“This is not a prophecy, but the physicist Barra Brazil, through research, suggests that 93% of human behavior is predictable, which means that when people conduct activities, they extend their observations to predictions and then go into control.”
That is to find out the specific characteristics of a set of data objects in big data, according to the classification into different groups to match a given customer, mainly using attributes and characteristics as the analysis of consumption trends and customer satisfaction for marketing.
Can clarify the value of the attribute in the data of the object, the direction formed by the change of time, and the relationship of mutual dependence, including the relationship between the forecasting trend of the order and the transaction, and apply to marketing, such as finding customers and maintaining existing customers. Preventing loss, which in turn leads to a targeted marketing direction for product cycle rate analysis or sales trend assessment.
It is to divide a group of data into each category according to similarity and difference. The purpose is to bring the same category closer to similarity, separate the dissimilarity, apply to customer group classification, attribute analysis, purchasing power prediction and market segmentation. The so-called clustering is to discover important customer attributes, to make things sorted, people to group, and to classify accordingly. For example, the banking sector conducts customer segmentation risk assessments for trusts or project investments.
Emphasizing the relevance analysis is the pursuit of ambiguity, rather than precise errors, to find out the association rules between data items in the database, that is, the occurrence of an event that may lead to another thing happening in the same situation or seemingly Interrelated affairs involve interesting interrelationships. Form non-associative association orientations hidden in the data, as product positioning, pricing, developing customer groups, managing risks, researching production, and influencing marketing effectiveness.
The selection of the main reasons for the characteristics of the data or the loss of business caused by the slow sales of the products is used as a reference.
Refers to an inconspicuous example of intriguing data, such as expected bias, and an exceptional answer to assess the difference in observations. This is for the enterprise crisis management, its abnormal tips to find out what kind of unexpected gains, and the unexpected regularity through the mining to detect various abnormal dynamics, analysis and evaluation, identification and other aspects of prevention.
Collect all industries, various market customers, behavioral data and all relevant analysis of competitors. This has a major impact on the business. According to the analysis, we can find out the problem that caused the crisis and solve it in advance.
In the era of big data, traditional commercial competition has long had no border disputes. It is more predictive of future data analysis. Through deep mining, it can predict future trends and user behavior predictions, and use known data to find unknowns. The answer is to convert the data into an impact index, and then make forward-looking knowledge decisions to seize the market.