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In retail operations, customer choices may be affected by stockout and promotion events. Given panel data with the transaction history of customers, and product availability and promotion data, our goal is to predict future individual purchases. We use a general nonparametric framework in which we represent customers by partial orders of preferences. In each store visit, each customer samples a full preference list of the products consistent with her partial order, forms a consideration set, and then chooses to purchase the most preferred product among the considered ones. Our approach involves: (a) defining behavioral models to build consideration sets as subsets of the products on offer, (b) proposing a clustering algorithm for determining customer segments, and (c) deriving marginal distributions for partial preferences under the multinomial logit model. Numerical experiments on real-world panel data show that our approach allows more accurate, fine-grained predictions for individual purchase behavior compared to state-of-the-art alternative methods. The online appendix is available at https://doi.org/10.1287/mnsc.2016.2683 . This paper was accepted by Vishal Gaur, operations management.

This study investigates how prior usage experience with various decision aids available in an Internet shopping environment contributes to online purchase behavior evolution. Four types of decision aids are examined: those for (1) nutritional needs, (2) brand preference, (3) economic needs, and (4) personalized shopping lists. We construct and estimate nonhomogeneous hidden Markov models of store- and category-level purchase decisions, in which parameters vary over time across hidden states as driven by usage experience with different decision aids. We find that consumers evolve through distinct behavioral states over time, and the evolution is attributable to their prior usage experience with various decision aids. Moreover, the impact varies by the specific decision aid, behavioral state, and category characteristics. In addition, consumers gravitate toward habitual decision processes in online grocery stores, and their average price and promotion sensitivities increase first and then decrease but the level of heterogeneity rises continuously. We identify beneficial versus potentially undesirable decision aids and demonstrate how the proposed research method can help online retailers improve their store environments, design customized promotions, and quantify the payoffs of these strategies.

Although customized price promotions are increasingly common in the marketplace, relatively little is known about how deal recipients evaluate them. The authors investigate the role of a promotional characteristic that has received little attention in the literature by examining whether consumers' responsiveness to a targeted discount is influenced by their perceptions of the deal's exclusivity (i.e., the degree to which the offer is available only to them or to other consumers as well). The results demonstrate that exclusive promotions may be viewed more, equally, or less favorably than inclusive offers, depending on several factors used in decisions about the delivery of targeted offers (e.g., customer demographics, transactional histories). Specifically, the authors find that exclusive deals are favored over inclusive offers (Study 1), a preference that is pronounced for consumers adopting independent self-construals (Study 2) and for male consumers with a history of purchasing from the marketer providing the offer (Study 3). These exclusivity effects are mediated by the ability of the promotional offer to allow consumers to engage in self-enhancement (Study 3).

The main objective of this paper is to provide a decision-support system of micro-level customized promotions, primarily for use in online stores. Our proposed approach utilizes the one-on-one and interactive nature of the Internet shopping environment and provides recommendations on when to promote how much to whom . We address the issue by first constructing a joint purchase incidence-brand choice-purchase quantity model that incorporates how variety-seeking/inertia tendency differs among households and change over time for the same household. Based on the model, we develop an optimization procedure to derive the optimal amount of price discount for each household on each shopping trip. We demonstrate that the proposed customization method could greatly improve the effectiveness of current promotion practices, and discuss the implications for retailers and consumer packaged goods companies in the age of Internet technology.

This paper investigated whether consumers differ in their perceptions of deal value, fairness and purchase intentions when presented with individually targeted (or customized) promotions versus universal promotions offered to all consumers at online retail websites. It was found that customized offers lead to significantly higher purchase intent compared to universal offers. Perceived fairness differed significantly across promotion types (free shipping, $ off and temporary reduced price) based on whether they were customized to the consumer or offered universally. Implications for designing customized online promotions are offered.

Background Reconstruction of mandibular defects following ablative surgery remains a challenge even for experienced surgeons. Virtual planning and guided surgery, including computer-aided design/computer-aided manufacturing (CAD/CAM), afford optimized ways by which to plan complex surgery. This study aimed to evaluate and compare aesthetic outcome and surgical efficiency of free fibular flap (FFF) with and without CAD/CAM customized osteotomy guide (COG) for reconstruction of onco-surgical mandibular defects. Methods Twenty-two patients indicated for segmental mandibulectomy were randomly assigned to either CAD/CAM with COG group or that without COG- Model based reconstruction (MB group) at a 1:1 ratio. Aesthetic outcomes were evaluated by means of morphometric assessment and comparison for each differential area (DAr) and angle (DAn) in the affected side to the contralateral side of the mandible using computerized digital imaging analysis (CDIA) based on the post-operative 3D CT-scan. Subjective evaluation was performed using the Visual Analogue Scale (VAS) and Patient’s Satisfaction Score (PSS). Surgical efficiency was a secondary outcome and evaluated as total operative time and ischemia time. Results The mean sagittal DAr was significantly lower in the COG group (277.28 ± 127.05 vs. 398.67 ± 139.10 mm 2 , P  = 0.045). Although there was an improvement in the axial DAr (147.61 ± 55.42 vs. 183.68 ± 72.85 mm 2 ), the difference was not statistically significant ( P  = 0.206). The mean differences (Δ) in both sagittal and coronal DAn were significantly lower in the COG group than in the MB group (6.11 ± 3.46 and 1.77 ± 1.12° vs. 9.53 ± 4.17 and 3.44 ± 2.34°), respectively. There were no statistically significant differences in the axial DAn between the two groups ( P  = 0.386). The PSS was significantly higher in the COG group, reflecting better aesthetic satisfaction than in the MB group ( P  = 0.041). The total operation and ischemia time were significantly shorter in favor of the COG group with a mean of (562.91 ± 51.22, 97.55 ± 16.80 min vs. 663.55 ± 53.43, 172.45 ± 21.87 min), respectively. Conclusion The CAD/CAM with COG is more reliable and highly valuable in enhancing aesthetic outcomes and surgical efficiency of mandibular reconstruction by FFF compared to that without COG (MB reconstruction). Trial registration This trial was registered at ClinicalTrials.gov . Registration number: NCT03757273. Registration date: 28/11/2018.

The authors assess how and why retailer-customized coupon campaigns affect customer purchases. The conceptual model proposes effects on trip incidence and revenues through the mere exposure to campaigns (exposure effect) and the redemption of coupons (redemption effect). The authors propose monetary savings of the coupons, regularity of the campaigns, and coupon fit with customer preferences as moderators. Analysis of data from a group of regional grocery chains that were part of a quasi experiment demonstrates that retailer-customized coupon campaigns have a positive exposure and redemption effect on customer purchases. Mere exposure to customized coupon campaigns contributes more than coupon redemption to campaign returns. Consistent with theoretical expectations, customized coupon campaigns are more effective if they provide more discounts, are unexpected, and are positioned as specially selected for and customized to consumer preferences. The substantial exposure effects suggest that managers should look beyond redemption rates and also consider sales lift from nonredeemers when measuring the effectiveness of customized coupon campaigns.

Background Responsible gambling messages are widely used as a tool to enable informed choice and encourage appropriate gambling behavior. It is generally accepted that gamblers have different levels of risk of developing gambling problems and require various harm minimization tools and resources. Therefore, it is reasonable to expect that responsible gambling messages should be customized and target specific groups of gamblers. This project aimed to understand hypothesized differences between cohorts of gamblers and receive qualitative feedback on archetypal targeted messages used to increase use of responsible gambling tools. Methods Focus groups were held to test messages for specific cohorts: young adults (18–24 years), seniors (60+ years), frequent gamblers (weekly), and gamblers of skill-based games (poker, sports betting). Results Cohorts exhibited different preferences and responses to message archetypes. Seniors preferred messages about limit setting, whilst young adults and frequent gamblers responded to messages about their own play and expertise. Skill game gamblers were interested in the odds of winning and their own outcomes over time. However, all groups agreed that using positive, non-judgmental language in messaging is important. Conclusions This research makes an important contribution to the field by demonstrating that the wording of message content will likely influence the effectiveness of such messages differentially across various groups of gamblers for engaging gamblers in harm reduction tools. Guidance is provided on themes that can be used by public health marketers.

Background Physical inactivity is prevalent, leading to a high burden of disease and large healthcare costs. Thus, there is a need for affordable, effective and scalable interventions. However, interventions that are affordable and scalable are beset with modest effects and engagement. Interventions that integrate machine learning with real-time data to offer unprecedented levels of personalisation and customisation might offer solutions. The aim of this study is to conduct a randomised controlled trial to evaluate the effectiveness of a machine learning and app-based digital assistant to increase physical activity. Methods One hundred and ninety-eight participants will be recruited through Facebook advertisements and randomly allocated to an intervention or control group. Intervention participants will gain access to an app-based physical activity digital assistant that can learn and adapt in real-time to achieve high levels of personalisation and user engagement by virtue of applying a range of machine learning techniques (i.e. reinforcement learning, natural language processing and large language models). The digital assistant will interact with participants in 3 main ways: (1) educational conversations about physical activity; (2) just-in-time personalised in-app notifications (‘nudges’), cues to action encouraging physical activity and (3) chat-based questions and answers about physical activity. Additionally, the app includes adaptive goal setting and an action planning tool. The control group will gain access to the intervention after the last assessment. Outcomes will be measured at baseline, 3 and 6 months. The primary outcome is device-measured (Axivity AX3) moderate-to-vigorous physical activity. Secondary outcomes include app engagement and retention, quality of life, depression, anxiety, stress, sitting time, sleep, workplace productivity, absenteeism, presenteeism and habit strength. Discussion The trial presents a unique opportunity to study the effectiveness of a new generation of digital interventions that use advanced machine learning methods to improve physical activity behaviour. By addressing the limitations of existing conversational agents, we aim to pave the way for more effective and adaptable interventions. Trial registration Australian New Zealand Clinical Trial Registry ACTRN12624000255583p. Registered on 14 March 2024. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=387332 .

This study empirically estimates the expected basket-level demand effects, as well as the expected store profit effects, of three different customization levels of retailer promotions. Using data from a national grocery retailer in the U.S., we estimate a household’s contemporaneous purchase incidence outcomes in 28 frequently shopped categories. Estimating the cross-category dependencies in purchase incidence as a function of exposure to levels of customized promotions, allows us to measure the effect of each campaign on expected retailer profit and implement prescriptive analytics to identify the appropriate multi-level coupon mix for maximizing profits. We find all three levels of coupon customization result in per-customer returns, but that medium customization leads to the highest incremental expected profit, while high customization generates the highest expected profit. The results provide insights to retailers about investing in more customized promotional efforts, with a detailed cross-category perspective into where such value is gained.