Optimal policy design for the sugar tax

Healthy nutrition promotions and regulations have long been regarded as a tool for increasing social welfare. One of the avenues taken in the past decade is sugar consumption regulation by introducing a sugar tax. Such a tax increases the price of extensive sugar containment in products such as soft drinks. In this article we consider a typical problem of optimal regulatory policy design, where the task is to determine the sugar tax rate maximizing the social welfare. We model the problem as a sequential game represented by the three-level mathematical program. On the upper level, the government decides upon the tax rate. On the middle level, producers decide on the product pricing. On the lower level, consumers decide upon their preferences towards the products. While the general problem is computationally intractable, the problem with a few product types is polynomially solvable, even for an arbitrary number of heterogeneous consumers. This paper presents a simple, intuitive and easily implementable framework for computing optimal sugar tax in a market with a few products. This resembles the reality as the soft drinks, for instance, are typically categorized in either regular or no-sugar drinks, e.g. Coca-Cola and Coca-Cola Zero. We illustrate the algorithm using an example based on the real data and draw conclusions for a specific local market

Leveraging data rich environments using marketing analytics

With the onset of what is popularly known as “big data”, increased attention is being paid to creating value from these data rich environments. Within the field of marketing, the analysis of customer and market data supported by models is known as marketing analytics. The goal of these analyses is to enhance managerial decision making regarding marketing problems. However, before these data rich environments can be used to guide managerial decision making, firms need to grasp the process of doing so. Therefore, in this thesis we explore two opportunities and one challenge that firms are faced with in this process. The first opportunity we identify is the possibility to get enhanced insights on own and competitors’ market behavior. Here, the difference in reactions to competing strategic and tactical marketing actions is investigated, in order to improve future decision making in the face of competitive response. The second opportunity identified is the possibility to engage in real-time marketing. Using ideas from statistical quality control, a control chart method to track customer purchase behavior is developed. Using this approach, firms can decide what the best time to approach a customer with a marketing action is, increasing the relevance and effectiveness of such actions. The challenge investigated is that of maintaining customer privacy when using marketing analytics. In the setting of customer churn prediction, it is shown that firms can still perform effective analyses of customer churn while maintaining customer privacy. The method developed in this chapter assures this

No Future Without the Past? Predicting Churn in the Face of Customer Privacy

For customer-centric firms, churn prediction plays a central role in churn management programs. Methodological advances have emphasized the use of customer panel data to model the dynamic evolution of a customer base to improve churn predictions. However, pressure from policy makers and the public geared to reducing the storage of customer data has led to firms' self-policing' by limiting data storage, rendering panel data methods infeasible. We remedy these problems by developing a method that captures the dynamic evolution of a customer base without relying on the availability past data. Instead, using a recursively updated model our approach requires only knowledge of past model parameters. This generalized mixture of Kalman filters model maintains the accuracy of churn predictions compared to existing panel data methods when data from the past is available. In the absence of past data, applications in the insurance and telecommunications industry establish superior predictive performance compared to simpler benchmarks. These improvements arise because the proposed method captures the same dynamics and unobserved heterogeneity present in customer databases as advanced methods, while achieving privacy preserving data minimization and data anonymization. We therefore conclude that privacy preservation does not have to come at the cost of analytical operations. (C) 2016 Elsevier B.V. All rights reserved.</p

Competitive reactions to personal selling:the difference between strategic and tactical actions

A recurring question facing managers is how (if at all) to react to competitive actions. In this research the authors distinguish between reactions to competing strategic and competing tactical actions, different from prior homogeneous definitions of competitive actions. Using a unique, single-source dataset of personal selling interactions between firms and customers covering fourteen drug categories, the authors shows that substantial differences in reactions exist. In particular, strategic actions elicit competitive responses with stronger short- and long-term consequences compared to tactical actions. Furthermore, while the decision to react to competing strategic actions is always warranted, this is not the case for a substantial amount of tactical actions, where firms retaliate with an ineffective marketing instrument, or accommodate with an effective marketing instrument. This divide between actions is further exacerbated in the strength of the reactions that we observe: stronger or weaker reactions to strategic actions occur in line with theoretical expectations, whereas reactions to tactical actions often are not. Based on these findings, the authors suggest directions to improve decision maker’s reactions to competing tactical action

Niet-routinematige vaardigheden in hbo-profielen

Door automatisering en globalisering veranderen de taken van werkenden op deNederlandse arbeidsmarkt. Routinematige taken kunnen door machines worden uitgevoerd of worden uitbesteed aan het buitenland. Dit geldt vooralsnog niet voor niet-routinematige taken, die soms zelfs talrijker en complexer kunnen worden als gevolg van de toenemende automatisering en globalisering. Doordat taken veranderen, verandert ook de vraag naar vaardigheden door werkgevers die niet-routinematige vaardigheden (ook wel brede vaardigheden genaamd zoals creativiteit, analytisch vermogen en communicatieve vaardigheden) steeds meer waarderen. Instellingen in het hoger onderwijs moeten inspelen op deze veranderende vraag. In dit onderzoek bieden wij, aan de hand van tekstanalyses, voor het eerst een kwantificering aan van de mate waarin niet-routinematige vaardigheden verwerkt zijn in de huidige profielomschrijvingen van hbo-opleidingen. Daarnaast onderzoeken wij de mate waarin afgestudeerden van die opleidingen aangeven dat zij daadwerkelijk over deze vaardigheden bezitten en de mate waarin deze vaardigheden worden vereist door hun werkgevers. Deze multimethode aanpak vergroot zodoende het zicht op het aanbod van niet-routinematige vaardigheden onder hbo-afgestudeerden enerzijds en de vraag naar deze vaardigheden door de arbeidsmarkt anderzijds. Onze tekstanalyse-aanpak van onderwijsprofielen is vernieuwend en wordt voor het eerst toegepast in deze context. Dit onderzoek biedt dan ook een proof of concept voor toepassing in andere domeinen

Timing customer reactivation initiatives

Firms operating in non-contractual settings apply customer reactivation initiatives such as email messages to stimulate customers who have become inactive temporarily or permanently to resume their transaction activities. Thus, firms need to know which customers are inactive, and when a customer becomes inactive. Existing approaches struggle to distinguish active from inactive customers and do not provide time-scale estimates of when to send reactivation mails. To address these shortcomings, we develop an approach to target and time the sending of reactivation mails. Building on control chart methods, we introduce a gamma–gamma control chart, modelling the average customer interpurchase time and the variation therein to determine activity boundaries. Crossing these boundaries signals a potential change in a customer's purchasing activity, providing a signal to initiate customer reactivation. A field experiment in the greetings and gifts industry, supported by several additional analyses, illustrates the improved performance of our approach when it comes to signaling customer activity against a wide range of competing models. The improved performance of our method occurs particularly in settings where customers vary strongly in purchase and inactivity patterns.</p

Timing Customer Reactivation Interventions

Firms operating in non-contractual settings use customer reactivation initiatives to stimulate customers that have ceased purchasing to resume purchasing. Customer reactivation requires these firms to identify which customers have become inactive at what point in time, and approach them with a marketing message. Existing approaches struggle to separate active from inactive customers, and do not provide calendar time estimates of when to send a reactivation message. Addressing these shortcomings, we develop an approach to target and time the sending of reactivation messages. Building on control chart methods, we introduce a gamma-gamma control chart, modeling the average customer interpurchase time and the variation therein to determine inactivity boundaries. Crossing these boundaries generates an inactivity signal, which should trigger reactivation. A field-test in the greetings and gifts industry illustrates that this approach increases activity by 1.9 - 3.5 percentage points. Additionally, our approach increases incremental activity by 111% and incremental revenue by 38%. Finally, timely targeting is important, as targeting customers earlier than their expected purchase time reduces their activity compared to customers targeted on time

Timing Customer Reactivation Initiatives

Firms operating in non-contractual settings use customer reactivation initiatives to stimulate inactive customers to resume purchasing. Customer reactivation requires these firms to identify which customers have become inactive at what point in time, and then approach them through direct mail or e-mail. Existing approaches struggle to separate active from inactive customers, and do not provide calendar time estimates of when to send a reactivation mailing. Addressing these shortcomings, we develop an approach to target and time the sending of reactivation mailings. Building on control chart methods, we introduce a gamma–gamma control chart, modeling the average customer interpurchase time and the variation therein to determine inactivity boundaries. Crossing these boundaries generates an inactivity signal, which should trigger reactivation. Comparing our control chart approach to multiple competing models establishes the improved ability of our chart to predict customer activity. Additionally, a field test in the greetings and gifts industry illustrates that this approach increases overall activity by 1.9–3.5 percentage points, and leads to incremental activity and revenue gains of 111% and 38%, respectively