Understanding customer malling behavior in an urban shopping mall using smartphones

Abstract This paper presents a novel customer malling behavior modeling framework for an urban shopping mall. As an automated computing framework using smartphones, it is designed to provide comprehensive understanding of customer behavior. We prototype the framework in a real-world urban shopping mall. Development consists of three steps; customer data collection, customer trace extraction, and behavior model analysis. We extract customer traces from a collection of 701-hour sensor data from 195 in-situ customers who installed our logging application at Android Market. The practical behavior model is created from the real traces. It has a multi-level structure to provide the holistic understanding of customer behavior from physical movement to service semantics. As far as we know, it is the first work to understand complex customer malling behavior in offline shopping malls

Sandra Helps You Learn: The More You Walk, The More Battery Your Phone Drains

Emerging continuous sensing apps introduce new major factors governing phones' overall battery consumption behaviors: (1) added nontrivial persistent battery drain, and more importantly (2) different battery drain rate depending on the user's different mobility condition. In this paper, we address the new battery impacting factors significant enough to outdate users' existing battery model in real life. We explore an initial approach to help users understand the cause and effect between their physical activity and phones' battery life. To this end, we present Sandra, a novel mobility-aware smartphone battery information advisor, and study its potential to help users redevelop their battery model. We perform an extensive explorative study and deployment for 30 days with 24 users. Our findings reveal what they essentially learned, and in which situations they found Sandra very helpful. We share the lessons learned to help in the design of future mobility-aware battery advisors.1

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Smartphones as steady companions: device use in everyday life and the economics of attention

This thesis investigates smartphone use in naturally occurring contexts with a dataset comprising 200 hours of audio-visual first-person recordings from wearable cameras, and self-confrontation interview video footage (N = 41 users). The situated context in which smartphone use takes place has often been overlooked because of the technical difficulty of capturing context of use, actual action of users, and their subjective experience simultaneously. This research project contributes to filling this gap, with a detailed, mixed-methods analysis of over a thousand individual phone engagement behaviours (EB). We observe that (a) the smartphone is a key structuring element in the flow of daily activities. Participants report complex strategies on how they manage engaging with or avoiding their devices. (b) Unexpectedly, we find that the majority of EB (89%) are initiated by users, not devices; users engage with the phone roughly every five minutes regardless of the context they are in. (c) A large portion of EB seems to stem from contextual cues and an unconscious urge to pick up the device, even when there is no clear reason to do so. d) Participants are surprised about, and often unhappy with how frequently they mindlessly reach for the phone. Our in-depth analysis unveils several overlapping layers of motivations and triggers driving EB. Monitoring incoming notifications, managing time use, responding to social pressures, actually completing a task with the phone, design factors, unconscious urges, as well as the accessibility of the device, and most importantly its affordance for distraction all contribute to picking up the phone. This user drive for EB is used by providers to feed the attention economy. So far, keeping the smartphone outside of the visual field and immediate reach has appeared as the only efficient strategy to prevent overuse