What is the use of big data for brand marketing?

The main value of big data marketing comes from the following aspects:

First, analyze user behavior and characteristics. Obviously, as long as enough user data is accumulated, users' preferences and buying habits can be analyzed, and even "know users better than users" can be achieved. With this, it is the premise and starting point of many big data marketing. In any case, those enterprises that used to take "everything is customer-centric" as the slogan can consider it. In the past, did you really know the needs and ideas of customers in a timely and comprehensive manner? Perhaps the answer to this question is clearer only in the era of big data.

Second, the push and support of precision marketing information for many years. Precision marketing has been mentioned by many companies, but it is really rare, but it is a flood of spam. The main reason is that the nominal precision marketing in the past was not very accurate, because it lacked the support of user characteristic data and detailed and accurate analysis. Relatively speaking, RTB advertising and other applications now show us better accuracy than before, behind which is the support of big data.

Third, guide products and marketing activities to meet user preferences. If you can understand the main characteristics of potential users and their expectations of products before production, then your product production will be to your liking. For example, before Netflix recently filmed House of Cards, it learned about the favorite directors and actors of potential audiences through big data analysis, and the result really captured the hearts of the audience. For another example, after the trailer of Tiny Times was put into use, we learned from the big data analysis on Weibo that the main audience of the film was the post-90s women, so the subsequent marketing activities were mainly aimed at this group of people.

Fourth, competitor monitoring and brand communication What competitors are doing is what many companies want to know. Even if the other party won't tell you, you can know it through big data monitoring and analysis. The effectiveness of brand communication can also be found in the right direction through big data analysis. For example, communication trend analysis, content feature analysis, interactive user analysis, positive and negative emotion classification, word-of-mouth category analysis, product attribute distribution and so on. It can be done by monitoring the spread of competitors, referring to industry benchmark users, planning content according to users' voices, and even evaluating the operation effect of Weibo matrix.

Fifth, brand crisis monitoring and management support the new media era. The brand crisis has made many companies daunting, but big data can give enterprises insight in advance. In the process of crisis outbreak, the most important thing is to track the trend of crisis spread and determine the important participants in order to facilitate rapid response. Big data can collect negative definitions, start crisis tracking and early warning in time, identify key people and transmission paths according to the analysis of people's social attributes and the clustering of opinions in the event process, thus protecting the reputation of enterprises and products, grasping the source and key nodes, and responding to the crisis quickly and effectively.

Sixth, the choice of key customers of enterprises. Many entrepreneurs are entangled in: among the users, friends and fans of the enterprise, which are the most valuable users? With big data, maybe all this can be more supported by facts. From the various websites visited by users, we can judge whether the things they care about recently are related to your enterprise; From all kinds of content published by users on social media and the content of interaction with others, we can find out countless pieces of information, and use some rules to correlate and synthesize them, which can help enterprises screen key target users.

Seventh, use big data to enhance the user experience. The key to improving the user experience is to really understand the user's status and the products they use, and make the most timely reminders. For example, in the era of big data, maybe the car you drive can save your life in advance. As long as the vehicle operation information is collected through sensors all over the vehicle, you or the 4S shop will get early warning before the key parts of your car have problems, which is not only to save money, but also to protect life. In fact, as early as 2000, UPS Express Company of the United States used this forecasting and analysis system based on big data to detect the real-time situation of 60,000 vehicles in the United States in order to carry out defensive maintenance in time.

Eighth, SCRM customer classification management supports ever-changing new media. Many enterprises hope to transform fans into potential users, activate the value of social assets and make multi-dimensional portraits of potential users by analyzing their public content and interaction records. Big data applications can analyze the interactive content of active fans, set various rules for consumer portraits, associate potential users with membership data, associate potential users with customer service data, and screen target groups for precise marketing, thus integrating traditional customer relationship management with social data, enriching users' labels in different dimensions, dynamically updating consumer life cycle data, and keeping information fresh and effective.

Ninth, discover new markets and trends. The analysis and forecast based on big data is of great support for entrepreneurs to gain insight into new markets and grasp economic trends. For example, Alibaba discovered the international financial crisis earlier from a large number of transaction data. For another example, in the 20 12 US presidential election, David Roth Rothschild of Microsoft Research Institute used big data model to accurately predict the election results of 50 of the 5/kloc-0 electoral districts in 50 US states and the District of Columbia, with an accuracy rate higher than 98%. Later, through big data analysis, he predicted the attribution of the 85th Academy Awards. Except for the best director, all other awards are considered to win.

Tenth, the support of market forecast and decision analysis. The support of data for market forecasting and decision analysis was put forward as early as the era when data analysis and data mining prevailed. Wal-Mart's famous "beer and diapers" case was a masterpiece at that time. Only the above-mentioned volume (large scale) and variety (multiple types) put forward new requirements for data analysis and data mining in the era of big data. More comprehensive and timely big data will inevitably provide better support for market forecasting and decision analysis. You know, specious or wrong outdated data is a disaster for decision makers.