Regularity Measure and Influence Weight for Analysis and Visualization of Consumer´s Attitude

Recently, analysis of massive purchase histories is thought to be effective for marketing science. For successful sales strategies and advertisements, understanding of consumer´s attitude (motivation toward purchasing) is important. However, marketers could obtain merely static attitude for the limited consumers using subjective questionnaires. We focus on purchase rhythm (regularity) as one approach to automatically extracting dynamic attitude transition. We propose "regular behavior measure" which quantifies how well each purchase corresponds to the user´s own daily habits. It considers non-periodic habits as well as periodic habits using multiple probabilistic models, we consider purchase probability and the number of purchased dates simultaneously. We also define "influence weight" to quantify whether each item is purchased on a fixed day of the week (DoW) or year-month (YM) using Normalized Mutual Information. We analyze actual purchase data to show the effectiveness of our visualization that considers consumer´s regularity transitions.