Marketing & Content Topics
Marketing strategy, content marketing, campaign management, and digital marketing. Covers content strategy, campaign execution, and marketing analytics.
Google Analytics and Web Metrics
Comprehensive knowledge of Google Analytics 4 and broader web analytics practices for measuring, analyzing, and translating website and application behavior into business decisions. Candidates should be able to navigate the Google Analytics 4 interface and configure properties and data streams, explain how data is collected through tracking snippets and event instrumentation, and distinguish user based metrics such as users from session based metrics such as sessions. Core metric fluency includes sessions, users, engagement rate, bounce rate, average session duration, pages per session, and conversion rate and when to use each. The scope includes designing and implementing conversion tracking and conversion events, defining goals and funnels, analyzing user behavior flows and identifying funnel drop off points, creating segments and performing cohort analysis, and using campaign tagging parameters for marketing attribution. Candidates should also understand attribution models including first click, last click, and multi touch, be able to diagnose data quality and tracking issues such as missing events, duplicate hits, cross domain problems, and consent related gaps, interpret campaign and product performance, and recommend measurement improvements and controlled experiments to validate hypotheses.
Content Performance and Measurement
Designing, measuring, and analyzing content effectiveness across acquisition, engagement, conversion, retention, and brand impact. This topic covers core metrics and key performance indicators such as sessions, users, pageviews, traffic sources, time on page, scroll depth, bounce rate, reach, impressions, likes, shares, comments, form submissions, cost per lead, click through rate, conversion rate, retention cohorts, and brand lift, and what each reveals about audience behavior and content performance. Candidates should be able to explain how to access and validate these metrics in analytics platforms, create and enforce an event taxonomy and campaign tagging strategy, ensure data quality, and select and instrument tracking methods and tools. It includes synthesizing metrics into actionable insights, setting targets and benchmarks, separating leading and lagging indicators, building dashboards and reporting cadences for stakeholders, and attributing content impact across channels and customer journeys. Measurement challenges such as multi touch attribution, offline and view through conversions, data gaps, and sample bias are in scope along with mitigation approaches such as incrementality testing, conversion modeling, first party data strategies, unified analytics platforms, and controlled experiments. Finally, the topic covers iterative optimization techniques including hypothesis driven experiments, split testing, personalization, search engine optimization, content gap analysis, cohort analysis, and how to tie content outcomes to business impact with quantitative examples.
Marketing Technology Evaluation and Selection
Covers understanding the marketing technology ecosystem and applying a repeatable framework to evaluate, compare, and select tools and vendors. Candidates should be familiar with common categories such as customer relationship management systems, marketing automation platforms, customer data platforms, web and product analytics, testing and experimentation tools, email and advertising systems, content and social management platforms, attribution and measurement solutions, and integration middleware. They should be able to explain the role each category plays in a stack, typical data flows and integration patterns, identity resolution and customer matching considerations, and how components interact to support measurement and personalization goals. The evaluation framework should include defining the business problem and success metrics, mapping required capabilities to vendor features, conducting gap analysis and weighted scorecards, and planning pilots or proofs of concept. Selection and trade off criteria should address total cost of ownership including licensing, implementation, integration, data migration, training, and ongoing maintenance; expected return on investment and measurable benefits; opportunity cost of not adopting; scalability and performance; security, privacy, and regulatory compliance; vendor reliability and commercial terms including service level agreements and data portability; user experience and adoption challenges; operational support and maintainability; and risks such as vendor lock in and technical debt. Candidates should also be able to discuss build versus buy trade offs, migration and exit planning, pilot success criteria and rollout sequencing, data governance and quality implications, and how priorities and selection trade offs shift with company stage, team capabilities, and budget.