Ten key points for large data marketing

Intermediary transaction SEO diagnosis Taobao guest Cloud host technology Hall

In the past two years, everyone has been talking about big data, but there are so many people who talk about it. What many entrepreneurs are more concerned about is: how can we really find the entry point for big Data marketing? The author concludes the following 10 points.

Wen/Chen Yongdong

Many people feel that the big data age is coming, but it's often just a hazy feeling, and the power it really brings to marketing can be described in a fashionable word--unclear. In fact, we should try to understand it, and then we will understand its powerful point. For most enterprises, the main value of large data marketing stems from the following aspects.

First, user behavior and feature analysis. Obviously, as long as the accumulation of sufficient user data, you can analyze the user's preferences and purchase habits, and even do "more than users know the user himself." With this, is a lot of large data marketing premise and starting point. In any case, those enterprises that used to be the slogan of "All customer-centric" can think about it, have you really been able to understand the customer's needs and desires in a timely and comprehensive way? Perhaps only the answer to the question in the big data age is clearer.

Second, precision marketing information push support. In the past many years, precision marketing is always mentioned by many companies, but really do a few, but the garbage information flooding. The main reason is that in the past nominal precision marketing is not very accurate, because of its lack of user characteristics data support and detailed analysis of accurate. In contrast, the current RTB ads and other applications show us better precision than before, and behind it is the large data support.

Third, to guide the product and marketing activities to vote for users. If you can understand the main characteristics of potential users before production, and their expectations of the product, then your products can be produced. Netflix, for example, has captured the hearts of viewers by Toupai a large data analysis of the potential audience's favorite directors and actors before it was near the House of Cards. For example, "small time" in the trailer after the launch, that is, from the micro-blog through large data analysis of the film's main audience for the female, so follow-up marketing activities are mainly targeted at these people.

Four, competitor monitoring and brand communication. What a competitor is doing is what many businesses want to know, even if they won't tell you, but you can learn from the Big Data monitoring analysis. The effectiveness of brand communication can also be based on large data analysis to find the direction. For example, can carry on the dissemination trend analysis, the content characteristic analysis, the interactive user analysis, the positive and negative sentiment classification, the word of mouth category analysis, the product attribute distribution and so on, may through the monitoring grasps the competitor dissemination situation, and may refer to the Industry benchmarking user planning, according to the user voice plan content, even may

Five, brand crisis monitoring and management support. In the new media age, the brand crisis makes many enterprises pale, but the big data can let the enterprise have the insight in advance. In the process of crisis, the most need is to track the trend of crisis communication, identify important participants to facilitate rapid response. Large data can be collected negative definition content, timely start crisis tracking and alarm, according to the social attributes of the population analysis, clustering Event process point of view, identify key people and transmission path, and then can protect the reputation of enterprises, products, grasp the source and key nodes, quickly and effectively deal with the crisis.

Six, the Enterprise key customer screening. What many entrepreneurs struggle with is: what are the most valuable users in a company's users, friends, and fans? With big data, perhaps all this can be more factual. A variety of Web sites that users visit can determine whether their recent concerns are related to your business; from the content that the user releases in the social media and interact with others, can find the countless information, use some kind of rule association and synthesize, can help the enterprise to filter the target user of the key.

The big data is used to improve the user experience. To improve the user experience, the key is to really understand the user and their use of the situation of your products, to do the most timely reminder. For example, in the big data age, you might be driving a car that could save your life in advance. As long as you collect vehicle running information through sensors all over the car, you will be alerted to your or 4S stores ahead of time in the critical parts of your car, which is more than just saving money, and it's great for protecting your life. In fact, UPS Express in the United States in 2000 to use this large data based predictive analysis system to detect 60,000 vehicles in the United States real-time car condition, in order to timely conduct defensive repair

VIII, Customer rating management support in SCRM. In the face of the ever-changing new media, many companies want to transform their fans into potential users, activate the value of social assets, and perform multiple dimensional portraits of potential users by analyzing their public content and interactive records. Large data can analyze the interactive content of active fans, set up a variety of rules for consumer portraits, associate potential users with member data, associate potential users with customer service data, and filter target groups for precision marketing, which in turn enables traditional customer relationship management to combine social data and enrich the labels of different dimensions of the user, Consumer lifecycle data can be dynamically updated to keep information fresh and effective.

Ninth, discover new market and new trend. Based on the analysis and prediction of large data, it is of great support for entrepreneurs to provide insight into new market and grasp economic trend. Alibaba, for example, has discovered the advent of the international financial crisis earlier in a large number of transaction data. Also, in the 2012 U.S. presidential election, the David Rothschild of Microsoft Project used a large data model to accurately predict the results of 50 of the 51 districts in the 50 states and Columbia, Dist. Of, more accurate than 98%. Later, he also through large data analysis, the 85th Annual Academy Awards of the attribution of the prediction, in addition to the best Director, all other awards predict all hit.

Tenth, market forecasts and decision analysis support. Support for data on market forecasting and decision analysis has long been raised in the age of data analysis and data mining. Wal-Mart's famous "beer and diapers" case was the masterpiece. Just because of the large data age the above volume (large scale) and produced (many types) have put forward new requirements for data analysis and data mining. More comprehensive, faster and more timely large data, the market forecast and decision analysis will provide better support for further steps. You know, plausible or erroneous, outdated data is a disaster for policymakers.