Marketing & Content Topics
Marketing strategy, content marketing, campaign management, and digital marketing. Covers content strategy, campaign execution, and marketing analytics.
Search Engine Optimization Metrics and Analytics
Comprehensive coverage of measuring and interpreting search engine optimization performance and designing key performance indicators that map to business goals. Includes which metrics to track and why, such as organic traffic, impressions, clicks, click through rate from search results, keyword rankings and average position, backlink profile metrics, Core Web Vitals and page speed, user engagement and bounce behavior, conversion rates and conversion attribution for organic channels, organic customer acquisition cost, and revenue attributable to organic search. Covers diagnostic signals and tradeoffs including how to interpret ranking versus traffic signals, click yield versus ranking, the typical lag between optimization work and ranking changes, and how to set realistic expectations by objective. Also describes practical use of tools and data sources such as Google Search Console and other search analytics platforms, methods for identifying technical and content opportunities from search data, designing dashboards and reporting for marketing and product stakeholders, prioritizing KPI sets by stakeholder and objective, and common attribution challenges and approaches.
Multi Channel Content Strategy and Distribution
Covers end to end development and execution of integrated content programs that deliver consistent messaging and measurable business outcomes across multiple distribution channels. Candidates should demonstrate audience research and segmentation, mapping content to buyer journey stages, and channel selection reasoning for blogs, social media, email, video, podcasts, web pages, landing pages, paid placements, search engine discoverability, and partner or creator collaborations. Topics include defining core messaging and brand narrative while tailoring tone, format, and delivery for each channel; channel specific formatting and optimization guidelines; frequency and cadence planning and editorial calendars; repurposing strategies to maximize content reuse; organic and paid distribution tactics and amplification strategies; platform algorithm and best practice considerations; measurement frameworks and key performance indicators for traffic, engagement, lead generation, conversion, retention, and lifetime value; attribution models and experiment design to evaluate cross channel impact; prioritization and resource allocation across channels given constraints; scaling evergreen content; and governance for content operations including roles workflows review processes and tooling such as content management systems marketing automation analytics and distribution platforms. Senior level discussion also includes building test and learn frameworks for content experiments designing attribution approaches for multi touch funnels and establishing operating models to scale content teams and cross functional coordination.
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.
Display Advertising and Programmatic Marketing
Comprehensive knowledge of display advertising and the digital advertising ecosystem, including how inventory is bought and sold and how programmatic buying operates. Candidates should be able to explain programmatic channels such as open auction, private marketplace, and programmatic direct, and describe the roles and interactions of demand side platforms, supply side platforms, ad exchanges, ad networks, and ad servers. Understand common ad formats including banner ads, video ads, native ads, rich media, and connected television, and the creative considerations for each, including dynamic creative optimization and split testing. Cover audience targeting approaches such as contextual targeting, behavioral targeting, demographic targeting, first party data and third party data usage, lookalike modeling, segmentation, retargeting strategies, frequency capping, and cross device targeting. Be prepared to discuss campaign strategy and operations including campaign setup, budget pacing, bid strategies, floor pricing, header bidding, and integration with walled gardens. Measurement and optimization topics should include impressions, click through rate, view through rate, reach and frequency, conversion tracking, cost per thousand impressions, cost per click, cost per acquisition, return on ad spend, key performance indicators, attribution approaches such as last click and multi touch attribution, viewability, brand safety, fraud detection and verification, and lift testing. Finally, address privacy and identity implications such as cookie deprecation, first party identity solutions, consent management, general data protection regulation, and California consumer privacy act, and explain how display supports upper funnel goals such as brand awareness and consideration and how to measure and optimize toward those objectives.
Occasion Based and Cultural Moment Marketing
Covers how to identify, plan for, and execute campaigns tied to seasonal, cultural, or event driven moments. Candidates should demonstrate an ability to map customer behavior to calendar and cultural signals, plan campaign briefs and creative tailored to moments, work with localization and partnerships teams, coordinate timing and logistics across channels, and measure incremental impact. Also include risk and sensitivity checks for culturally sensitive content and approaches to rapid creative iteration.
Content and Editorial Performance Optimization
Measures and improves the performance of content and editorial work by combining quantitative analytics and qualitative research to increase discoverability, engagement, and conversions. Core skills include defining and tracking key performance indicators such as organic traffic, engagement metrics, and conversion rates; diagnosing whether pages suffer from low traffic versus low engagement; using analytics platforms and search console data to identify opportunity pages, content gaps, and keyword opportunities; applying on page search engine optimization including titles, headers, and metadata; designing and running A B testing for headlines, layouts, placements, and calls to action; prioritizing improvements by impact and effort to build data driven content roadmaps; iteratively testing and measuring changes; translating insights into editorial and product decisions; and documenting results and learnings to scale improvements across the content portfolio.
Content Attribution and Channel Analysis
Covers methods and trade offs for assigning credit to content and marketing channels in multi touch, multi channel environments. Topics include common attribution models such as last click, first click, time decay, position based, and fractional multi touch allocation, plus algorithmic and data driven attribution approaches. Discuss practical challenges including cross device tracking, offline conversions, attribution windows, sampling bias, delayed conversions, signal sparsity, and data quality issues. Includes using experiments and holdout tests, causal inference techniques, and marketing mix modeling to validate or supplement attribution. Emphasizes how to analyze channel performance with imperfect signals, make resource allocation decisions, prioritize content investments, and balance quantitative measurement with qualitative judgment and business context.
Content Performance Metrics
Measurement and evaluation of content effectiveness across the customer journey. Covers primary content metrics such as traffic, engagement, time on page, conversion rates and lead generation; secondary metrics like bounce rate, shares, and scroll depth; business outcome metrics such as leads and revenue attributable to content; differentiating metrics by content type and funnel stage; understanding statistical significance, correlation versus causation, experimentation and A B testing, and how content metrics map to business objectives and optimization strategies.
Algorithm Updates and Adaptability
This topic assesses a candidate's process for staying current with algorithm and platform changes that affect how content, campaigns, or products are discovered, ranked, or distributed (for example: search engine ranking updates, social platform feed algorithm changes, ad-auction algorithm shifts, or recommendation-system changes), and for adapting strategy and execution accordingly. Interviewers evaluate how the candidate monitors official announcements and behavioral/performance signals, synthesizes learnings from updates to inform channel and content strategy, and measures the impact of changes through data analysis and experiments. Candidates should be prepared to give concrete examples of responding to a major platform or algorithm update, adjusting strategy based on emerging trend signals (such as the rise of AI-generated content or new discovery experiences), the tools and sources they use for ongoing monitoring, and how they prioritize and communicate required changes to stakeholders and implementation teams.