AI-Powered B2B Marketing

Höfundur: Simon Hall (Útgáfa: 1)
AI-Powered B2B Marketing

Kaup valmöguleikar

How can mid-career B2B marketers effectively harness AI to build strategy and drive career impact? AI-Powered B2B Marketing offers clear, practical frameworks for professionals to implement AI technologies confidently and strategically. Backed by real-world examples and expert insights from global leaders including Epsilon, CACI UK and UrSpectr, this guide demystifies AI integration across B2B marketing.

From powering market research to embedding ethical AI practices, it equips marketers to optimize every stage of the buyer journey while developing skills that elevate career credibility. You'll learn how to: - Develop strategic AI-driven frameworks tailored for B2B marketing - Implement actionable tools for lead generation, nurturing and account management - Apply principles of ethical AI use within marketing strategies - Optimize technology selection to align with business objectives Written by marketing innovator Simon Hall, this authoritative guide helps marketers transform B2B strategies, delivering measurable results and positioning you for career growth.

Nánar um bókina

Útgefandi
Kogan Page
ISBN
9781398621978
Print ISBN
9781398621961
Format
ePub
Útgáfa
1
Höfundar
Simon Hall
Tungumál
English
Útgefið
2025-12-03
Prent takmörkun á líftíma
100
Prent takmörkun
10
Afritunar takmörkun
10

Kaflar

  • Cover
  • Endorsements
  • Titlepage
  • Dedication
  • Contents
  • List of figures and tables
  • Figures
  • Tables
  • About the author
  • Preface
  • Introduction
  • What you will gain from this chapter
  • Introduction
  • What is AI?
  • History of AI in marketing
  • B2B marketing and AI
  • Legislation and ethics
  • The recent explosion of AI marketing technology
  • AI B2B marketing maturity model
  • Main risks in using AI in B2B marketing
  • What this book is and isn’t
  • The AI B2B marketing framework
  • References
  • Further reading
  • PART ONE AI-based market and customer research
  • 01 AI-powered market research
  • What you will gain from this chapter
  • Introduction
  • Qualitative and quantitative market research
  • AI and market research phases
  • The emergence of AI in market research
  • Types of AI in market research
  • Survey ideation
  • Market sizing
  • AI for market trends
  • Examples of AI use in market research
  • AI and market hypotheses
  • AI for competitive research
  • References
  • Further reading
  • 02 AI for customer insights
  • What you will gain from this chapter
  • Introduction
  • The role of AI in customer research
  • Creating a buyer persona
  • The importance of the command
  • How to build using machine learning: summary
  • Media preferences
  • B2B communities
  • The decision-making unit
  • Customer segmentation and AI
  • Customer journey maps
  • Voice of customer analysis
  • Customer data platforms
  • Reference
  • Further reading
  • PART TWO Awareness and content marketing
  • 03 AI-powered advertising
  • What you will gain from this chapter
  • Introduction
  • Types of B2B digital advertising
  • AI and advertising
  • Cons of using AI in advertising
  • AI features in social media advertising
  • Other AI-based advertising tools
  • Ad fraud detection
  • Tone of voice in PR
  • Influencer identification
  • Sentiment analysis
  • Endnotes
  • Reference
  • Further reading
  • 04 AI and SEO
  • What you will gain from this chapter
  • What is SEO?
  • B2B on-page SEO
  • SEO data and AI
  • Google Search and AI
  • Keyword identification
  • Dynamic ranking
  • Voice-based search and AI in B2B
  • Link building
  • References
  • Further reading
  • 05 Content marketing
  • What you will gain from this chapter
  • Introduction
  • Generative AI
  • Evolution of AI in content marketing
  • Role of AI
  • Creating content briefs
  • Content planning and ideation
  • Content creation and AI
  • Copywriting AI tools
  • Structures and processes in using machine learning, tools
  • Video AI tools
  • Image AI tools
  • Content optimization
  • Content repurposing
  • Content localization and translation
  • Content distribution
  • Limitations in generative AI
  • Reference
  • Further reading
  • PART THREE AI lead generation and lead nurturing
  • 06 AI and account prospecting
  • What you will gain from this chapter
  • Introduction
  • Account prospecting and AI
  • Website visitor identification
  • Finding account information
  • Lookalike accounts
  • LinkedIn sales navigator
  • The DMU
  • Buyer triggers
  • Account prospecting and intent
  • Predicting CLV with AI
  • AI tools for financial analysis of accounts
  • Account mapping
  • Social media for account analysis
  • Technographic profiling
  • Reference
  • Further reading
  • 07 Lead capture and AI
  • What you will gain from this chapter
  • Introduction
  • AI role in lead generation
  • Lookalike audiences
  • Lead magnets
  • AI and call to actions (CTA)
  • Timing of gating content and AI
  • Lead enhancement and data cleansing
  • Using AI chatbots for lead capture
  • Automated lead qualification and segmentation
  • Landing pages
  • AI and social media lead capture
  • References
  • Further reading
  • 08 AI-powered lead nurturing
  • What you will gain from this chapter
  • Introduction
  • Importance of lead nurturing
  • Lead nurture campaigns
  • Behavioural analysis and segmentation
  • Automated workflow creation
  • Multi-channel nurturing
  • Lead scoring
  • Social media automation and AI
  • AI and email nurturing
  • LinkedIn and lead nurturing
  • LinkedIn-based AI tools
  • Automated follow-ups
  • References
  • Further reading
  • PART FOUR AI for optimizing customer relationships
  • 09 Personalization and AI
  • What you will gain from this chapter
  • Introduction
  • Role of AI in personalization
  • Content personalization with AI
  • Blogs and personalization
  • Videos
  • Personalizing email
  • White papers
  • Webinars
  • Ebooks/guides
  • Presentations
  • Survey and poll personalization
  • Podcasts and scripts
  • Further reading
  • 10 Conversational technology
  • What you will gain from this chapter
  • Introduction
  • Main use cases for conversational technology
  • Chatbots
  • Types of chatbots
  • Qualifying leads
  • Conversational/revenue assistants
  • Challenges in implementing chatbots
  • Limitations of AI conversational technology
  • Chatbots and GDPR
  • References
  • Further reading
  • 11 B2B events and AI
  • What you will gain from this chapter
  • Introduction
  • Types of events
  • Events data capture
  • Areas of events marketing
  • Pre-event planning
  • During events
  • Post event
  • Using AI to gamify events
  • AI and virtual events
  • References
  • Further reading
  • 12 AI for customer retention marketing
  • What you will gain from this chapter
  • Introduction
  • Predictive analytics
  • Code-free predictive analytics
  • Cross-selling and upselling – development
  • AI for identifying business development opportunities
  • Intent data for development of accounts
  • Customer loyalty
  • Customer lifetime value
  • Customer onboarding
  • AI for gamification
  • Real-world example
  • References
  • Further reading
  • PART FIVE AI management and planning
  • 13 Data and privacy
  • What you will learn from this chapter
  • Introduction
  • Types of data
  • Data collection
  • Challenges in data collection
  • First-and third-party data
  • Data privacy regulations and AI
  • Considerations regarding data when using AI tools
  • Risks of data breaches or unauthorized access to AI tools
  • AI risks vs non-AI
  • Transparency AI-driven marketing practices
  • Consent and opt-in
  • Data minimization
  • Training employees
  • References
  • Further reading
  • 14 Ethics
  • What you will learn from this chapter
  • Introduction
  • Ethical use of data
  • Bias and fairness in AI tools
  • How to make AI ethical
  • Ensuring ethical AI
  • AI marketing accountability
  • Regulatory compliance and ethical standards
  • AI misuse in B2B marketing
  • AI compliance best practices
  • Ethical considerations for generative AI
  • Real-world example
  • References
  • Further reading
  • 15 AI-powered analytics
  • What you will gain from this chapter
  • The evolution of marketing analytics
  • Types of AI tools for marketing performance analysis
  • Real-time data analysis
  • AI for forecasting marketing results
  • Post-marketing campaign optimization and AI
  • Marketing budget management and tracking
  • Challenges in using AI for performance analysis
  • AI for social media marketing performance
  • AI-powered dashboards
  • ROI and attribution tools
  • References
  • Further reading
  • 16 Marketing operations and AI
  • What you will learn from this chapter
  • Introduction
  • Marketing collaboration
  • Project and workflow management
  • Microsoft Team Copilot
  • Knowledge sharing
  • Limitations or considerations
  • Internal communication translation
  • Internal communication improvement
  • Task review through visual aids
  • Automated data visualization with AI
  • Proofreading with AI
  • Real-world example
  • References
  • Further reading
  • 17 Technology planning
  • What you will gain from this chapter
  • Introduction
  • The process
  • Step 1: Objectives, use case and prioritization
  • Step 2: Assess current in-house technologies
  • Step 3: Research AI tools
  • Step 4: Test and roll-out
  • Step 5: Securing buy-in for roll-out
  • Step 6: Roll-out
  • Budgeting for AI
  • Reference
  • Further reading
  • Index
  • Copyright