More Like this

1. Data science for pedestrian and high street retailing as a framework for advancing urban informatics to individual scales

   https://doi.org/10.1007/s44212-022-00009-x  

   Torrens, Paul M. ( October 2022 , Urban Informatics)

   In this paper, we consider the applicability of the customer journey framework from retailing as a driver for urban informatics at individual scales within urban science. The customer journey considers shopper experiences in the context of shopping paths, retail service spaces, and touch-points that draw them into contact. Around this framework, retailers have developed sophisticated data science for observation, identification, and measurement of customers in the context of their shopping behavior. This knowledge supports broad data-driven understanding of customer experiences in physical spaces, economic spaces of decision and choice, persuasive spaces of advertising and branding, and inter-personal spaces of customer-staff interaction.

   We review the literature on pedestrian and high street retailing, and on urban informatics. We investigate whether the customer journey could be usefully repurposed for urban applications. Specifically, we explore the potential use of the customer journey framework for producing new insight into pedestrian behavior, where a sort of empirical hyperopia has long abounded because data are always in short supply.

   Our review addresses how the customer journey might be used as a structure for examining how urban walkers come into contact with the built environment, how people actively and passively sense and perceive ambient city life as they move, how pedestrians make sense of urban context, and how they use this knowledge to build cognition of city streetscapes. Each of these topics has relevance to walking studies specifically, but also to urban science more generally. We consider how retailing might reciprocally benefit from urban science perspectives, especially in extending the reach of retailers' insight beyond store walls, into the retail high streets from which they draw custom.

   We conclude that a broad set of theoretical frameworks, data collection schemes, and analytical methodologies that have advanced retail data science closer and closer to individual-level acumen might be usefully applied to accomplish the same in urban informatics. However, we caution that differences between retailers’ and urban scientists’ viewpoints on privacy presents potential controversy.

    

   more » « less
2. Smart and Sentient Retail High Streets

   https://doi.org/10.3390/smartcities5040085  

   Torrens, Paul M. ( December 2022 , Smart Cities)

   Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public. 

   more » « less
3. Changing an Electrical and Computer Engineering Department Culture from the Bottom Up: Action Plans Generated from Faculty Interviews

   https://doi.org/10.18260/1-2--34273  

   Frickey, Elise ; Rover, Diane ; Zambreno, Joseph ; Khokhar, Ashfaq ; Jacobson, Douglas ; Larson, Lisa ; Shelley, Mack ( June 2020 , 2020 ASEE Virtual Annual Conference)

   Changing Electrical and Computer Engineering Department Culture from the Bottom Up: Action Plans Generated from Faculty Interviews We prefer a Lessons Learned Paper. In a collaborative effort between a RED: Revolutionizing Engineering and Computer Science Departments (RED) National Science Foundation grant awarded to an electrical and computer engineering department (ECpE) and a broader, university-wide ADVANCE program, ECpE faculty were invited to participate in focus groups to evaluate the culture of their department, to further department goals, and to facilitate long-term planning. Forty-four ECpE faculty members from a large Midwestern university participated in these interviews, which were specifically focused on departmental support and challenges, distribution of resources, faculty workload, career/family balance, mentoring, faculty professional development, productivity, recruitment, and diversity. Faculty were interviewed in groups according to rank, and issues important to particular subcategories of faculty (e.g., rank, gender, etc.) were noted. Data were analyzed by a social scientist using the full transcript of each interview/focus group and the NVivo 12 Qualitative Research Software Program. She presented the written report to the entire faculty. Based on the results of the focus groups, the ECpE department developed an action plan with six main thrusts for improving departmental culture and encouraging departmental change and transformation. 1. Department Interactions – Encourage open dialogue and consider department retreats. Academic areas should be held accountable for the working environment and encouraged to discuss department-related issues. 2. Mentoring, Promotion, and Evaluation – Continue mentoring junior faculty. Improve the clarity of P&T operational documents and seek faculty input on the evaluation system. 3. Teaching Loads – Investigate teaching assistant (TA) allocation models and explore models for teaching loads. Develop a TA performance evaluation system and return TA support to levels seen in the 2010 timeframe. Improvements to teaching evaluations should consider differential workloads, clarifying expectations for senior advising, and hiring more faculty for undergraduate-heavy areas. 4. Diversity, Equity, and Inclusion – Enact an explicit focus on diversity in hiring. Review departmental policies on inclusive teaching and learning environments. 5. Building – Communicate with upper administration about the need for a new building. Explore possibilities for collaborations with Computer Science on a joint building. 6. Support Staff – Increase communication with the department regarding new service delivery models. Request additional support for Human Resources, communications, and finance. Recognize staff excellence at the annual department banquet and through college/university awards. 

   more » « less
4. Dynamic Assortment Optimization for Reusable Products with Random Usage Durations

   https://doi.org/10.1287/mnsc.2019.3346  

   Rusmevichientong, Paat ; Sumida, Mika ; Topaloglu, Huseyin ( July 2020 , Management science)

   We consider dynamic assortment problems with reusable products, in which each arriving customer chooses a product within an offered assortment, uses the product for a random duration of time, and returns the product back to the firm to be used by other customers. The goal is to find a policy for deciding on the assortment to offer to each customer so that the total expected revenue over a finite selling horizon is maximized. The dynamic-programming formulation of this problem requires a high-dimensional state variable that keeps track of the on-hand product inventories, as well as the products that are currently in use. We present a tractable approach to compute a policy that is guaranteed to obtain at least 50% of the optimal total expected revenue. This policy is based on constructing linear approximations to the optimal value functions. When the usage duration is infinite or follows a negative binomial distribution, we also show how to efficiently perform rollout on a simple static policy. Performing rollout corresponds to using separable and nonlinear value function approximations. The resulting policy is also guaranteed to obtain at least 50% of the optimal total expected revenue. The special case of our model with infinite usage durations captures the case where the customers purchase the products outright without returning them at all. Under infinite usage durations, we give a variant of our rollout approach whose total expected revenue differs from the optimal by a factor that approaches 1 with rate cubic-root of Cmin, where Cmin is the smallest inventory of a product. We provide computational experiments based on simulated data for dynamic assortment management, as well as real parking transaction data for the city of Seattle. Our computational experiments demonstrate that the practical performance of our policies is substantially better than their performance guarantees and that performing rollout yields noticeable improvements. 

   more » « less
5. Distribution-Free Contextual Dynamic Pricing

   https://doi.org/10.1287/moor.2023.1369  

   Luo, Yiyun ; Sun, Will Wei ; Liu, Yufeng ( May 2023 , Mathematics of Operations Research)

   Contextual dynamic pricing aims to set personalized prices based on sequential interactions with customers. At each time period, a customer who is interested in purchasing a product comes to the platform. The customer’s valuation for the product is a linear function of contexts, including product and customer features, plus some random market noise. The seller does not observe the customer’s true valuation, but instead needs to learn the valuation by leveraging contextual information and historic binary purchase feedback. Existing models typically assume full or partial knowledge of the random noise distribution. In this paper, we consider contextual dynamic pricing with unknown random noise in the linear valuation model. Our distribution-free pricing policy learns both the contextual function and the market noise simultaneously. A key ingredient of our method is a novel perturbed linear bandit framework, in which a modified linear upper confidence bound algorithm is proposed to balance the exploration of market noise and the exploitation of the current knowledge for better pricing. We establish the regret upper bound and a matching lower bound of our policy in the perturbed linear bandit framework and prove a sublinear regret bound in the considered pricing problem. Finally, we demonstrate the superior performance of our policy on simulations and a real-life auto loan data set. Funding: Y. Liu and W.W. Sun acknowledge support from the National Science Foundation Division of Social and Economic Sciences [Grant NSF-SES 2217440]. Supplemental Material: The supplementary material is available at https://doi.org/10.1287/moor.2023.1369 . 

   more » « less