Analysis of the foundation of next generation marketing of large data

Every two days we produce data that is about 350,000 times times the size of the library in the United States Congress. This includes only 4.8 trillion of the online advertising data generated in 2011, and 294 billion emails sent every day. If these data can be explained, then all these data will provide us with a very valuable reference to analyze and insight into consumers ' consumption habits and intentions. In the final analysis, this can help us integrate a large amount of structured and unstructured data and learn more about it, drawing on the ability to capture large data.

What's the benefit of big data?

First thing, what are the benefits of big data? The answer is the same as most forms of marketing intelligence: a better understanding of customers, a more accurate breakdown of the customer base in order to more effectively respond to the target customer base.

Large data, particularly in relation to predictive analysis, is designed to create a model of consumer buying intentions or tendencies. In fact, according to recent research, 90% of companies dealing with big data also use predictive analysis. Part of the reason is that the amount of data in large data is so large that simple human analysis and the possibility of not analyzing the predicted trends and patterns.

There is something necessary in the prediction analysis. It can inform us of all relevant information, from the analysis activity to provide the product or service of the potential customer base to send electronic promotional messages subject. According to Aberdeen's forecast marketing analysis, it is the two most commonly used marketing strategies to improve marketing product target and obtain a 360-degree customer analysis.

The two main themes of Aberdeen's research on marketing data analysis are helpful in analyzing the trend of large data market.

1. The best performing companies (i.e., market leaders) are more likely to contain predictive models of structured and unstructured data

2, market leaders are more likely to give enterprise users access rights, speed up the deployment time analysis model, and carry out a new application analysis

As the managers of European utilities say, data is the "fuel" of analysis, and to a large extent, the quality of the model depends on the quality of the data you hold. As a result, people will expect to have the ability to make extensive use of user data, to do predictive analysis, and in fact they do.

As the following illustration shows, market leaders are more likely to have a broader classification of data than their peers. Here are a few things to watch out for. The first is access to view transactions and behavioral data, which is particularly important in marketing because it provides operational insight, even if the buyer does not know and supports real-time application analysis such as online display of advertisements or products. The second is access to internal unstructured data (such as call center data) and external data (i.e., social media data).

Operable Insight

The second major trend in shaping the future of Big data marketing is ease of use and easy integration. The advantage here is to be able to get operational insights more quickly and actually implement them.

In Aberdeen's study, nearly half of the market leaders (46%) found that their predictive analytics solutions were easy to integrate with other application integration and business processes, while only One-fourth (26%) of the average enterprise enjoyed the same ease of integration.

Integration consolidation is critical because the prediction technology is rarely used for isolation. In order for the output prediction model to be effective, it is usually injected into the business process. For example, in marketing, it can be used to predict real-time applications to determine the next best offer, whether on a network or in a call-center environment. Similarly, for a direct mail activity, the output from the predictive model will be sent to the mail solution.

Overall, 45% per cent of respondents said that lack of key technologies or related mathematical skills was a major obstacle to the use of predictive analysis. When predictive modeling techniques are so scarce, one remedy is to make as many predictive modeling tasks as possible into the hands of the business manager.

Aberdeen's research found that market leaders have data mining tools that are twice times more common than the average enterprise (43% vs. 21%), which business managers can use to analyze without the help of a statistical expert. If the interface of the tool hides the underlying algorithm from the user, then a person with little or no knowledge of statistical analysis can directly use the predictive model. In this way, the enterprise users can master marketing expertise, even if there is little or no statistical knowledge, can also manipulate the model, directly improve marketing efficiency. Without this practical approach, marketing goals rely on dedicated predictive modeling and statisticians to help them assess their marketing campaigns and performance.

Provides solution templates and examples, and can also help marketers directly forecast models and accelerate path results. Templates, as building blocks, provide a springboard for marketers who lack in-depth technical knowledge and skills to complete applications faster.

Key changes

As for large data, marketing is just beginning to adapt the existing real-time data analysis and model into high-capacity, unstructured data, while high-capacity, unstructured data real-time unlock will improve business value, resulting in better ease of use and easy integration.

Traditional business analysis costs are high, both in terms of technology and talent limit the high value of their applications, high-volume use. The increased ease of use and cost reduction solution not only enables a wider audience to conduct market analysis, but also adds a wider range of usage cases.

(Responsible editor: Schpeppen)