In an era of increasingly fierce competition, how to capture the hearts of buyers seems to be a problem that every e-commerce company must face. Buyer portraits can solve this problem very well. In essence, buyer portraits are virtual representatives of buyers, that is, through the understanding and analysis of various aspects of buyer information, a unique and personalized portrait is formed.
Early buyer portraits were very similar to personal profile information and were not particularly complex, so the differentiation and usability were relatively poor. However, as big data technology became more and more mature, the amount of data also increased significantly. E-commerce companies can obtain a large amount of buyer behavior data and give full play to the role of buyer portraits.
Generally speaking, a typical buyer portrait mainly includes dimensions such as gender, age, preferences, consumption habits, place of residence, occupation, and interests. Of course, if e-commerce companies want to make buyer portraits more accurate, they can continue to subdivide based on these dimensions.
Some e-commerce companies may not know how to build buyer portraits. In fact, this work is not difficult. Specifically, you can start from the following four aspects, as shown in Figure 2-3.
(1) Clarify the direction or classification
For whom to draw the portrait? What kind of portrait should be drawn? Why should such a portrait be drawn? What kind of portrait classification and results will there be? These are all issues that e-commerce companies must consider. Considering these issues clearly can not only ensure the systematization and structuring of buyer portraits, but also enhance the practicality of buyer portraits.
(2) Collect buyer data
Clearing the direction or classification of buyer portraits is equivalent to obtaining important information, such as buyers’ consumption habits, time of product purchase, and frequency of purchase. In order to ensure the scientific nature of buyer portraits, e-commerce companies must collect real, reliable, and effective data, and those irrelevant data can be appropriately discarded.
(3) Study buyer labels and comprehensive modeling
Buyer labels often need to rely on big data technology for comprehensive modeling to obtain. Assuming that the core buyer of an e-commerce company is a mother, then the e-commerce company cannot label her based on a certain shopping behavior of the mother, but should conduct comprehensive modeling based on her shopping frequency, consumption ratio, shopping time and other information.
(4) Pay attention to buyer privacy
Establishing buyer portraits is an important means for e-commerce companies to increase sales, but privacy protection must be paid attention to in the process of establishing buyer portraits. For example, e-commerce companies can use data analysis to accurately label buyers, but they must never sell or give this data to other industries and sectors.
Zhang Xiaofeng is the founder of an e-commerce company and has opened several flagship stores on Taobao. Since starting his business, he has set out to build a big data system to provide data support for buyer portraits. At present, Zhang Xiaofeng has recorded nearly 50,000 pieces of buyer shopping habits data and labeled buyers with multiple labels. Not only that, he also divides buyers into six types, namely practicalists, super fans, fashion control, fetishists, literary and artistic people, and perfectionists. With this approach, the sales of his flagship store have increased by nearly 20%.
Tips
The importance and role of buyer portraits are self-evident. In this case, e-commerce companies should collect and analyze buyer data and strive to establish buyer portraits as soon as possible.