Cross-border e-commerce marketing should be based on big data, and various problems in marketing should be solved through data, rather than just relying on experience. Specifically, the support that data can provide in cross-border e-commerce marketing includes:
1. User behavior and feature analysis
By accumulating enough user data, analyzing user preferences and purchasing habits, and achieving “better understanding of users than users themselves”. This is the premise and starting point of many big data marketing.
2. Precision marketing information push
Through the support of user feature data and detailed and accurate analysis, real precision marketing can be achieved. Today’s RTB (real-time bidding) advertising and other applications have shown better accuracy than before, and the support behind them is big data.
3. Guide products and marketing activities to cater to users
Through data analysis, understand the main characteristics of potential users and their expectations of products before product production,
Great support for enterprises.
Catch what they like from many aspects such as product design and promotion. Data analysis can gain insight into new markets and give enterprises the greatest support in grasping economic trends.
4. Competitor monitoring
Through big data monitoring and analysis, we can learn what competitors are doing and what layout they have, so as to understand the current situation of competitors and predict their future actions.
5. Brand communication and crisis monitoring
Brand communication trend analysis, content feature analysis, interactive user analysis, positive and negative emotion analysis, word-of-mouth category analysis, product attribute analysis, etc. can be carried out. We can also grasp the communication situation of competitors through monitoring, and refer to industry benchmarks for user planning.
During the outbreak of brand crisis, the most needed thing is to track the trend of crisis communication and identify important participants to facilitate rapid response. Big data can collect negative definition content, timely initiate crisis tracking and early warning, analyze the views of the incident process according to the social attributes of the crowd, identify key figures and communication paths, grasp the source and key nodes, so as to quickly and effectively deal with the crisis.
6. Key user screening
Through big data analysis of the websites that users mainly visit, the various types of content they publish on social media, and the content of interaction with others, we can find out which users are the most valuable, so as to help enterprises screen key target users. After grasping the status of key users using products through big data, the user experience can be improved in time.
7. Customer tiered management support
Big data can analyze the interactive content of active fans, set various rules for consumer portraits, associate potential users with member data, associate potential users with customer service data, screen target groups for precision marketing, and then combine traditional customer relationship management with social data, enrich user labels in different dimensions, and dynamically update consumer life cycle data to keep information fresh and effective.
8. Market forecasting and decision analysis support
The support of data for market forecasting and decision analysis was proposed in the past when data analysis and data mining were popular. Walmart’s famous “beer and diapers” case was a masterpiece at that time. The large scale and multi-type of data in the big data era have put forward new requirements for data analysis and data mining.