1,134 research outputs found
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 retailersâ 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 customersâ 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 Ă
bo 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.SmÄ och medelstora detaljhandlare Àr centrala aktörer i den privata sektorn och bidrar starkt till den ekonomiska tillvÀxten, men de möter ofta enorma utmaningar i att uppnÄ sin fulla potential. Finansiella svÄrigheter, brist pÄ marknadstilltrÀde och svÄrigheter att utnyttja teknologi har ofta hindrat dem frÄn att nÄ optimal produktivitet. Marknadsintelligens (MI) bestÄr av kunskap som samlats in frÄn olika interna externa kÀllor av data och som syftar till att erbjuda en helhetssyn av marknadslÀget samt möjliggöra beslutsfattande i realtid. Ett relaterat och vÀxande fenomen, samt ett viktigt tema inom marknadsföring Àr artificiell intelligens (AI) som stÀller nya krav pÄ marknadsförarnas fÀrdigheter.
Enorma mĂ€ngder kunskap finns sparade i databaser av transaktioner samlade frĂ„n detaljhandlarnas försĂ€ljningsplatser. ĂndĂ„ Ă€r formatet pĂ„ dessa data ofta sĂ„dant att det inte Ă€r lĂ€tt att tillgĂ„ och utnyttja kunskapen. Som AI-verktyg erbjuder affinitetsanalys en effektiv teknik för att identifiera upprepade mönster som statistiska associationer i data lagrade i stora försĂ€ljningsdatabaser. De hittade mönstren kan sedan utnyttjas som regler som förutser kundernas köpbeteende. I detaljhandel har affinitetsanalys blivit en nyckelfaktor bakom kors- och uppförsĂ€ljning. Som den centrala metoden i denna process fungerar marknadskorgsanalys som fĂ„ngar upp kunskap frĂ„n de heterogena köpbeteendena i data och hjĂ€lper till att utreda hur effektiva marknadsföringsplaner Ă€r. Apriori, som rĂ€knar upp de vanligt förekommande produktkombinationerna som köps tillsammans (marknadskorgen), Ă€r den centrala algoritmen i analysprocessen.
Trots detta har Apriori brister som algoritm gÀllande lÄg berÀkningshastighet och svag intelligens. NÀr antalet parallella databassökningar stiger, ökar ocksÄ berÀkningskostnaden, vilket har negativa effekter pÄ prestanda. Dessutom finns det brister i beslutstödet, speciellt gÀllande metoder att hitta sÀllan förekommande produktkombinationer, och i att identifiera ökande popularitet av varumÀrken frÄn trenddata och utnyttja det innan det nÄr sin höjdpunkt.
Eftersom mĂ„let för denna forskning Ă€r att hjĂ€lpa smĂ„ och medelstora detaljhandlare att vĂ€xa med hjĂ€lp av MI-strategier, demonstreras effekter av AI med hjĂ€lp av algoritmer i förberedelsen av data, marknadssegmentering och trendanalys. Med hjĂ€lp av försĂ€ljningsdata frĂ„n en liten, lokal detaljhandlare visar vi hur Ă
bo-algoritmen ökar prestanda och intelligens i datautvinningsprocessen och hjÀlper till att avslöja vÀrdefulla insikter för marknadsföring, framför allt gÀllande dynamiken i efterfrÄgan och trender i populariteten av produkterna. Ytterligare visas hur detta resulterar i kommersiella fördelar och konkret avkastning pÄ investering. Dessutom hjÀlper den utvidgade normalfördelningsmetoden i förberedelsen av data och med att hitta olika slags anomalier
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 â 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 â 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 â consistency of travelling sales person journey between customer locations. Loyalty to brand, or brand market power â whatever forces field sales representatives to put at least one product of market player of interest into nearly every market basket â 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
Customer Relationship Management : Concept, Strategy, and Tools -3/E
Customer relationship management
(CRM) as a strategy and as a technology
has gone through an amazing evolutionary
journey. After the initial technological
approaches, this process has matured considerably â both from a conceptual and
from an applications point of view. Of
course this evolution continues, especially
in the light of the digital transformation.
Today, CRM refers to a strategy, a set of
tactics, and a technology that has become
indispensable in the modern economy.
Based on both authorsâ rich academic and
managerial experience, this book gives a
unified treatment of the strategic and
tactical aspects of customer relationship
management as we know it today. It
stresses developing an understanding of
economic customer value as the guiding
concept for marketing decisions. The goal
of this book is to be a comprehensive and
up-to-date learning companion for
advanced undergraduate students, master
students, and executives who want a
detailed and conceptually sound insight
into the field of CRM
Drone-based Integration of Hyperspectral Imaging and Magnetics for Mineral Exploration
The advent of unoccupied aerial systems (UAS) as disruptive technology has a lasting impact on remote sensing, geophysics and most geosciences. Small, lightweight, and low-cost UAS enable researchers and surveyors to acquire earth observation data in higher spatial and spectral resolution as compared to airborne and satellite data. UAS-based applications range from rapid topographic mapping using photogrammetric techniques to hyperspectral and geophysical measurements of surface and subsurface geology. UAS surveys contribute to identifying metal deposits, monitoring of mine sites and can reveal arising environmental issues associated with mining. Further, affordable UAS technology will boost exploration data availability and expertise in the global south.
This thesis investigates the application of UAS-based multi-sensor data for mineral exploration, in particular the integration of hyperspectral imagers, magnetometers and digital cameras (covering the visible red, green, blue light spectrum). UAS-based research is maturing, however the aforementioned methods are not unified effectively. RGB-based photogrammetry is used to investigate topography and surface texture. Image spectrometers measure mineral-specific surface signatures. Magnetometers detect geomagnetic field changes caused by magnetic minerals at surface and depth. The integration of such UAS sensor-based methods in this thesis augments exploration potential with non-invasive, high-resolution, safe, rapid and practical survey methods.
UAS-based surveying acquired, processed and integrated data from three distinct test sites. The sites are located in Finland (Fe-Ti-V at OtanmÀki; apatite at SiilinjÀrvi) and Greenland (Ni-Cu-PGE at Qullissat, Disko Island) and were chosen as geologically diverse areas in subarctic to arctic environments. Restricted accessibility, unfavourable atmospheric conditions, dark rocks, debris and vegetation cover and low solar illumination were common features. While the topography in Finland was moderately flat, a steep landscape challenged the Greenland field work. These restraints meant that acquisitions varied from site to site and how data was integrated and interpreted is dependent on the commodity of interest.
Iron-based spectral absorption and magnetic mineral response were detected using hyperspectral and magnetic surveying in OtanmÀki. Multi-sensor-based image feature detection and classification combined with magnetic forward modelling enabled seamless geologic mapping in SiilinjÀrvi. Detailed magnetic inversion and multispectral photogrammetry led to the construction of a comprehensive 3D model of magmatic exploration targets in Greenland. Ground truth at different intensity was employed to verify UAS-based data interpretations during all case studies.
Laboratory analysis was applied when deemed necessary to acquire geologic-mineralogic validation (e.g., X-ray diffraction and optical microscopy for mineral identification to establish lithologic domains, magnetic susceptibility measurements for subsurface modelling), for example for trace amounts of magnetite in carbonatite (SiilinjÀrvi) and native iron occurrence in basalt (Qullissat). Technical achievements were the integration of a multicopter-based prototype fluxgate-magnetometer data from different survey altitudes with ground truth, and a feasibility study with a high-speed multispectral image system for fixed-wing UAS.
The employed case studies transfer the experiences made towards general recommendations for UAS application-based multi-sensor integration. This thesis highlights the feasibility of UAS-based surveying at target scale (1â50 km2) and solidifies versatile survey approaches for multi-sensor integration.Ziel dieser Arbeit war es, das Potenzial einer Drohnen-basierten Mineralexploration mit Multisensor-Datenintegration unter Verwendung optisch-spektroskopischer und magnetischer Methoden zu untersuchen, um u. a. ĂŒbertragbare ArbeitsablĂ€ufe zu erstellen.
Die untersuchte Literatur legt nahe, dass Drohnen-basierte Bildspektroskopie und magnetische Sensoren ein ausgereiftes technologisches Niveau erreichen und erhebliches Potenzial fĂŒr die Anwendungsentwicklung bieten, aber es noch keine ausreichende Synergie von hyperspektralen und magnetischen Methoden gibt.
Diese Arbeit umfasste drei Fallstudien, bei denen die DrohnengestĂŒtzte Vermessung von geologischen Zielen in subarktischen bis arktischen Regionen angewendet wurde.
Eine Kombination von Drohnen-Technologie mit RGB, Multi- und Hyperspektralkameras und Magnetometern ist vorteilhaft und schuf die Grundlage fĂŒr eine integrierte Modellierung in den Fallstudien.
Die Untersuchungen wurden in einem GelĂ€nde mit flacher und zerklĂŒfteter Topografie, verdeckten Zielen und unter oft schlechten LichtverhĂ€ltnissen durchgefĂŒhrt. Unter diesen Bedingungen war es das Ziel, die Anwendbarkeit von Drohnen-basierten Multisensordaten in verschiedenen Explorationsumgebungen zu bewerten.
Hochauflösende OberflĂ€chenbilder und Untergrundinformationen aus der Magnetik wurden fusioniert und gemeinsam interpretiert, dabei war eine selektive Gesteinsprobennahme und Analyse ein wesentlicher Bestandteil dieser Arbeit und fĂŒr die Validierung notwendig.
FĂŒr eine EisenerzlagerstĂ€tte wurde eine einfache RessourcenschĂ€tzung durchgefĂŒhrt, indem Magnetik, bildspektroskopisch-basierte Indizes und 2D-Strukturinterpretation integriert wurden. Fotogrammetrische 3D-Modellierung, magnetisches forward-modelling und hyperspektrale Klassifizierungen wurden fĂŒr eine Karbonatit-Intrusion angewendet, um einen kompletten Explorationsabschnitt zu erfassen. Eine Vektorinversion von magnetischen Daten von Disko Island, Grönland, wurden genutzt, um groĂrĂ€umige 3D-Modelle von undifferenzierten Erdrutschblöcken zu erstellen, sowie diese zu identifizieren und zu vermessen.
Die integrierte spektrale und magnetische Kartierung in komplexen Gebieten verbesserte die Erkennungsrate und rĂ€umliche Auflösung von Erkundungszielen und reduzierte Zeit, Aufwand und benötigtes Probenmaterial fĂŒr eine komplexe Interpretation.
Der Prototyp einer Multispektralkamera, gebaut fĂŒr eine StarrflĂŒgler-Drohne fĂŒr die schnelle Vermessung, wurde entwickelt, erfolgreich getestet und zum Teil ausgewertet.
Die vorgelegte Arbeit zeigt die Vorteile und Potenziale von Multisensor-Drohnen als praktisches, leichtes, sicheres, schnelles und komfortabel einsetzbares geowissenschaftliches Werkzeug, um digitale Modelle fĂŒr prĂ€zise Rohstofferkundung und geologische Kartierung zu erstellen
Whatâs Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter
© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter
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Modeling, Predicting and Capturing Human Mobility
Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility
Risk Management in Environment, Production and Economy
The term "risk" is very often associated with negative meanings. However, in most cases, many opportunities can present themselves to deal with the events and to develop new solutions which can convert a possible danger to an unforeseen, positive event. This book is a structured collection of papers dealing with the subject and stressing the importance of a relevant issue such as risk management. The aim is to present the problem in various fields of application of risk management theories, highlighting the approaches which can be found in literature
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