"Disruptive Marketing: Business Revolution in the age of Big Data" : Big data "more is less, less is more" a variety of marketing methods have been dazzling

All kinds of marketing techniques are already dazzling, but the essence is to study the customer (consumer), to study what the customer wants and needs, so that the product or service is targeted. The era of big data has given it a new term: precision marketing. Most of the first applications of big data are customer-facing industries, and most of the first applications are precision marketing.

"The best wine is the best alley", the product or service information to reach the customer is likely to lead to a deal. It is generally believed that advertising is the key to communicating information about products or services to customers. Advertising has existed in ancient times, "three bowls but post" the guise of wine is advertising. Without the Internet era, we are familiar with television advertising, radio advertising, print print advertising, outdoor billboards, etc., of course, also includes Shouting. But in the past, advertising was one-size-fits-all and undifferentiated. Later, when merchants collect customer information, they have CRM, which can better serve different customer groups through customer classification. The era of Internet + big data has brought new development opportunities to CRM. Customer management is no longer a simple number statistics and direct mail and subscription without personality (or simple clustering). With more and deeper understanding of customers, businesses have the opportunity to provide customers with personalized marketing plans to further improve customer experience, which is called personalized marketing or precision marketing. In the era of big data, many impossibilities in the past become possible, and marketing activities also win new development opportunities.

Different times, the form of business will change, but the essence of two things: open source, save money. 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. All of this requires data-driven decision-making. In the past, people also collected and used a lot of strong correlation data related to business activities in long-term business activities, and formed the criteria for selecting customers. In view of the technical bottleneck at that time, the cost of data collection and data analysis of large samples was too high to be applied on a wider scale. In the era of big data, it is possible for people to collect and store data cheaply, and cheap computing resources make data analysis possible.

Big data precision marketing is behind the use of multi-dimensional data to observe customers, describe customers, that is to say, for the customer portrait. It is fair to say that "relying on big data allows marketers to understand their customers better than ever before, and understand their needs better than the 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, what is the most effective way to deliver information to them, and so on. Through data collection and data analysis, they can find out the answer. Precision marketing can not only help businesses open source, which is to find potential customers, but also help businesses reduce costs, which is to find potential risks. As we learn more about our customers, we know which ones might be at risk in our operations.

If you ask every executive whether he or she will apply his or her experience to marketing, most say yes. But ask managers if they use data for marketing and the answers are mixed. Using data for marketing is generally thought to be a big business, not a small one. In fact, the use of data for marketing, from multinational companies to street vendors, can have unexpected results. Don't believe me? Street vendors who pay attention to the weather forecast (wind, rain, sun) know what business opportunities will be available tomorrow and how to stock up. It is suggested that small and medium-sized companies do not reject the idea of precision marketing, might as well learn the idea of precision marketing. Even if the operator has a lot of experience, it can be helpful to digitize that experience.

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

I agree with many points in the book: "Big data redefines the rules of industrial competition, not by data size, not by statistical technology, not by powerful computing power, but by the ability to interpret core data". Today, when many people are obsessed with the definition of big data, we should pay more attention to the understanding and application of the core value of data. It's also important to ask the right questions. Operators usually have a lot of problems, but when asking what it is, there may be deviation, resulting in "miss as far as possible". The improvement of the ability to ask the right questions involves the method of thinking and needs to be improved in exercise. Verifying the right questions is where data analysts can contribute.

The book also raises two questions that deserve further consideration:

It is far from enough to discover the consumption habits of different customer groups and remind them to consume at the right time. For example: A consumer's normal rational consumption in a month at the level of 2000 yuan, generally in A and B two stores consumption. If A store uses the concept of precision marketing, consumers will spend 2,000 yuan in A store. As B store comes in behind, consumers may return to B store to spend 2,000 yuan. In today's world of excess supply and insufficient demand, the distribution or migration of existing consumption among different merchants cannot increase the total amount of social consumption. A higher level application of big data marketing is knowing ahead of time that a customer's needs have not yet been met or even discovered. The value mining of big data has the opportunity to connect merchants (including manufacturers) and customers together, so that merchants can provide more products or services to meet customers' personalized needs, and improve customers' consumption willingness. This is a new challenge for data value mining workers.

Is more data really better? Many big data companies are keen to use crawlers to "crawl" all kinds of data on the web. However, the value density of the same data set is different in different application scenarios. For specific application scenarios, the more dimensions of data is not the better. Data collection and use must be centered on application objectives. Moving up the dimension to collect more data certainly helps to describe things in more detail, but it certainly adds complexity to processing the data. Every technological progress brings new space for human imagination. It is inevitable that our desire and confidence will swell, and our understanding of the world will also be enhanced, even immoderately. Later, I found that the increase of dimension would cause the occupation of resources, and the wisdom could not keep up. The excessive increase of dimension would complicate the solution. When I calmed down, I would restart the thinking of dimension reduction. Perhaps human cognition and intelligence is in ascending dimension, descending dimension, ascending dimension, descending dimension. The book's thinking of dimensionality reduction, when necessary to return to the original thinking to enlighten people.

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