STATISTICAL ANALYSIS OF USER CHECK-IN BEHAVIOR AND ITS APPLICATION IN O2O MARKETING

Mobile network user group of location-based service is of great value for O2O marketing. As a result check-in, as one of the most important parts of Location-Based Social Networks, has been attached with great attention. Through reflecting users' real offline life mode, it can explore the law behind the check-in behavior, which will help merchants better realize marketing precision and consumers get more high quality services at the same time. This paper collected check-in data in 6 months from one of China's largest social networks by using a crawler. It also designed a useful and effective Max Matching Algorithm, which creatively recognized check-in records so as to match these locations with POIs in POI Semantic Hierarchy Model, since users name the locations by themselves at will. Finally, a Check-in Behavior Information Model for each user could be constructed. On this basis, the paper analyzed users' empirical check-in data making using relevant statistics technology, such as descriptive statistics and cluster analysis, and studied the individual's main geographical activity areas and check-in POI types, Chinese users' regularity of check-in time distribution, all user's Significant Scores, and the dependent relationship between User Experience and POI Interest, in order to reveal the underlying user life mode and find out the frequency of check-in behavior. This paper hopes to find out active users and hot merchants to realize merchants ranking recommendation and loyal and potential consumers searching, and help local merchants improve the precision of marketing and provide better services for customs.