The importance of Internet data analysis for optimizing customer experience

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The data analysis of the internet transforms the artificial perceptual judgment into quantitative analysis and plays an important role in enhancing the customer experience. I have always believed that the number of "language", the number is the most intuitive reflection of the performance of a measurement, and the idle pile of data is useless, only after the conscious processing, analysis can bring benefits to our work.

In particular, the trade on the Internet, we can not only from the number of orders to determine whether there is value. For example, the author is operating equipment manufacturing Network Marketing staff, daily needs to carry out a large number of promotional work, including blog promotion, micro-blog promotion, forum promotion, QQ Group promotion, soft text promotion and so on, and I am busy in the promotion of the results of how to measure?—— data analysis. Also such as Lotte.com is a South Korea's online shopping mall, with 13 million customers, the site's daily visits to nearly 1 million people. Managers found that the site each time the expected results of promotional activities are far below the user's response, the production of sales of goods is not high, which makes the staff very confused.

The importance of Internet data analysis

After realizing the importance of data analysis, it is necessary to put it into practice. Data analysis is not a simple summary of a large number of data, in this respect lotte.com do is still very good, you can refer to. The online shopping mall has established a customer behavior Analysis database that evaluates each visitor, the page visited, the way visitors browse the site, and the actions they take. In addition, the database captures user population information, cart size and conversion rates, order quantity, and intent quantity.

Compared with the simple analysis of the number of visitors, the site's data analysis system can be compared to each type of promotional activities of the actual visitors (e-mail, advertising, keywords, etc.) analysis of conversion rates (shopping cart, immediate purchase, intent list, procurement completed), so you can analyze the efficiency of the channel in detail. At the same time, you can confirm that each type of promotional activity visitor searches for the most frequently used keywords, locations, and purchased items. Page coverage allows you to measure the number of clicks and visitors per item in the page, measure the value of each position in the page, and identify hotspots and non hotspot areas.

With this function, the site can immediately replace the low flow of goods, optimize the page, and carry out targeted marketing. Further, the site will focus on data analysis from the process of placing orders extended to the shopping cart, in order to find obstacles to customer shopping links, improve conversion rate. By analyzing the movement mode and exit point of the visitor before shopping, we can forecast the customer's shopping mode and gain insight into each customer's behavior, demand and interest so as to promote the customer's purchase more effectively and improve the customer's satisfaction.

After the results of Internet data analysis, in practice, Lotte only by using data recognition analysis to give up the reason for the shopping cart, the first year's site sales increased by 10 million of dollars.

This is precisely the role of data analysis, which transforms artificial perceptual judgment into quantitative analysis and plays an important role in enhancing customer experience. In the Internet industry, data analysis of customer behavior is meaningful. For example, customers who behave similarly are clustered into the same class. Some netizens are professional women, their network browsing behavior patterns, such as time period, the content of the Web page, access to the channel and housewives are not the same.

In addition, the company is facing increasingly fierce market competition, will continue to have the problem of customer churn. And the characteristics of customer churn and model to do data analysis to know certain rules and characteristics. For example, an airline in the data analysis of the customer ordering process, people found that more customers will always be in a certain step before the pause, and finally give up, the results found that the Web page and hint content is unreasonable, mistakenly put the customer input "Invalid information" tip for "space has no", resulting in some business opportunities lost.

For Internet enterprises, through the analysis of customer online behavior data model, can find many opportunities for improvement, on the one hand, improve the customer experience, but also for the enterprise to bring tangible profits.

Eight important aspects of Internet data analysis

Generally, enterprises in the data analysis, the most important is the business problem-oriented, data-driven, technical support guiding principles. There are eight aspects in the application of data analysis technology.

First: Fixed statements. For example, regularly reflect the user visits, report product sales, etc.

Second: ad hoc query, support flexible query analysis, to meet the information needs outside the report. For example, see what customers have purchased some problem products to achieve active customer service.

Third: Multidimensional Analysis (OLAP), from a number of points of view to analyze the answer to the question. For example, from geographical areas, product lines, time, user groups and other angles to find the reasons for the decline in sales.

IV: Early warning function. For example, a quarterly sales revenue is not up to standard can be labeled as a highlight, or send a reminder message to focus attention.

V: statistical analysis. For example, through regression analysis, in the price, product, service, logistics efficiency and other factors to identify the key factors affecting customer satisfaction.

Sixth: Predictive analysis (forecasting). For example, predict whether the next one months will increase the number of Web site visits, how to increase the site's space and performance deployment, to meet business needs, to ensure that the user's service experience.

Seventh: Data Mining modeling analysis, based on network browsing behavior, segmentation customer base, looking for each customer group behavior characteristics and potential needs, push personalized information and services.

Eighth: Optimization analysis. To the whole process of online retail business, for example, according to the historical business demand, offline how to arrange inventory, to avoid the waste of inventory resources, but also to meet the demand for timely supply.

In the Internet transactions, the proportion of logistics links is self-evident, customers are very likely because of logistics services and give up or choose to buy products. Therefore, in the product delivery Logistics link, how to arrange the best route of distribution, in order to the fastest speed, less delivery costs to complete delivery. Again such as the communication channel aspect, the call center's customer service resource How to configure, can reduce the user to wait online, does not waste the user's time. In the Internet industry, enterprises often accumulate a lot of customer behavior and other data, to the data to value, the enterprise data assets into competitive advantage, some successful network enterprises have come out of the road, it is worth other enterprises to learn and draw lessons from.

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