. The unrestrained upgrade would complicate the solution. If you calm down, you will restart thinking about dimensionality reduction. Perhaps human cognition and wisdom are alternately moving forward in dimensionality upgrade, dimensionality reduction, dimensionality upgrade, and dimensionality reduction. The thoughts on dimensionality reduction in this book, and the thoughts of returning to the original when necessary, give people enlightenment.

While tools and means are important in the era of big data, thinking methods are even more important.