Every customer is unique. If you look at each individual customer carefully, everyone has different characteristics. Whether it is basic customer files, multiple identities, feature tags, consumption records, and interactive records, these information are equally important and work together to form a 360° profile of a customers.

Insights based on the data of each individual customer does not mean that marketing must treat everyone differently. What is more practical is to treat each group of people and each group of "similar" people differently. This requires the brand to have the ability to segment the customers. People with one or more identical characteristics constitute a segmentation. Segmentation is the goal of most precision marketing and the granularity of customer characteristic analysis. Specific segmentation capabilities are the basis for analysis, insight and precision marketing.

Customer characteristics analysis helps brands answer the following questions: the distribution and number of the population of different characteristic dimensions (such as demographic attributes, consumption habits, membership levels) changes in the number of people who meet specific characteristics within a certain period of time.

Consumer behavior is the willingness expressed by customers with "money", so the displayed customer characteristics are more authentic and credible, and have a higher weight in data analysis. Through consumption behavior data, brands can understand customers' purchasing power, buying habits (from the perspective of time, place, and channel), and product or service preferences (brand, category, style, and other preferences). Combined with specific data mining models, brands can also predict the possibility of future consumption based on historical consumption behavior.

The consumer behavior data is of great value, but the magnitude is much fewer than the non-consumer behavior data. After having the ability to collect and organize non-consumer behavior data, brands need to classify, process, and analyze the large amount of accumulated data to form customer insights. These insights can better optimize the strategies that lead to consumption conversion, or compare the consumption and non-consumption characteristics of the same crowd to form new insights (for example, discover potential high-consumption groups).

After having data and analysis tools of different dimensions, it is necessary to further combine the analysis of different dimensions to generate new insights.
For example:
- Analyze the results of the same-dimensional analysis of different groups of people, and find out the relationship between group differences or behavioral performance and group characteristics.
- Within a certain period of time, the number of characteristic customer changes, the behavior or the trend of changes in characteristics
- In the specific behavior analysis path, drill down on the data of a specific step or customer to find the next level of characteristics and behavior reasons