Articles

Assess voice, gesture and automated customer journeys through confirmation, accessibility, error recovery and task completion.

Compare Claude subscriptions and API access through draft quality, review time, permissions and the full cost of useful marketing work.

Compare ChatGPT plan prices, Business seat options and separate API charges, with a practical budget example for marketing teams.

Understand training data, retrieved context and evaluation, with checks for outcome labels, data leakage and reliable marketing predictions.

What research on AI and critical thinking means for marketers, with practical checks for claims, comparisons, quotations and calculations.

Understand how generative and multimodal AI differ, where they overlap, and how to evaluate marketing tasks using text, images and other inputs.

Learn where Claude fits in research, campaign briefs and copy drafting, with a practical division of work and checks before publication.

Explore marketing predictions, clustering and recommendations, with clear guidance on baselines, precision, recall and evaluating new data.

Assess avatars, AI assistants and digital experiences against customer tasks, accessibility, accurate claims and useful engagement.

Understand AI capabilities in marketing, from prediction to generated content, and how to evaluate a useful workflow before expanding it.

Connect campaigns and CRM records with clear identifiers, field ownership and reconciliation. Includes a field map and acceptance checks.

Plan a marketing AI pilot with a defined task, test set, quality gates, cost measures and a clear decision on whether to ...

Build marketing research around primary sources, checked citations, consistent comparisons and a clear distinction between facts and inference.

Start with a bounded reporting workflow. Define agent permissions, handle partial failures and measure accepted output, review time and cost.

Understand agent instructions, models, tools and checks, and what to assess before allowing software to act on marketing data or campaigns.

What Meta's January 2025 memory announcement said, how personalization differs from advertiser access, and what marketers can assess.

Compare data quality tools for CRM records using duplicate handling, validation, integration and review controls, with a practical scorecard.

Connect customer feedback with behavioral data. Use topic analysis, clear comparisons and focused tests to improve the customer journey.

Compare marketing CRMs through source capture, qualification, ownership, duplicate handling and reporting, using a practical lead-handoff checklist.

Assess marketing automation through routing rules, exceptions, retries, permissions and the cost of maintaining low-code or no-code workflows.

Explore marketing warehouse use cases in retail, B2B and subscriptions, with checks for record grain, joins, timing and reliable reporting.

Find marketing patterns worth testing. Compare mining methods and assess associations, data quality and the difference between correlation and cause.

Evaluate visual search, catalog tagging and virtual try-on through product accuracy, relevant results and useful customer outcomes.

Prioritize marketing data-science projects by the decision, available records and evidence needed across acquisition, retention and forecasting.

Build marketing chatbots around customer tasks, approved product information, clear human handoffs and measures of answer quality.

Take a marketing data-science project from question to action with clear outcomes, reliable records, a baseline and a practical evaluation plan.

Compare marketing SaaS through workflow fit, charging units, AI costs, implementation effort and data export before choosing a supplier.

Choose NLP methods for feedback, sentiment, search and summaries, then evaluate labels, ambiguity and source accuracy before acting.

Choose marketing charts that answer a clear question, with consistent denominators, fair comparisons and accessible presentation.

Connect payment events to marketing records with clear ownership, duplicate handling and checks for failed payments, refunds and retries.

Choose evidence for a marketing decision by the question, sample quality and missing groups, and combine qualitative and quantitative findings.

Build a marketing analytics routine around clear metrics, reliable comparisons and actions, with checks for misleading changes in performance.

Assess AR and VR marketing through product understanding, accurate assets, device access and comparison with conventional product content.

Understand how marketing data management, analytics and data science fit together, with clear ownership and a practical campaign example.