Detecting multi-timescale consumption patterns from receipt data: A non-negative tensor factorization approach
This work addresses the high-dimensional challenge of understanding consumer behavior for marketing and economic policy management, though it appears incremental as it applies an existing method to a new dataset.
The researchers tackled the problem of detecting multi-timescale consumption patterns from receipt data by developing a non-negative tensor factorization method, which successfully extracted intra- and inter-week expenditure patterns to characterize consumers based on correlated consumption behaviors.
Understanding consumer behavior is an important task, not only for developing marketing strategies but also for the management of economic policies. Detecting consumption patterns, however, is a high-dimensional problem in which various factors that would affect consumers' behavior need to be considered, such as consumers' demographics, circadian rhythm, seasonal cycles, etc. Here, we develop a method to extract multi-timescale expenditure patterns of consumers from a large dataset of scanned receipts. We use a non-negative tensor factorization (NTF) to detect intra- and inter-week consumption patterns at one time. The proposed method allows us to characterize consumers based on their consumption patterns that are correlated over different timescales.