Disruptive Marketing: The Business Revolution in the Age of Big Data ": Big Data's" more is less, less is more "Various marketing methods have long been dazzling

Various marketing methods have long been dazzling, but fundamentally they are all about studying customers (consumers), researching their thoughts and needs, and making products or services targeted. The era of big data has given it a new term: precision marketing. The fields where big data is first applied are mostly industries facing customers, and the scenarios where it is first applied are also mostly precision marketing.

Good wine is afraid of deep alleys, and information about products or services must be delivered to customers in order to facilitate transactions. It is generally believed that conveying product or service information to customers relies on advertising. Advertising has existed since ancient times, and the guise of "three bowls but no more than a post" is advertising. In the era without the Internet, we are familiar with TV advertisements, radio advertisements, print advertisements, outdoor billboards, and of course, shouting and selling. But in the past, advertisements were one-sided and did not differentiate between audiences. Later on, merchants collected customer information and developed CRM, which can better serve different customer groups through customer classification. The era of Internet plus big data gives CRM a new development opportunity. Managing customers is no longer simple digital statistics or personalized (or simple clustering) direct mail and fixed investment. As businesses gain a deeper understanding and knowledge of their customers, they have the opportunity to provide personalized marketing solutions, further improving the customer experience and becoming personalized or precision marketing. In the era of big data, many previously impossible things have become possible, and marketing activities have also won new opportunities for development.

In different times, the form of business operations may change, but fundamentally there are two things: open source and cost saving. Open source is about exploring new customers and discovering new business opportunities; Cost saving is to reduce internal operating costs and improve resource utilization efficiency. To achieve all of this, data-driven decision-making is required. In the past, people also collected and utilized a lot of strongly correlated data related to business activities in long-term business operations, and formed criteria for selecting customers. Due to the technological bottleneck at that time, the cost of collecting and analyzing large sample data was too high, making it impossible to promote and apply it on a larger scale. In the era of big data, people have the possibility of collecting and storing data at a low cost, and cheap computing resources have made data analysis possible.

Behind big data precision marketing is the use of multidimensional data to observe and describe customers, that is, to create customer profiles. It is not an exaggeration to say that relying on big data can enable marketers to understand customers better than before and understand their needs better than customers themselves. Marketers all want to know who customers are, where they are, what their consumption habits are, what they need, when they need it, and how to effectively convey information to them. The answer can be found through data collection and analysis. Precision marketing can not only help businesses open up and discover potential customers, but also help businesses cut costs and identify potential risks. As we gain a better understanding of our clients, we will know which ones may be at risk in our operations.

If asked whether each operator would use their professional experience for marketing, the majority of answers are affirmative. But if you ask operators if they will use data for marketing, the answer is probably diverse. It is generally believed that using data for marketing is the responsibility of large companies and not suitable for small ones. In fact, from multinational corporations to street vendors, using data for marketing can yield unexpected results. Don't you believe it? Street vendors need to pay attention to the weather forecast (windy, rainy, or sunny) to know what business opportunities there are tomorrow and how to stock up. I suggest that people in small and medium-sized companies should not reject the concept of precision marketing, and may learn the thinking and methods of precision marketing. Even if the operator has rich experience, digitizing the experience can be very helpful for the operation.

The book 'Disruptive Marketing' is teaching readers how to use big data for marketing. The book is rich in case studies and has 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 of the viewpoints in the book: 'Big data redefines the rules of industry competition, not by the size of data, not by statistical techniques, nor by powerful computing power, but by the ability to interpret core data.'. In today's world where many people are struggling with the definition of big data, we should indeed focus more on understanding and applying the core value of data. The concept of 'asking the right questions' mentioned in the book is also very important. Operators usually have many questions, but when asked for the truth, deviations may occur, leading to the saying 'a small mistake can lead to a thousand miles'. The improvement of questioning ability involves thinking methods and needs to be enhanced through exercise. Verifying whether the question is asked correctly is precisely where data analysts can contribute.

This book also raises two questions worth further consideration:

Merely discovering the consumption habits of different customer groups and timely reminding them to consume is far from enough. For example, a consumer's normal rational consumption for a month is at the level of two thousand yuan, usually at stores A and B. The concept of precision marketing applied by A store will make consumers spend all these two thousand yuan on A store. With B store catching up, consumers may return to B store to consume these two thousand yuan again. In today's world of oversupply and insufficient demand, the distribution or relocation 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 the unmet or even undiscovered needs of customers. The value mining of big data has the opportunity to connect merchants (including manufacturers) with customers, allowing merchants to provide more products or services that meet customers' personalized needs, and increasing customers' willingness to consume. This is a new challenge faced by data value miners.

Is more data really better? Many big data companies are enthusiastic about using web crawling software to "crawl" various data online. However, the value density of the same dataset varies in different application scenarios, and for specific application scenarios, it is not necessarily better to have more data dimensions. It is necessary to collect and use data around application goals. Improving dimensions to collect more data is certainly helpful for describing things in more detail, but undoubtedly also increases the complexity of processing data. Every technological advancement brings new imaginative space to humanity, inevitably leading to an expansion of desires and confidence, and an increase in our understanding of the world, even without restraint. Later on, it was discovered that the increase in dimensionality resulted in resource occupation, and intelligence could not keep up. Uncontrolled dimensionality increase instead led to complex solutions. When one calmed down, they would restart dimensionality reduction thinking. Perhaps human cognition and wisdom alternate between ascending, descending, ascending, and descending dimensions. The dimension reduction thinking in this book and the necessary return to the original thinking can provide inspiration for people.

In the era of big data, while tools and methods are important, ideas and methods are even more crucial.