The biggest challenge facing enterprises is no longer competitors, but customers.
Understanding customers is the primary issue of customer management. We can get to know customers through customer portraits, and judge the possibility of customer transactions by analyzing their purchasing behavior, thereby forming customer insights.
Customer-centricity has become a consensus for refined operations of cross-border e-commerce, and accurately understanding customers is the primary task of refined operations. Empowered by big data technology, merchants can use customer portrait technology to deeply understand customers. Customer portraits are customer data obtained by merchants using search engines, e-commerce platforms, social networks and other applications, labeling customer information, and using label sets to describe customer group characteristics from multiple dimensions, statistics, and mining potential value information, thereby abstracting the overall picture of customer information. Customer portraits are virtual representatives of real customers and are customer models based on a series of real data. For example, Alibaba International Station has set up more than 300 labels to define customer characteristics, covering basic customer attributes, purchasing power, behavioral characteristics, social characteristics, psychological characteristics, interest preferences and other aspects. Compared with traditional customer portraits, data related to browsing, orders, customer service, delivery and logistics related to e-commerce transactions can be introduced into the modeling process of customer portraits, so as to more accurately depict the comprehensive characteristics of customers.
The customer portrait example lays the foundation for data-driven operations. Customer customer portraits are a prerequisite for targeted advertising and personalized recommendations. Portraits enable marketing systems such as search, recommendation, and advertising to serve customers more intelligently. For example, using customer portraits, when customers search, you can infer the customer’s shopping intentions, and recommend products that meet the customer’s preferences based on the customer’s attribute characteristics, personality traits or behavioral habits. This recommendation method is called “one thousand faces for one thousand people.”