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Salma Akter
16 de jul. de 2022
In Discussões Gerais
Many companies have started the simultaneous b2b email list operation mode of online and offline products, and the trend of online and offline omni-channel is very obvious. Offline companies are constantly trying online e-commerce sales channels. When operating online and offline products, we need to analyze marketing data, understand the effect of online and offline product operations through the data, and summarize experience. There is a popular saying in the Internet industry: "Second-rate merchants look at traffic, and first-class merchants look at data." How to analyze online and offline marketing data when operating products online and offline? After several years of development, it has b2b email list become an internationally renowned clothing brand. Uniqlo has introduced a hypermarket-style clothing sales method and adopted a unique product planning, development and sales system to achieve low-cost store operations, thus triggering a hot sale. Uniqlo takes into account that China's e-commerce system is becoming more and more mature, and the advantages of e-commerce compared with the sports store model are becoming more and more obvious. However, it is impossible for traditional companies like Uniqlo to abandon the brick-and-mortar store model. On the one hand, brick-and-mortar stores have the effect of offline advertising; on the other hand, traditional companies will build their own e-commerce channels when relying on third-party platforms. to increase initiative. As a result, Uniqlo launched a free delivery campaign in China. This is a global project of Uniqlo, which is held in different countries every year. This activity distributes new clothes to users for free through a combination of online and offline methods. In 2013 and 2014, Uniqlo gave away 100,000 and 200,000 new clothes for free worldwide.
Operation Pocket Notes: How b2b email list to Do Data Operation 
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Salma Akter
16 de jul. de 2022
In Discussões Gerais
Many companies often use the pirate model (AARRR) in email list their operations. The first A is Acquisition, and the Chinese name is Customer Acquisition. We generally understand it as pulling new. In the past few years, the company's new promotion was rude, and it was done by spending money to buy volume. Today, as the Internet traffic dividend is declining, the company's new cost is increasing step by step. After all, the real money is spent, and the same is true. The amount of money that may be pulled back is less than one-tenth of the previous amount. Therefore, for new users who come in through external channels (inter-industry cooperation to exchange volume, purchase volume) or natural channels, Classification of user data: To analyze users, you email list must first have data. For users, there are generally data in attribute fields and behavior fields. The data of the attribute domain generally refers to the data that records the basic attribute characteristics of the user, such as gender, age, occupation, etc. If you have downloaded and used some financial management software, you must have filled out questionnaires to record user attributes, such as risk appetite, annual household asset income, etc. The data in the behavior domain generally refers to the data that records the user behavior track log, such as when the user accesses what function of the product. 1. Attribute Domain In the attribute domain, we can analyze the relationship between new users and retention of different attributes, so as to carry out refined operations. This is relatively simple and will not be expanded today. We focus on the competitive analysis in the attribute domain. 1.1 [Competitive Analysis] Competitive product analysis generally looks at the various apps installed on users’ mobile phones. These apps can usually be divided into two categories: competing products and non-competing products. For this type of data, we generally make a correlation diagram between user installation and retention, that is, among users who use our products:
A must-see for email list operators: Several approaches to new user analysis
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Salma Akter

Salma Akter

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