New Approach for Market Intelligence Using Artificial and Computational Intelligence

Small and medium sized retailers are central to the private sector and a vital contributor to economic growth, but often they face enormous challenges in unleashing their full potential. Financial pitfalls, lack of adequate access to markets, and difficulties in exploiting technology have prevented them from achieving optimal productivity. Market Intelligence (MI) is the knowledge extracted from numerous internal and external data sources, aimed at providing a holistic view of the state of the market and influence marketing related decision-making processes in real-time. A related, burgeoning phenomenon and crucial topic in the field of marketing is Artificial Intelligence (AI) that entails fundamental changes to the skillssets marketers require. A vast amount of knowledge is stored in retailers’ point-of-sales databases. The format of this data often makes the knowledge they store hard to access and identify. As a powerful AI technique, Association Rules Mining helps to identify frequently associated patterns stored in large databases to predict customers’ shopping journeys. Consequently, the method has emerged as the key driver of cross-selling and upselling in the retail industry. At the core of this approach is the Market Basket Analysis that captures knowledge from heterogeneous customer shopping patterns and examines the effects of marketing initiatives. Apriori, that enumerates frequent itemsets purchased together (as market baskets), is the central algorithm in the analysis process. Problems occur, as Apriori lacks computational speed and has weaknesses in providing intelligent decision support. With the growth of simultaneous database scans, the computation cost increases and results in dramatically decreasing performance. Moreover, there are shortages in decision support, especially in the methods of finding rarely occurring events and identifying the brand trending popularity before it peaks. As the objective of this research is to find intelligent ways to assist small and medium sized retailers grow with MI strategy, we demonstrate the effects of AI, with algorithms in data preprocessing, market segmentation, and finding market trends. We show with a sales database of a small, local retailer how our Åbo algorithm increases mining performance and intelligence, as well as how it helps to extract valuable marketing insights to assess demand dynamics and product popularity trends. We also show how this results in commercial advantage and tangible return on investment. Additionally, an enhanced normal distribution method assists data pre-processing and helps to explore different types of potential anomalies.Små och medelstora detaljhandlare är centrala aktörer i den privata sektorn och bidrar starkt till den ekonomiska tillväxten, men de möter ofta enorma utmaningar i att uppnå sin fulla potential. Finansiella svårigheter, brist på marknadstillträde och svårigheter att utnyttja teknologi har ofta hindrat dem från att nå optimal produktivitet. Marknadsintelligens (MI) består av kunskap som samlats in från olika interna externa källor av data och som syftar till att erbjuda en helhetssyn av marknadsläget samt möjliggöra beslutsfattande i realtid. Ett relaterat och växande fenomen, samt ett viktigt tema inom marknadsföring är artificiell intelligens (AI) som ställer nya krav på marknadsförarnas färdigheter. Enorma mängder kunskap finns sparade i databaser av transaktioner samlade från detaljhandlarnas försäljningsplatser. Ändå är formatet på dessa data ofta sådant att det inte är lätt att tillgå och utnyttja kunskapen. Som AI-verktyg erbjuder affinitetsanalys en effektiv teknik för att identifiera upprepade mönster som statistiska associationer i data lagrade i stora försäljningsdatabaser. De hittade mönstren kan sedan utnyttjas som regler som förutser kundernas köpbeteende. I detaljhandel har affinitetsanalys blivit en nyckelfaktor bakom kors- och uppförsäljning. Som den centrala metoden i denna process fungerar marknadskorgsanalys som fångar upp kunskap från de heterogena köpbeteendena i data och hjälper till att utreda hur effektiva marknadsföringsplaner är. Apriori, som räknar upp de vanligt förekommande produktkombinationerna som köps tillsammans (marknadskorgen), är den centrala algoritmen i analysprocessen. Trots detta har Apriori brister som algoritm gällande låg beräkningshastighet och svag intelligens. När antalet parallella databassökningar stiger, ökar också beräkningskostnaden, vilket har negativa effekter på prestanda. Dessutom finns det brister i beslutstödet, speciellt gällande metoder att hitta sällan förekommande produktkombinationer, och i att identifiera ökande popularitet av varumärken från trenddata och utnyttja det innan det når sin höjdpunkt. Eftersom målet för denna forskning är att hjälpa små och medelstora detaljhandlare att växa med hjälp av MI-strategier, demonstreras effekter av AI med hjälp av algoritmer i förberedelsen av data, marknadssegmentering och trendanalys. Med hjälp av försäljningsdata från en liten, lokal detaljhandlare visar vi hur Åbo-algoritmen ökar prestanda och intelligens i datautvinningsprocessen och hjälper till att avslöja värdefulla insikter för marknadsföring, framför allt gällande dynamiken i efterfrågan och trender i populariteten av produkterna. Ytterligare visas hur detta resulterar i kommersiella fördelar och konkret avkastning på investering. Dessutom hjälper den utvidgade normalfördelningsmetoden i förberedelsen av data och med att hitta olika slags anomalier

On the Interface Between Operations and Human Resources Management

Operations management (OM) and human resources management (HRM) have historically been very separate fields. In practice, operations managers and human resource managers interact primarily on administrative issues regarding payroll and other matters. In academia, the two subjects are studied by separate communities of scholars publishing in disjoint sets of journals, drawing on mostly separate disciplinary foundations. Yet, operations and human resources are intimately related at a fundamental level. Operations are the context that often explains or moderates the effects of human resource activities such as pay, training, communications and staffing. Human responses to operations management systems often explain variations or anomalies that would otherwise be treated as randomness or error variance in traditional operations research models. In this paper, we probe the interface between operations and human resources by examining how human considerations affect classical OM results and how operational considerations affect classical HRM results. We then propose a unifying framework for identifying new research opportunities at the intersection of the two fields

Time series forecast and anomaly detection at scale applied to business metrics in an ERP environment

In the business world, dashboards are a widely used analytical mechanism that helps in the decision-making process by displaying insights, key performance indicators, and business metrics. The information provided by this type of mechanism is strongly aggregated, to obtain a high level of summarization and consequently make reading easier. However, the necessary summarization causes “blind spots” to appear by hiding important information such as a sharp drop in revenue from a specific customer, seller, or product/ service. These “blind spots” make it difficult to detect potential business problems and opportunities, which depend on lengthy and thorough additional exploration. Also, the digital transformation process has resulted in a substantial increase in the number of metrics for all systems supporting the business that need to be tracked. Thus, it will be possible to anticipate actions based on the prediction of future behavior, as well as to detect any isolated or successive deviation from the expected behavior. With this dissertation, we intend to promote the acquisition of knowledge from business data through the application of Machine Learning techniques. Based on the Data-Driven Decision-Making process, we intend to propose integration into an ERP application of a mechanism to predict time-series behavior, as well as detecting and measuring possible anomalies. For dealing with a wide diversity of time series, we propose a meta-learning forecasting method that uses a classifier to identify the best forecasting method for each time series. We also propose a new intelligent metric that allows us to sort time series by the accumulated anomaly. The knowledge generated will complement the information provided by the analytical mechanisms typically present in an ERP application (including dashboards). In this way, we intend to contribute to the maximization of profits and reduction of the possibility of error or fraud, as well as waste and consequently mitigate uncertainty and reduce operational risk. Our solution should promote the need to use Machine Learning in Small and Medium Enterprises, and consequently, future implementation of AI-Driven Decision Making. AI-Driven Decision-Making purposes an assertive and automated reaction to problems or opportunities encountered, but whose study is outside the scope of this dissertation.No meio empresarial, “dashboards” são mecanismos analíticos amplamente utilizados que ajudam no processo de tomada de decisão ao exibirem insights, indicadores de desempenho (KPIs) e métricas de negócio. A informação disponibilizada por este tipo de mecanismo é fortemente agregada, de forma a obter-se um elevado nível de sumarização e consequentemente facilitar a sua consulta. No entanto, a necessária sumarização provoca o surgimento de “blind spots”, ao ocultar informação importante como, por exemplo, uma quebra acentuada de receita de um cliente, ou de um vendedor, ou de um produto/serviço específico. Estes “blind spots” dificultam a deteção de eventuais problemas e oportunidades de negócio, que ficam dependentes de uma exploração adicional demorada e minuciosa. Adicionalmente, o processo de transformação digital tem como consequência um aumento substancial do número de métricas referentes a todos os sistemas que suportam o negócio, que importa acompanhar. Desta forma, será possível antecipar ações baseadas na previsão de um comportamento futuro, bem como detetar um eventual desvio isolado ou sucessivo face ao seu comportamento espectável. Como objetivo desta dissertação pretendemos promover a obtenção de conhecimento a partir de dados de negócio, através da aplicação de técnicas de Aprendizagem Automática (“Machine Learning”). Tendo por base o processo de tomada de decisão a partir de dados (“Data-Driven Decision-Making”) pretende-se propor a integração numa aplicação ERP de um mecanismo que permita prever o comportamento futuro de séries temporais que contêm dados de negócio, bem como detetar e medir possíveis anomalias de forma a poderem ser gerados alertas. Para lidar com uma ampla diversidade de séries temporais, propomos um método de previsão de meta-aprendizagem que utiliza um classificador para identificar o melhor método de previsão para cada série temporal, e uma nova métrica inteligente que permite ordenar séries temporais pela anomalia acumulada. O conhecimento gerado irá complementar a informação disponibilizada pelos mecanismos analíticos tipicamente existente numa aplicação ERP (incluindo “dashboards”). Desta forma pretendemos contribuir para uma maximização dos proveitos e redução da possibilidade de erro ou fraude, bem como do desperdício e consequentemente mitigar a incerteza e diminuir o risco operacional. Pretende-se igualmente que a solução promova a utilização de Aprendizagem Automática em Pequenas e Médias Empresas, e consequentemente uma futura implementação de tomada de decisões a partir de Inteligência Artificial (“AI-Driven Decision Making”), onde uma reação assertiva e automatizada é despoletada, face a problemas ou oportunidades encontradas, mas cujo estudo fica fora do âmbito do presente trabalho

Using big data for customer centric marketing

This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe

A waste of energy? A critical assessment of the investigation of the UK Energy Market by the Competition and Markets Authority

This document is the accepted manuscript version of the following article: Chrysovalantis Amountzias, Hulya Dagdeviren and Tassos Patokos, ‘A waste of energy? A critical assessment of the investigation of the UK energy market by the Competition and Markets Authority’, Competition & Change, Vol. 21 (1): 45-60, February 2017. The final version of this paper is available at doi: http://journals.sagepub.com/doi/pdf/10.1177/1024529416678070. Published by SAGE Publishing.In this paper, we assess the findings of the UK energy market investigation by the Competition and Markets Authority, conducted during June 2014–June 2016.We argue that the results of the investigation have been advantageous for the large energy companies and they risk failing to bring any significant and positive change to the energy industry.We highlight three major aspects of the Competition and Markets Authorities assessment. First, the panel examined retail and wholesale segments of the energy industry in isolation, which can be misleading in the assessment of vertical integration. It also considered new entries to the sector as a sign of competitive strength when many were due to favourable government policies in the form of exemptions from various obligations. Second, its conclusion that a position of unilateral market power by the large energy companies arises from weak customer engagement (i.e. low switching rates) shifts the focus and responsibility for the problems of the energy markets away from the conduct of the companies onto customers. Finally, the investigation placed an overemphasis on competition without due reference to its consequences for consumers’ welfare.Peer reviewe

Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201