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

“A good wine is always afraid of a dark alley.” Only when product or service information is delivered to customers can transactions be facilitated. It is generally believed that advertising is needed to convey product or service information to customers. Advertising has been around since ancient times, and the guise of “three bowls of wine is not enough” is advertising. In the era before the Internet, we were familiar with TV advertisements, radio advertisements, print advertisements, outdoor billboards, etc., of course, including shouting and selling. But in the past, advertising was one-size-fits-all and did not differentiate between audiences. Later, merchants collected customer information and developed CRM. After classifying customers, they can better serve different customer groups. The Internet + big data era has given CRM new development opportunities. Managing customers is no longer a matter of simple numerical statistics, impersonal (or simple clustering) direct mail, and fixed investment. As businesses know more and more about their customers, they have the opportunity to provide customers with personalized marketing plans and further improve customer experience, which is called personalized marketing or precision marketing. In the era of big data, many things that were impossible in the past have become possible, and marketing activities have also won new development opportunities.

As times change, the form of business operations will change, but the essence is two things: increasing revenue and reducing expenditure. Open source is to develop new customers and discover new business opportunities; throttling is to reduce internal operating costs and improve resource utilization efficiency. Achieving all of this requires data-driven decisions. In the past, people also collected and used a lot of strongly relevant data related to business activities in long-term business activities, and also formed criteria for selecting customers. In view of the technical bottlenecks at the time, the cost of data collection and data analysis for large samples was too high, and it could not be promoted and applied on a larger scale. 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 the use of multi-dimensional data to observe and describe customers, that is, to create portraits of customers. It is not an exaggeration to say that "relying on big data, marketers can understand customers better than before and understand their needs better than the customers themselves." Marketers all want to know who their customers are, where they are, what their consumption habits are, what they need, when they need it, how to deliver information to them more effectively, etc. The answers can be found through data collection and data analysis. Precision marketing can not only help merchants gain revenue---discover potential customers, but also help merchants reduce expenditure---discover potential risks. When we know more about our customers, we will know which customers may be at risk in our operations.

If you ask every operator whether he or she will use business experience for marketing, most of the answers will be yes. But if you ask operators whether they will use data for marketing, I'm afraid the answers will be varied. It is generally believed that the application of data for marketing is a matter for large companies and has no relevance for small companies. In fact, when companies as large as multinational companies or as small as street vendors use data for marketing, they will receive unexpected results. Don't believe it? Street vendors pay attention to the weather forecast (wind, rain, or sun) to know what business opportunities there will be tomorrow, and then know how to stock up. It is recommended that people in small and medium-sized companies should not reject the concept of precision marketing and might as well learn the thinking and methods of precision marketing. Even if the operator has rich experience, digitizing the experience will be very helpful to the operation.

The book "Disruptive Marketing" teaches readers how to use big data for marketing. The book is rich in cases and the language is highly readable. It is worth reading for friends from all walks of life who are concerned about big data marketing.

I agree with many of the views in the book: "Big data redefines the rules of industrial competition. It is not about the size 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 should indeed pay more attention to the understanding and application of the core value of data. "Asking the right questions" proposed in the book is also important. Operators must have a lot of questions on a daily basis, but when they ask for the truth, deviations may occur, leading to "a mistake that makes a difference a thousand miles". Improving the ability to ask correct questions involves thinking methods and needs to be improved through exercise. Verifying that the questions are asked correctly is exactly where data analysts can contribute.

This book also raises two questions that deserve more in-depth consideration:

It is not enough to just discover the consumption habits of different customer groups and remind customers to consume in a timely manner. For example: a consumer's normal and rational consumption in a month is at the level of 2,000 yuan, and he usually spends money in two stores, A and B. Store A uses the concept of precision marketing to make consumers spend the two thousand yuan in store A. As store B catches up, consumers may return to store B to spend the two thousand yuan. In today's world of excess supply and insufficient demand, the distribution or migration of existing consumption among different merchants cannot lead to an increase in total social consumption. A higher-level application of big data marketing is to know in advance the needs of customers that have not been met or even discovered. The value mining of big data has the opportunity to connect merchants (including manufacturers) and customers, allowing merchants to provide more products or services that meet customers' personalized needs, and increase customers' willingness to consume. This is a new challenge faced by data value mining workers.

Is more data really better? Many big data companies are keen to use crawler software to "crawl" various data on the Internet. However, the value density of the same data set is different in different application scenarios. For a specific application scenario, more data dimensions are not always better. Data must be collected and used around the application goals. Raising the dimension to collect more data will definitely help describe things in more detail, but it will undoubtedly increase the complexity of processing the data. Every technological advancement brings new imagination space to human beings. It is inevitable that desires will expand and self-confidence will increase, and the understanding of the world will also increase, even uncontrollably. Later, I discovered that dimension upgrading brought about the occupation of resources, and my wisdom could not keep up. Uncontrolled dimension upgrading would complicate the solution. When I calmed down, I would restart thinking about dimensionality reduction. Perhaps human cognition and wisdom are advancing alternately in dimensionality promotion, dimensionality reduction, dimensionality promotion again, and dimensionality reduction again. This book's thinking on dimensionality reduction and returning to basics when necessary provide inspiration to people.

In the era of big data, tools and methods are important, but thinking methods are even more important.