Forecasting category sales and market share for wireless telephone subscribers: a combined approach

The ability to forecast market share remains a challenge for many managers especially in dynamic markets, such as the telecommunications sector. In order to accommodate the unique dynamic characteristics of the telecommunications market, we use a multi-component model, called MSHARE. Our method involves a two-phase process. The first phase consists of three components: a projection method, a ring down survey methodology and a purchase intentions survey. The predictions from these components are combined to forecast category sales for the wireless subscribers market. In the second phase, market shares for the various brands are generated using the forecast of the number of subscribers that are obtained in Phase 1 and the share predictions from the ring down methodology. The proposed methodology produces the minimum Relative Absolute Error for each market as compared to the forecasts from each individual component in the first phase. The value of the proposed model is illustrated by its application to a real world scenario. The managerial implications of the proposed model are also discussed.     

On commodity price limits

This paper examines the behavior of futures prices and trader positions around the occurrence of price limits in commodity futures markets. We ask whether limit events are the result of shocks to fundamental volatility or the result of temporary volatility induced by the trading of noncommercial market participants (speculators). We find little evidence that limits events are the result of speculative activity, but instead associated with shocks to fundamentals that lead to persistent price changes. When futures trading halts price discovery migrates to options markets, but option prices provide a biased estimate of subsequent future prices when trading resumes.     

CRM in Data-Rich Multichannel Retailing Environments: A Review and Future Research Directions

Many retailers have collected large amounts of customer data using, for example, loyalty programs. We provide an overview of the extant literature on customer relationship management (CRM), with a specific focus on retailing. We discuss how retailers can gather customer data and how they can analyze these data to gain useful customer insights. We provide an overview of the methods predicting customer responses and behavior over time. We also discuss the existing knowledge on the application of marketing actions in a CRM context, while providing an in-depth discussion on CRM and firm value. We outline future research directions based on the literature review and retail practice insights.     

The ‘alien invasion’ in South Africa: Illegal immigrants in Durban

Although South Africa's emerging non‐racial democracy has been internationally acclaimed, global integration has also brought its problems. One of these is the greater number of illegal immigrants entering the country. This article examines the problem of illegal immigration by focusing on an intensive case study in the locality of Durban. It attempts to identify reasons for illegal immigrants coming to South Africa, ascertain their country of origin, investigate the consequences of their stay in the Republic, identify problems associated with immigrants, and assess policy options to reduce the influx of aliens. The study reveals that the majority of illegal immigrants come to South Africa in search of better economic opportunities. However, they are accused of taking away the jobs of locals, lowering wages and spreading diseases. Although official government policy towards illegals is embodied in the Aliens Control Act (1991), there is a need to understand the problem within its regional and historical context.     

Public spending and outcomes: Does governance matter?

This paper studies the links between public spending, governance, and outcomes. We examine the role of governance–measured by the level of corruption and the quality of bureaucracy–in determining the efficacy of public spending in improving human development outcomes. Our analysis contributes to our understanding of the relationship between public spending, governance and outcomes, and helps explain the surprising result that public spending often does not yield the expected improvement in outcomes. We show empirically that the differences in the efficacy of public spending can be largely explained by the quality of governance. Public health spending lowers child mortality rates more in countries with good governance. Similarly, public spending on primary education becomes more effective in increasing primary education attainment in countries with good governance. More generally, public spending has virtually no impact on health and education outcomes in poorly governed countries. These findings have important implications for enhancing the development effectiveness of public spending. The lessons are particularly relevant for developing countries, where public spending on education and health is relatively low, and the state of governance is often poor.     

Evaluation of business processes using probability-driven activity-based costing

The design of business processes often ignores detailed consideration of service cost. With competitive market pressure, this has become a key factor for the service sector. The ideas discussed in this paper are a result of research into automating the re-engineering of business processes. This paper presents a methodology for estimating the cost of a process, crucial to the evaluation of the process, using a novel variation of activity-based cost estimation. The majority of the research in this project has concentrated on the service sector and these types of processes are used as examples.     

Will the frog change into a prince? Predicting future customer profitability

More and more companies have customer databases that enable them to analyze customer profitability over time. These companies often seek to determine the most important customers as indicated by their current or historical profitability and focus attention on them. Focusing on profitable customers can result in more efficient use of marketing resources, but this approach neglects the fact that customers can evolve over time. Some customers begin as low-profit customers but eventually develop into high-profit customers. Others may start out as high-profit customers but become unprofitable over time. Previous efforts to predict future profitability have been relatively unsuccessful, with relatively simple, naïve models often performing just as well as or better than more sophisticated ones. Our paper presents a new approach to predicting customer profitability in future periods that performs significantly better than naïve models. We estimate the models on data from a high-tech company in a business-to-business context and validate the models' predictive ability on a holdout sample.We show that a model based on simulation of customer futures provides large improvements over naïve extrapolation of average profits. By using the simulation model to select customers, ROI from marketing efforts is projected to increase by 58%.     

Improving customer profit predictions with customer mindset metrics through multiple overimputation

Journal of the Academy of Marketing Science - Research and practice have called for the incorporation of customer mindset metrics (CMMs) to improve the accuracy of models that predict individual...     

Knowing what to sell, when, and to whom

Despite an abundance of data, most companies do a poor job of predicting the behavior of their customers. In fact, the authors' research suggests that even companies that take the greatest trouble over their predictions about whether a particular customer will buy a particular product are correct only around 55% of the time--a result that hardly justifies the costs of having a CRM system in the first place. Businesses usually conclude from studies like this that it's impossible to use the past to predict the future, so they revert to the timeworn marketing practice of inundating their customers with offers. But as the authors explain, the reason for the poor predictions is not any basic limitation of CRM systems or the predictive power of past behavior, but rather of the mathematical methods that companies use to interpret the data. The authors have developed a new way of predicting customer behavior, based on the work of the Nobel Prize-winning economist Daniel McFadden, that delivers vastly improved results. Indeed, the methodology increases the odds of successfully predicting a specific purchase by a specific customer at a specific time to about 85%, a number that will have a major impact on any company's marketing ROI. What's more, using this methodology, companies can increase revenues while reducing their frequency of customer contact-evidence that overcommunication with customers may actually damage a company's sales.