*Changes may apply
AI Marketing Foundations & the Agentic Mindset
- The AI marketer: from channel manager to architect of end-to-end workflows that connect data, media, content, and product.
- The agentic organization: five pillars (business model, operating model, governance, workforce, technology & data) and what they mean for marketing teams.
- From lead generation to demand and pipeline – full-funnel thinking, and how AI changes speed, scale, and consumer expectations.
- Marketing Fundamentals: Role of marketing professional in the business process and managerial level, strategic influences and effects of marketing with R&D, product, finance, HR and more.
- Market research – hands on workshop
ICP, Market Intelligence & AI Content Strategy
- Market and category mapping in tech: defining ICPs, segments, and opportunity spaces using AI to synthesize signals from search, social, reviews, G2/Capterra, and industry reports.
- From isolated assets to content systems: content marketing as an always-on engine that supports search, social, lifecycle, and sales enablement.
- AI-driven content strategy: using models to identify topic clusters, content gaps, and audience intents, then translating them into a structured editorial roadmap.
- Value-based thinking: tying content initiatives to demand capture, demand creation, and retention — not vanity metrics.
Performance Marketing I: Search, Performance Max & App Growth
- Deep structure of performance ecosystems: Google Ads (Search, Performance Max, Demand Gen, YouTube) and display, with a focus on intent capture and AI-driven automation.
- App and mobile marketing foundations: app campaigns for install and engagement, store presence, deep linking, and how these connect to broader growth goals.
- Attribution basics for web and apps: install vs. in-app events, last-click vs. multi-touch, privacy constraints (iOS/ATT), and working with mobile measurement partners.
- How AI is embedded in performance: smart bidding, AI Max for Search, asset generation in PMax, and event-based optimization for downstream actions.
Performance Marketing II: Paid Social, B2B Platforms & AI Creative Systems
- Paid social landscape: Meta Ads, LinkedIn Campaign Manager: when to prioritize each for B2B vs. B2C, including mobile-first experiences.
- Audience design at scale: interest and behavioral targeting, lookalikes, account-based audiences, CRM uploads, and retargeting of app and web behaviors.
- Creative systems: modular concepts for feed, story, reels, and short-form video; UGC and AI co-creation for scripts, storyboards, and variants — operationalized via the “content generator” archetype.
- Synthetic audience testing and digital-twin A/B: testing creative on AI-generated personas before spending a single dollar.
- From volume to resonance — vibe marketing & generative media: orchestrating tone, visuals, pacing, and format so each touchpoint stays consistent with brand identity but responds to user context, device, and mood. Using AI co-creators (Pomelly, Veo, Lyria) to produce hyper-personalized variants at the speed mobile and social demand.
SEO, GEO & Answer-Engine Presence in the LLM Era
- Technical and strategic SEO foundations: crawlability, site architecture, Core Web Vitals, and structured data.
- Content strategy for discovery: topic clusters, intent mapping, long-form vs. micro-content, and authority signals.
- Generative Engine Optimization (GEO): designing content to be surfaced and cited by AI Overviews, ChatGPT Search, Perplexity, Claude, and Gemini answer surfaces.
- Entity-based optimization, llms.txt, and direct-answer patterns that make authoritative assets retriever-friendly.
Marketing Operations, Lifecycle, CRM & Agentic Automation
- Lifecycle frameworks in tech: onboarding, activation, feature adoption, monetization, retention, and expansion — and how each stage maps to a measurable revenue outcome.
- CRM and marketing-automation architecture: objects, events, scoring, segments, triggers, and the unified data layer – “defensible AI advantage.” Deep dives in HubSpot, Salesforce Marketing Cloud, and Marketo.
- Sales & Marketing alignment: defining MQLs, SQLs, and SALs; lead-handoff SLAs; pipeline reviews; and the rise of RevOps as the connective tissue between marketing automation, CRM, and the sales motion.
- Conversion Rate Optimization (CRO) as a continuous practice: hypothesis → experiment → ship; A/B and multivariate testing, multi-armed bandits, and AI-driven on-page personalization across landing pages, pricing pages, and product flows.
- Marketing Operations as a profession: the rise of the “marketing engineer” — non-technical marketers who manage agent fleets, build automations, and own the funnel like a product team.
- Agentic workflows inside lifecycle: agents that monitor behaviour, trigger playbooks, propose experiments, and route exceptions to humans “above the loop.”
Data, Analytics, Attribution & AI Co-Analysts
- Analytics foundations: event tracking, GA4 concepts, funnels, cohorts, and the move to event-based, privacy-first measurement.
- Instrumentation thinking with Google Tag Manager (GTM): event taxonomy, dataLayer design, server-side tagging, consent mode, and how marketing teams own the tagging contract with engineering.
- Attribution models in depth: last-click, first-click, linear, position-based, time-decay, data-driven attribution (DDA), and the comeback of Marketing Mix Modelling (MMM).
- Predictive analytics: using AI to interpret behavioral and social signals, predict churn and upsell propensity, score accounts, and surface anomalies before they become quarterly problems.
- AI as a co-analyst: transforming raw reports into narratives, anomaly detection, hypothesis generation, automated executive briefings, and stakeholder-ready insights.
- Data governance and quality: ensuring agentic workflows act on reliable signals, with confidence thresholds, source attribution, and human validation before insights drive spend or brand
App Economy & Hyper-Personalization at Scale
- App economy fundamentals: the mobile-first growth stack, why apps remain the highest-engagement surface in tech, and how mobile economics differ from web (LTV, retention curves, in-app monetization).
- App Store Optimization (ASO): keywords, metadata, screenshots, ratings, and conversion-rate factors on the App Store and Google Play. Using AI tools to automate keyword research, creative testing, and store-listing localisation across markets.
- App user acquisition and re-engagement: in-app advertising, rewarded video, interstitials, and in-app purchases. Mobile Measurement Partners (AppsFlyer, Adjust, Branch) and how iOS reshaped the measurement stack.
- Cross-channel orchestration: coordinating push, in-app messages, email, SMS, web, and ad retargeting for a single user — with AI agents deciding which channel wins, when, and with what message.
B2B AI-First Playbooks · LinkedIn, Podcasts, Thought Leadership & Executive Presence
- B2B motion essentials: from lead generation to demand creation, account targeting, and buying committees — and how AI changes research, outreach, and content velocity.
- LinkedIn as a B2B operating system: company pages, personal brands, employee advocacy, and pipeline-focused content formats.
- Thought-leadership engines: structuring ongoing content streams where AI assists with ideation, drafting, repurposing, and distribution while preserving the executive’s voice and “uncopyable” lived experience (Joaquín Cuenca Abela / Freepik thesis).
- Podcasts and executive presence: using audio and video to position experts, leveraging AI tools for scripting, show-notes, clips, and multi-channel distribution.
Capstone Presentations, Industry Critique & Career Activation
- Team capstone presentations to a panel of CMOs, growth leads, and recruiters from partner companies — covering business context, AI-first marketing architecture, sample journeys and media, and measurement.
- Career activation: mapping course outputs to portfolio assets, target role profiles (e.g., Senior Digital Marketing Manager, Lifecycle Lead, Growth PMM, Content Strategist, Marketing Ops Engineer), and a 90-day post-program learning plan.