AI in Digital Marketing Training Course

Participants can define priority audiences and assess whether available data supports AI applications.

• Jobs to Be Done interviews translated into audience needs
• STP segmentation using needs, value, behaviour and propensity signals
• Customer data inventory covering identifiers, events, consent and quality
• RFM analysis for recency, frequency and monetary value segmentation
• Propensity model concepts for response, churn and conversion likelihood
• Data-quality checks for completeness, duplication, timeliness and representativeness

Participants can write, test and refine prompts for repeatable marketing tasks.

• Prompt structure using role, task, context, constraints and format
• Few-shot prompting with approved examples and counterexamples
• Brand voice instructions built from tone, vocabulary and prohibited claims
• Source-grounded prompts requiring supplied documents and cited evidence
• Prompt comparison matrices assessing relevance, accuracy, originality and compliance
• Reusable prompt libraries organised by channel, audience and campaign stage

Participants can use AI to accelerate research while verifying findings against reliable evidence.

• Research brief design specifying decisions, hypotheses, sources and evidence thresholds
• Social listening themes extracted from reviews, comments and support interactions
• Thematic coding of interview transcripts using structured codebooks
• Competitor message audits comparing positioning, offers, proof and calls-to-action
• Synthetic persona limits when evidence is sparse or unrepresentative
• Fact-checking protocol using primary sources, dates, citations and confidence ratings

Participants can design AI-assisted content processes that maintain quality and accountability.

• Content pillar model linking audience needs to commercial objectives
• Editorial calendars balanced across campaign, lifecycle and always-on content
• AI drafting workflows for articles, landing pages, scripts and captions
• Claim substantiation checks before publishing regulated or technical content
• Content repurposing matrices converting one asset into channel-specific formats
• Editorial approval routes defining authors, reviewers, owners and escalation points

Participants can plan search content and optimisation activities for conventional and generative search results.

• Search intent classification across informational, commercial, transactional and navigational queries
• Keyword clustering using topic, intent, page type and conversion potential
• Topic clusters connecting pillar pages, supporting content and internal links
• On-page optimisation for titles, headings, entities, metadata and structured answers
• E-E-A-T signals through authorship, evidence, experience and source transparency
• Generative search visibility assessed through cited sources, entities and answer usefulness

Participants can apply AI-informed methods to paid search, social and display campaign planning.

• Media objectives translated into reach, traffic, lead and conversion targets
• Audience hypotheses documented before platform targeting and algorithmic optimisation
• Creative variation matrices testing message, format, offer and call-to-action
• Google Ads bidding options matched to conversion quality and data volume
• Meta Ads Manager audience controls, exclusions and placement decisions
• Budget pacing rules using marginal returns, learning periods and guardrails

Participants can design AI-supported lifecycle programmes using responsible personalisation principles.

• Lifecycle maps covering acquisition, onboarding, activation, retention and win-back
• Trigger design based on events, thresholds, preferences and customer status
• Next-best-action logic using eligibility, value, propensity and contact rules
• Email personalisation fields checked for relevance, consent and data accuracy
• Subject-line generation tested against brand, clarity and deliverability criteria
• Frequency caps protecting customer experience across automated communications

Participants can use AI to support social planning and community response without losing human judgement.

• Social content systems combining audience themes, formats and publishing cadence
• Sentiment analysis interpreted alongside sample comments and context
• Community response playbooks covering service issues, complaints and misinformation
• Escalation criteria for vulnerable customers, threats and reputational incidents
• Influencer screening using audience fit, engagement quality and disclosure requirements
• AI-generated visuals reviewed for representation, rights and brand suitability

Participants can specify useful conversational AI experiences and escalation routes.

• Conversation design using intents, entities, utterances and response patterns
• Service blueprints linking chatbot steps to people, systems and policies
• Knowledge-base preparation using approved answers, ownership and review dates
• Fallback responses handling uncertainty, ambiguity and unsupported customer requests
• Human handoff rules based on risk, value, complexity and sentiment
• Conversation analytics tracking containment, resolution, abandonment and satisfaction

Participants can commission and review AI-assisted creative assets with appropriate rights controls.

• Creative briefs specifying objective, audience, proposition, mandatories and deliverables
• Image-generation prompts controlling composition, lighting, subject and visual style
• Asset review checklists for anatomy, text, stereotypes and factual accuracy
• Copyright risk assessment for training data, inputs, outputs and licences
• Disclosure decisions for synthetic imagery, avatars and altered media
• Digital asset registers recording provenance, permissions, versions and expiry dates

Participants can measure AI-enabled marketing activity using credible metrics and test designs.

• Measurement frameworks linking business objectives, outcomes, outputs and leading indicators
• GA4 event design for acquisition, engagement, conversion and retention actions
• North Star metrics balanced with channel-level diagnostic measures
• A/B test design covering hypotheses, variants, sample size and decision rules
• Incrementality concepts separating campaign effect from existing customer demand
• Attribution model assumptions across last-click, data-driven and multi-touch approaches

Participants can establish governance controls for safe, accountable marketing AI use.

• AI risk register covering privacy, bias, security, accuracy and reputation
• Consent management requirements for profiling, targeting and automated decisions
• Data minimisation applied to prompts, uploads and vendor integrations
• Bias testing across protected groups, proxies, outcomes and exclusions
• Model cards documenting purpose, limitations, performance and intended users
• Incident response procedures for harmful outputs, data exposure and misinformation

Participants can plan a controlled implementation of priority AI marketing initiatives.

• RACI matrices assigning accountability for data, content, technology and approvals
• Vendor due diligence covering security, data retention, support and ownership
• Pilot charters defining scope, success measures, users and exit criteria
• Change plans addressing skills, communications, adoption and resistance
• Standard operating procedures for prompt use, review and version control
• Implementation roadmaps sequencing quick wins, dependencies and scale decisions

Participants can assemble campaign components into an AI-enabled customer acquisition or retention plan.

• Campaign briefs connecting customer problem, proposition, channels and commercial targets
• Audience priority choices justified through value, need and reachable data
• Content journey mapping from awareness through conversion and post-purchase engagement
• Channel plans allocating roles to search, social, email and conversational touchpoints
• AI workflow maps showing inputs, outputs, reviews and escalation decisions
• Measurement scorecards defining baselines, targets, owners and reporting intervals

Participants can use performance evidence to improve AI-enabled campaigns without chasing misleading signals.

• Performance review cadences separating daily signals from strategic learning
• Funnel diagnostics locating drop-off across reach, engagement, conversion and retention
• Creative fatigue indicators using frequency, response decline and sentiment changes
• Audience performance analysis distinguishing correlation from causal campaign effects
• Optimisation backlogs prioritised by expected impact, effort and confidence
• Decision logs recording evidence, changes, owners and intended outcomes

Participants can build, review and present a complete AI-enabled digital marketing plan for a defined business challenge.

• Business challenge framed through audience, objective, proposition and success measures
• Use-case portfolio prioritised with impact, feasibility, risk and data readiness
• Channel strategy integrating search, paid media, CRM, social and content
• AI content workflow documented with prompts, approvals, sources and quality checks
• Governance controls covering consent, claims, bias, intellectual property and escalation
• Measurement plan specifying baselines, experiments, attribution assumptions and reporting cadence