Enhancing the Prediction of Missing Targeted Items from the Transactions of Frequent, Known Users

The ability for individual grocery retailers to have a single view of its customers across all of their grocery purchases remains elusive, and is considered the “holy grail” of grocery retailing. This has become increasingly important in recent years, especially in the UK, where competition has intensified, shopping habits and demographics have changed, and price sensitivity has increased. Whilst numerous studies have been conducted on understanding independent items that are frequently bought together, there has been little research conducted on using this knowledge of frequent itemsets to support decision making for targeted promotions. Indeed, having an effective targeted promotions approach may be seen as an outcome of the “holy grail”, as it will allow retailers to promote the right item, to the right customer, using the right incentives to drive up revenue, profitability, and customer share, whilst minimising costs. Given this, the key and original contribution of this study is the development of the market target (mt) model, the clustering approach, and the computer-based algorithm to enhance targeted promotions. Tests conducted on large scale consumer panel data, with over 32000 customers and 51 million individual scanned items per year, show that the mt model and the clustering approach successfully identifies both the best items, and customers to target. Further, the algorithm segregates customers into differing categories of loyalty, in this case it is four, to enable retailers to offer customised incentives schemes to each group, thereby enhancing customer engagement, whilst preventing unnecessary revenue erosion. The proposed model is compared with both a recently published approach, and the cross-sectional shopping patterns of the customers on the consumer scanner panel. Tests show that the proposed approach outperforms the other approach in that it significantly reduces the probability of having “false negatives” and “false positives” in the target customer set. Tests also show that the customer segmentation approach is effective, in that customers who are classed as highly loyal to a grocery retailer, are indeed loyal, whilst those that are classified as “switchers” do indeed have low levels of loyalty to the selected grocery retailer. Applying the mt model to other fields has not only been novel but yielded success. School attendance is improved with the aid of the mt model being applied to attendance data. In this regard, an action research study, involving the proposed mt model and approach, conducted at a local UK primary school, has resulted in the school now meeting the required attendance targets set by the government, and it has halved its persistent absenteeism for the first time in four years. In medicine, the mt model is seen as a useful tool that could rapidly uncover associations that may lead to new research hypotheses, whilst in crime prevention, the mt value may be used as an effective, tangible, efficiency metric that will lead to enhanced crime prevention outcomes, and support stronger community engagement. Future work includes the development of a software program for improving school attendance that will be offered to all schools, while further progress will be made on demonstrating the effectiveness of the mt value as a tangible crime prevention metric

Evolving Loyalty Programs- Merging Classic Loyalty with New Technology

Thesis purpose: The purpose of this thesis is to determine how a technolo-gically enhanced shopping tool, synergizing eCRM tech-niques with personalized promotion at the point-of-sale, can increase customer loyalty by considering perceptions of current grocery retailer loyalty programs in Sweden. Methodology: An exploratory research was undertaken in the light of a qualitative method collecting primary data through focus group interviews. Theoretical perspective: The major considerations regarding theoretical aspects, for this thesis, can be addressed by Behavioral/Attitudinal loyalty and Relevance of Promotion. Empirical data: The empirical data, in regards to primary data, was ga-thered based on four focus group interviews. Conclusion: The conclusions of the thesis imply, that in order to make a loyalty program successful, it needs to firstly be conven-ient for the customer; easy to join, and provide an easy and time efficient display at the point-of-sale. Secondly, it has to be relevant/meaningful in terms of cash-back, promotion and have additional functions such as life-style profiles which customers can use. Thirdly, it needs to be inspiring; through recipes on the display and giving recommendations based on other customers with similar purchase habits. Finally, the program needs to address ethical concerns; how the data is used and by whom

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces

Critical success factors for business-to-commerce E-business : lessons from Amazon and Dell

Thesis (S.M.M.O.T.)--Massachusetts Institute of Technology, Sloan School of Management, Management of Technology Program, 2000.Includes bibliographical references.The Internet has becoming an increasingly important channel for both business-to-consumer and business-to-business e-commerce. It has changed the way many companies do business. Every day, more and more companies worldwide are being linked electronically. But the success rate in terms of profitability for these startups is low. This thesis focuses on business-to-consumer aspect of e-commerce. My research is to study the models from a set of online merchants and see how these companies translate their companies' e-business vision into reality. What are the critical factors these online merchants considered as they transform their companies into an e-commerce? This thesis identifies the key success factors of this technology strategy and model as well as helps understanding to what extent this success can be replicated in other markets and industries. Through detailed case studies on Amazon and Dell, we will analyze their strategies and identify the success factors that make them unique to thrive on this competitive landscape of the Digital Economy.by Le Kha.S.M.M.O.T

Utility-Based Data Mining Workshop Chairs:

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Advancing the reform agenda: selected speeches

In the decade and a half since the Productivity Commission was formally established, it has completed 110 inquiries and other commissioned studies and made some 1500 policy recommendations to governments. All of these recommendations were made because the Commission judged that their implementation would enhance Australians’ living standards and quality of life.   In many cases, they would do so by raising the capacity of Australia’s economy to produce valued goods and services — in other words, by raising its ‘productivity’. On a rough reckoning, around two-thirds of the Commission’s recommendations over the years have been accepted and (more or less) implemented by governments. That is not a bad strike rate, given that our reports typically deal with complex and politically contentious areas of public policy, where benefits to the majority can necessitate withdrawing advantages from (vocal) minorities. It nevertheless leaves a sizeable residual, to which Reserve Bank Governor Glenn Stevens was no doubt alluding in his much-reported remarks. Many in the media took him literally though, and were disappointed that the Commission did not in fact have a ‘list’ at the ready