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Produkt zum Begriff Big Data Analytics:


  • Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World
    Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World

    Distill Maximum Value from Your Digital Data! Do It Now!Why hasn’t all that data delivered a whopping competitive advantage? Because you’ve barely begun to use it, that’s why! Good news: neither have your competitors. It’s hard! But digital marketing analytics is 100% doable, it offers colossal opportunities, and all of the data is accessible to you. Chuck Hemann and Ken Burbary will help you chop the problem down to size, solve every piece of the puzzle, and integrate a virtually frictionless system for moving from data to decision, action to results! Scope it out, pick your tools, learn to listen, get the metrics right, and then distill your digital data for maximum value for everything from R&D to customer service to social media marketing!Prioritize—because you can’t measure and analyze everything Use analysis to craft experiences that profoundly reflect each customer’s needs, expectations, and behaviors Measure real digital media ROI: sales, leads, and customer satisfaction Track the performance of all paid, earned, and owned digital channels Leverage digital data way beyond PR and marketing: for strategic planning, product development, and HR Start optimizing digital content in real time Implement advanced tools, processes, and algorithms for accurately measuring influence Make the most of surveys, focus groups, and offline research synergies Focus new marketing investments where they’ll deliver the most value • Identify and understand your most important audiences across the digital ecosystem“Chuck and Ken lead marketers clearly and efficiently through the minefield of digital marketing measurement. And they do so with a lightness of touch and absence of jargon so rare in this overhyped, much-misunderstood ecosystem.” —Sam Knowles, Founder & MD of Insight Agents; author of Narrative by Numbers: How to Tell Powerful & Purposeful Stories with Data

    Preis: 29.95 € | Versand*: 0 €
  • Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python
    Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python

    Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications.   Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis.   Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

    Preis: 48.14 € | Versand*: 0 €
  • Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data
    Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data

    The Definitive Guide to Enterprise-Level Analytics Strategy, Technology, Implementation, and Management Organizations are capturing exponentially larger amounts of data than ever, and now they have to figure out what to do with it. Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage. Suddenly, you can go beyond understanding “how, when, and where” events have occurred, to understand why – and use this knowledge to reshape the future. Now, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) have brought together the latest techniques, best practices, and research on analytics in a single primer for maximizing the value of enterprise data. Enterprise Analytics is today’s definitive guide to analytics strategy, planning, organization, implementation, and usage. It covers everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. The authors offer specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions. They support their powerful techniques with many real-world examples, including chapter-length case studies from healthcare, retail, and financial services. Enterprise Analytics will be an invaluable resource for every business and technical professional who wants to make better data-driven decisions: operations, supply chain, and product managers; product, financial, and marketing analysts; CIOs and other IT leaders; data, web, and data warehouse specialists, and many others.

    Preis: 22.46 € | Versand*: 0 €
  • Network Security with Netflow and IPFIX: Big Data Analytics for Information Security
    Network Security with Netflow and IPFIX: Big Data Analytics for Information Security

    A comprehensive guide for deploying, configuring, and troubleshooting NetFlow and learning big data analytics technologies for cyber security   Today’s world of network security is full of cyber security vulnerabilities, incidents, breaches, and many headaches. Visibility into the network is an indispensable tool for network and security professionals and Cisco NetFlow creates an environment where network administrators and security professionals have the tools to understand who, what, when, where, and how network traffic is flowing.   Network Security with NetFlow and IPFIX is a key resource for introducing yourself to and understanding the power behind the Cisco NetFlow solution. Omar Santos, a Cisco Product Security Incident Response Team (PSIRT) technical leader and author of numerous books including the CCNA Security 210-260 Official Cert Guide, details the importance of NetFlow and demonstrates how it can be used by large enterprises and small-to-medium-sized businesses to meet critical network challenges. This book also examines NetFlow’s potential as a powerful network security tool.   Network Security with NetFlow and IPFIX explores everything you need to know to fully understand and implement the Cisco Cyber Threat Defense Solution. It also provides detailed configuration and troubleshooting guidance, sample configurations with depth analysis of design scenarios in every chapter, and detailed case studies with real-life scenarios.   You can follow Omar on Twitter: @santosomar   NetFlow and IPFIX basics Cisco NetFlow versions and features Cisco Flexible NetFlow NetFlow Commercial and Open Source Software Packages Big Data Analytics tools and technologies such as Hadoop, Flume, Kafka, Storm, Hive, HBase, Elasticsearch, Logstash, Kibana (ELK) Additional Telemetry Sources for Big Data Analytics for Cyber Security Understanding big data scalability Big data analytics in the Internet of everything Cisco Cyber Threat Defense and NetFlow Troubleshooting NetFlow Real-world case studies    

    Preis: 25.67 € | Versand*: 0 €
  • Was ist das Big Data?

    Was ist das Big Data? Big Data bezieht sich auf die riesigen Mengen an Daten, die in unserer digitalen Welt generiert werden. Diese Daten stammen aus verschiedenen Quellen wie sozialen Medien, Sensoren, Mobilgeräten und mehr. Big Data zeichnet sich durch die 3Vs aus: Volumen, Vielfalt und Geschwindigkeit. Unternehmen nutzen Big Data, um Muster und Trends zu erkennen, fundierte Entscheidungen zu treffen und ihre Geschäftsprozesse zu optimieren. Es erfordert spezielle Tools und Technologien wie Data Mining, maschinelles Lernen und künstliche Intelligenz, um Big Data effektiv zu verarbeiten und zu analysieren.

  • Was ist der Unterschied zwischen Big Data und Smart Data?

    Big Data bezieht sich auf große Mengen von Daten, die aus verschiedenen Quellen stammen und oft unstrukturiert sind. Smart Data hingegen bezieht sich auf die Analyse und Nutzung dieser Daten, um wertvolle Erkenntnisse und Handlungsempfehlungen zu generieren. Smart Data konzentriert sich auf die Auswahl und Verarbeitung relevanter Daten, um konkrete Probleme zu lösen oder Entscheidungen zu unterstützen.

  • Wie beeinflusst Big Data die datengesteuerte Entscheidungsfindung in verschiedenen Branchen?

    Big Data ermöglicht es Unternehmen, riesige Mengen an Daten zu sammeln, zu analysieren und zu interpretieren, um fundierte Entscheidungen zu treffen. In der Finanzbranche kann Big Data beispielsweise genutzt werden, um Risiken zu minimieren und Investitionsentscheidungen zu optimieren. In der Gesundheitsbranche kann Big Data dazu beitragen, personalisierte Behandlungspläne zu erstellen und die Effizienz von medizinischen Verfahren zu verbessern.

  • Was ist wichtig bei Social Media Marketing?

    Was ist wichtig bei Social Media Marketing?

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  • Social Marketing
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  • Pauliks, Kevin: Meme Marketing in Social Media
    Pauliks, Kevin: Meme Marketing in Social Media

    Meme Marketing in Social Media , Meme Marketing ist zu einer gängigen Medienpraxis in der Werbewirtschaft geworden. Viele Unternehmen werben in den Sozialen Medien mit Memes, um Aufmerksamkeit für ihre Marken, Produkte und Dienstleistungen zu generieren sowie zu signalisieren, dass sie zur digitalen Medienkultur dazugehören. Die Verwendung von Memes ist allerdings risikobehaftet. Wenn Werbende nicht über die Medienpraktiken des Memeing Bescheid wissen, besteht die Gefahr, dass sie Memes falsch verwenden und von ihrer Zielgruppe verlacht oder ausgeschlossen werden. Subkulturen auf Plattformen wie Reddit achten penibel darauf, ihre Medienkultur vor Außenstehenden zu schützen. Werbende stehen dort unter Verdacht, Memes nur für Profite auszunutzen. Wie verhält sich nun das anti-kommerzielle Produzieren, Zirkulieren und Rezipieren von Memes in den Sozialen Medien zur visuellen Verwendung von Memes in der Werbung? Diese Frage beantwortet Kevin Pauliks in acht medienpraxeografischen Proben, die den Unterschied von Memeing und Meme Marketing untersuchen und anhand unterschiedlicher Marken wie IKEA, Gucci, Siemens, Sixt aufzeigen, wie Memes in der Werbung verwendet werden. , Bücher > Bücher & Zeitschriften

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    Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications.   Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis.   Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

    Preis: 36.37 € | Versand*: 0 €
  • Wie können Unternehmen Social-Media-Marketing nutzen, um ihre Markenpräsenz und Kundenbindung zu stärken?

    Unternehmen können Social-Media-Marketing nutzen, um ihre Markenpräsenz zu stärken, indem sie regelmäßig relevante und ansprechende Inhalte teilen, mit ihren Kunden interagieren und deren Feedback ernst nehmen. Durch gezielte Werbung und Influencer-Marketing können sie zudem neue Zielgruppen erreichen und ihre Reichweite erhöhen. Indem sie auf Social-Media-Plattformen präsent sind, können Unternehmen auch schnell auf aktuelle Trends reagieren und ihre Kundenbindung durch persönliche Ansprache und individuelle Angebote stärken.

  • Wie können Unternehmen effektives Social Media Marketing betreiben, um ihre Reichweite und Kundenbindung zu verbessern?

    Unternehmen können effektives Social Media Marketing betreiben, indem sie regelmäßig relevanten und ansprechenden Content teilen, mit ihren Followern interagieren und auf deren Feedback eingehen. Zudem sollten sie gezielte Werbekampagnen schalten, um ihre Zielgruppe zu erreichen und ihre Reichweite zu erhöhen. Durch die Nutzung von Analysen und Statistiken können Unternehmen außerdem ihre Social Media Strategie kontinuierlich optimieren und die Kundenbindung stärken.

  • Was sind die potenziellen Auswirkungen von Big Data auf die Privatsphäre und Datensicherheit?

    Die potenziellen Auswirkungen von Big Data auf die Privatsphäre sind eine erhöhte Gefahr der Datenmissbrauch und -diebstahl, da große Mengen sensibler Informationen gesammelt werden. Zudem kann die Profilierung von Nutzern durch die Analyse von Big Data zu einer Verletzung der Privatsphäre führen. Schließlich könnten Regierungen und Unternehmen Big Data nutzen, um Bürger und Kunden zu überwachen und zu kontrollieren.

  • "Was sind die wichtigsten Strategien für erfolgreiches Social Media Marketing?"

    Die wichtigsten Strategien für erfolgreiches Social Media Marketing sind eine klare Zielsetzung, regelmäßige Interaktion mit der Zielgruppe und die Nutzung von relevantem Content. Zudem ist es wichtig, auf Trends und Entwicklungen in den sozialen Medien zu reagieren und die Ergebnisse regelmäßig zu analysieren, um die Strategie anzupassen und zu optimieren. Eine konsistente Markenpräsenz und die Nutzung von verschiedenen Plattformen sind ebenfalls entscheidend für den Erfolg im Social Media Marketing.

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