"Subversive Marketing: Business Revolution in the Age of Big Data": Big Data "More is less, less is more" Various marketing methods have long been dazzling

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

"If the wine is good, I am afraid that the alley is deep". The information of the product or service must be delivered to the customer to facilitate the transaction. It is generally believed that the information of the product or service to the customer depends on advertising. Advertising has been around for a long time, and the guise of "three bowls but no job" is advertising. In the era without the Internet, we are familiar with TV advertisements, radio advertisements, print advertisements, out-of-home advertising signs, etc., and of course, shouting and selling. But in the past, advertising was one-sided and did not distinguish between audiences. Later, merchants collected information about customers and had CRM. After customer classification, they could better serve different customer groups. The Internet + big data era has opened up new development opportunities for CRM. Managing customers is no longer a simple matter of statistics and direct mail without personality (or simple clustering). As merchants know more about customers and have a deeper understanding, they have the opportunity to provide customers with personalized marketing solutions, further improve customer experience, and become personalized marketing or precision marketing. The era of big data has made many past impossibilities possible, and marketing activities have also won new development opportunities.

In different times, the form of business operation will change, but the essence is two things: open source, throttling. Open source is to open up new customers and find new business opportunities; throttling is to reduce internal operating costs and improve resource utilization efficiency. To achieve all this requires data-based decisions. In the past, people also collected and used a lot of strongly related data related to business activities in long-term business activities, and also formed the criteria for selecting customers. In view of the technical bottlenecks at that time, the cost of data collection and data analytics for large samples was too high to be used on a larger scale. In the era of big data, people have the possibility of collecting and storing data cheaply, and cheap computing resources make data analytics possible.

Behind big data precision marketing is the use of multi-dimensional data to observe customers, describe customers, that is to say, portray customers. It is not an exaggeration to say that "relying on big data can allow marketers to understand customers better than in the past, and understand their needs better than customers themselves". Marketers do not want to know who customers are, where they are, what their consumption habits are, what they need, when they need it, and how to communicate information to them more effectively. Through data collection and data analytics, they can find the answer. Precision marketing can not only help merchants open source - find potential customers, but also help merchants cut costs - find latent risks. As we learn more about our customers, we will identify which ones may be at risk in our operations.

If you ask every business operator whether they will use their experience in marketing, most of the answers are yes. But if you ask the operator whether they will use data for marketing, the answer is probably varied. It is generally believed that the application of data for marketing is the business of large companies, not small companies. In fact, large multinational companies, small street vendors, use data for marketing, will receive unexpected results. Don't believe it? Street vendors pay attention to the weather forecast (wind, rain, or sun exposure) to know what business opportunities are available 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 may wish to learn the thinking method of precision marketing. Even if an operator has extensive experience, it is helpful to digitize that experience.

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

I agree with many of the points in the book: "Big data redefines the rules of industrial competition, not the size of the data, not the statistical technology, not the powerful computing power, but the ability to interpret the core data". In today's world where many people are torn by the definition of big data, we should indeed pay more attention to the understanding and application of the core value of data. The "ask the right questions" raised in the book is also very important. Operators must have a lot of questions at ordinary times, but when they ask the truth, there may be deviations, resulting in "the slightest mistake". The improvement of the ability to ask the right questions involves thinking methods and needs to be improved through exercise. Verifying whether the question is asked correctly is precisely where the data analyst can contribute.

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

Simply discovering the consumption habits of different customer groups and reminding customers to spend in a timely manner is not enough. For example, the normal rational consumption of a consumer is at the level of 2,000 yuan a month, which is generally spent in two stores, A and B. Store A uses the concept of precision marketing to make consumers spend the 2,000 yuan in Store A, and as Store B catches up, consumers may return to Store B to spend the 2,000 yuan. In today's world of excess supply and insufficient demand, the distribution or migration of existing consumption among different merchants cannot bring about an increase in the 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) with customers, so that merchants can provide more products or services that meet customers' personalized needs, and improve customers' willingness to spend. This is a new challenge for data value mining workers.

Is more data really 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 different, and the more data dimensions for specific application scenarios are not the better. It is necessary to collect data and use data around the application goal. Improving the dimension to collect more data will definitely help to describe things in more detail, but it will undoubtedly increase the complexity of processing data. Every technological advancement brings new imagination to human beings, and it is inevitable that the desire will expand and the confidence will be full, and the cognition of the world will also rise in dimension, even unrestrained. Afterwards, it was found that dimensional upgrading brought about the occupation of resources, and wisdom could not keep up. Uncontrolled dimensional upgrading would complicate the solution, and calming down would restart dimensional reduction thinking. Maybe human cognition and wisdom are alternately moving forward in dimensional upgrading, dimensional reduction, re-dimensional upgrading, and re-dimensional reduction. The dimensionality reduction thinking in this book gives people inspiration when necessary.

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