From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Trade marketing analytics in consumer goods industry

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementWe address transparency of trade spends in consumer goods industry and propose a set of business performance indicators that follow Pareto (80/20) rule – a popular concept in optimization problem solving. Discovery of power laws in behaviors of travelling sales persons, buying patterns of customers, popularity of products, and market demand fluctuations – all that leads to better-informed decisions among all those involved into planning, execution, and post-promotion evaluation. Practical result of our work is a prototype implementation of proposed measures. The most remarkable finding – consistency of travelling sales person journey between customer locations. Loyalty to brand, or brand market power – whatever forces field sales representatives to put at least one product of market player of interest into nearly every market basket – fits into small world model. This behavior not only changes from person to person, but also remains the same after reassignment into different territory. For industrialization stage of this project, we outline key design considerations for information system capable of handling real-time workload scalable to petabytes. We built our analyses for collaborative processes of integrated planning that requires joint effort of multidisciplinary team. Field tests demonstrate how insights from data can trigger business transformation. That is why we end up with recommendation for system integrators to include Knowledge Discovery into information system deployment projects

Predictive Customer Lifetime value modeling: Improving customer engagement and business performance

CookUnity, a meal subscription service, has witnessed substantial annual revenue growth over the past three years. However, this growth has primarily been driven by the acquisition of new users to expand the customer base, rather than an evident increase in customers' spending levels. If it weren't for the raised subscription prices, the company's customer lifetime value (CLV) would have remained the same as it was three years ago. Consequently, the company's leadership recognizes the need to adopt a holistic approach to unlock an enhancement in CLV. The objective of this thesis is to develop a comprehensive understanding of CLV, its implications, and how companies leverage it to inform strategic decisions. Throughout the course of this study, our central focus is to deliver a fully functional and efficient machine learning solution to CookUnity. This solution will possess exceptional predictive capabilities, enabling accurate forecasting of each customer's future CLV. By equipping CookUnity with this powerful tool, our aim is to empower the company to strategically leverage CLV for sustained growth. To achieve this objective, we analyze various methodologies and approaches to CLV analysis, evaluating their applicability and effectiveness within the context of CookUnity. We thoroughly explore available data sources that can serve as predictors of CLV, ensuring the incorporation of the most relevant and meaningful variables in our model. Additionally, we assess different research methodologies to identify the top-performing approach and examine its implications for implementation at CookUnity. By implementing data-driven strategies based on our predictive CLV model, CookUnity will be able to optimize order levels and maximize the lifetime value of its customer base. The outcome of this thesis will be a robust ML solution with remarkable prediction accuracy and practical usability within the company. Furthermore, the insights gained from our research will contribute to a broader understanding of CLV in the subscription-based business context, stimulating further exploration and advancement in this field of study

Modeling the Psychology of Consumer and Firm Behavior with Behavioral Economics

Marketing is an applied science that tries to explain and influence how firms and consumers actually behave in markets. Marketing models are usually applications of economic theories. These theories are general and produce precise predictions, but they rely on strong assumptions of rationality of consumers and firms. Theories based on rationality limits could prove similarly general and precise, while grounding theories in psychological plausibility and explaining facts which are puzzles for the standard approach. Behavioral economics explores the implications of limits of rationality. The goal is to make economic theories more plausible while maintaining formal power and accurate prediction of field data. This review focuses selectively on six types of models used in behavioral economics that can be applied to marketing. Three of the models generalize consumer preference to allow (1) sensitivity to reference points (and loss-aversion); (2) social preferences toward outcomes of others; and (3) preference for instant gratification (quasi-hyperbolic discounting). The three models are applied to industrial channel bargaining, salesforce compensation, and pricing of virtuous goods such as gym memberships. The other three models generalize the concept of gametheoretic equilibrium, allowing decision makers to make mistakes (quantal response equilibrium), encounter limits on the depth of strategic thinking (cognitive hierarchy), and equilibrate by learning from feedback (self-tuning EWA). These are applied to marketing strategy problems involving differentiated products, competitive entry into large and small markets, and low-price guarantees. The main goal of this selected review is to encourage marketing researchers of all kinds to apply these tools to marketing. Understanding the models and applying them is a technical challenge for marketing modelers, which also requires thoughtful input from psychologists studying details of consumer behavior. As a result, models like these could create a common language for modelers who prize formality and psychologists who prize realism

Store Choice in the Emerging Indian Apparel Retail Market: an Empirical Analysis

Store Choice has been a subject of frequent research in the developed retail markets of the west. However, the retail sector in India has been largely fragmented and unorganized. However, the retail scenario in India is changing at a very brisk pace. Many international retailers entering India and many Indian retailers in the organized segment are coming up with stores all across the country, but a majority of these stores have merely transplanted western formats onto the Indian retail scene without actually evaluating the salience of various store attributes from the customer perspective. In light of this the purpose of this paper is to study the store choice criteria in the context of apparel retailing in India. Drawing from major global and Indian studies conducted in the past, this research has identified two dimensions, which in different combinations could create sustainable store choice and hence, store loyalty. These two dimensions are termed “loyalty drivers†and experience enhancersâ€.Consumer Behaviour

Holiday Price Rigidity and Cost of Price Adjustment

The Thanksgiving-Christmas holiday period is a major sales period for US retailers. Due to higher store traffic, tasks such as restocking shelves, handling customers’ questions and inquiries, running cash registers, cleaning, and bagging, become more urgent during holidays. As a result, the holiday-period opportunity cost of price adjustment may increase dramatically for retail stores, which should lead to greater price rigidity during holidays. We test this prediction using weekly retail scanner price data from a major Midwestern supermarket chain. We find that indeed, prices are more rigid during holiday periods than non-holiday periods. For example, the econometric model we estimate suggests that the probability of a price change is lower during holiday periods, even after accounting for cost changes. Moreover, we find that the probability of a price change increases with the size of the cost change, during both, the holiday as well as non-holiday periods. We argue that these findings are best explained by higher price adjustment costs (menu cost) the retailers face during the holiday periods. Our data provides a natural experiment for studying variation in price rigidity because most aspects of market environment such as market structure, industry concentration, the nature of long-term relationships, contractual arrangements, etc., do not vary between holiday and nonholiday periods. We, therefore, are able to rule out these commonly used alternative explanations for the price rigidity, and conclude that the menu cost theory offers the best explanation for the holiday period price rigidity.

A framework for knowledge discovery within business intelligence for decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Business Intelligence (BI) techniques provide the potential to not only efficiently manage but further analyse and apply the collected information in an effective manner. Benefiting from research both within industry and academia, BI provides functionality for accessing, cleansing, transforming, analysing and reporting organisational datasets. This provides further opportunities for the data to be explored and assist organisations in the discovery of correlations, trends and patterns that exist hidden within the data. This hidden information can be employed to provide an insight into opportunities to make an organisation more competitive by allowing manager to make more informed decisions and as a result, corporate resources optimally utilised. This potential insight provides organisations with an unrivalled opportunity to remain abreast of market trends. Consequently, BI techniques provide significant opportunity for integration with Decision Support Systems (DSS). The gap which was identified within the current body of knowledge and motivated this research, revealed that currently no suitable framework for BI, which can be applied at a meta-level and is therefore tool, technology and domain independent, currently exists. To address the identified gap this study proposes a meta-level framework: - ‘KDDS-BI’, which can be applied at an abstract level and therefore structure a BI investigation, irrespective of the end user. KDDS-BI not only facilitates the selection of suitable techniques for BI investigations, reducing the reliance upon ad-hoc investigative approaches which rely upon ‘trial and error’, yet further integrates Knowledge Management (KM) principles to ensure the retention and transfer of knowledge due to a structured approach to provide DSS that are based upon the principles of BI. In order to evaluate and validate the framework, KDDS-BI has been investigated through three distinct case studies. First KDDS-BI facilitates the integration of BI within ‘Direct Marketing’ to provide innovative solutions for analysis based upon the most suitable BI technique. Secondly, KDDS-BI is investigated within sales promotion, to facilitate the selection of tools and techniques for more focused in store marketing campaigns and increase revenue through the discovery of hidden data, and finally, operations management is analysed within a highly dynamic and unstructured environment of the London Underground Ltd. network through unique a BI solution to organise and manage resources, thereby increasing the efficiency of business processes. The three case studies provide insight into not only how KDDS-BI provides structure to the integration of BI within business process, but additionally the opportunity to analyse the performance of KDDS-BI within three independent environments for distinct purposes provided structure through KDDS-BI thereby validating and corroborating the proposed framework and adding value to business processes

Effect of Customer Heterogeneity on Online Pricing: Just Noticeable Differences in a Competitive Service Industry

Online sales for both products and services are on the rise globally and are projected to increase by 10% annually to $370 billion by 2017 (Lomas 2013). Price is a key management lever for firm performance (McKinsey 2002) and key determinant of purchasing decision for a consumer (Bishop 1984, Doyle 1984, Sawyer and Dickson 1984, Schechter 1984). However, customers do not remember exact price but have a band of prices that are acceptable (Monroe 1973) (Olson 1976)(Monroe 1969). This research uses Just Noticeable Difference (JND) theory as the theoretical lens to study online pricing thresholds in a retail service industry. This quantitative field study uses three and half years of non-contractual transactional and customer level data from a B2C company to evaluate the hypotheses. Two phased investigations are conducted. Study 1 empirically determines the pricing threshold range for the service industry. Study 2 examines the effect of pricing action on purchase frequency based on customer heterogeneity and competitive prices. Contributions are three-fold. Theoretically, the study furthers the conceptual understanding of the pricing thresholds in the digital marketplace by using real customer level data. Second, the application of JND theory in a non-contractual B2C sector confirms that pricing thresholds for the service industry are higher than consumer goods industry. Third, this research confirms the varying effects of customer attributes (loyalty, motivation, and online purchase channel) on pricing thresholds. These findings are key to implementing a differentiated pricing strategy across channels and customer types to maximize firm performance and increase customer retention