Performance Marketing Career Transition

Clean and standardize a raw campaign-performance table in Excel or Google Sheets.

Use relative and absolute cell references to calculate campaign KPIs.

Use IF and error-handling formulas to classify campaign performance against targets.

Use a lookup formula to combine campaign results with budget or target data.

Summarize channel and campaign performance with a pivot table.

Calculate daily budget pacing and forecast end-of-period spend.

Create a concise chart and written insight from campaign data.

Distinguish users, sessions, events, key events, and revenue in the GA4 data model.

Navigate a GA4 demo property to locate acquisition, landing-page, event, and conversion data.

Distinguish User Acquisition from Traffic Acquisition reports and select the appropriate report for a question.

Interpret source, medium, campaign, and default channel group dimensions in GA4.

Apply report filters and comparisons to isolate a campaign or audience segment.

Draft a GA4 event and parameter specification for a sample conversion funnel.

Diagnose common GA4 data-quality problems involving UTMs, referrals, duplicate events, and missing conversions.

Explain how Google Ads account structure, the ad auction, Ad Rank, and Quality Score affect delivery.

Conduct keyword research for a sample product using intent and relevance criteria.

Organize a keyword list into tightly themed ad groups.

Assign keyword match types and create an initial negative-keyword list.

Write a responsive search ad aligned with one ad group's keywords and landing-page offer.

Select a bidding strategy that matches a campaign's objective, data maturity, and constraints.

Set a defensible daily budget using traffic, CPC, and conversion assumptions.

Audit a mock Search campaign build for targeting, tracking, budget, bidding, and brand-safety errors.

Calculate and compare Google Ads performance by campaign, ad group, keyword, and device.

Use a search-terms report to identify negative keywords and expansion opportunities.

Diagnose weak delivery or efficiency using impression share, Quality Score components, and conversion metrics.

Apply decision rules to pause, retain, or adjust keywords and ads in a simulated campaign.

Design a valid Google Ads experiment with one variable, a hypothesis, and a success metric.

Evaluate a landing page for message match, conversion friction, and mobile usability.

Document an optimization decision with evidence, expected impact, and a follow-up date.

Explain how Meta's campaign, ad-set, and ad hierarchy interacts with its auction and delivery system.

Select a Meta campaign objective and optimization event for a stated business goal.

Define prospecting, retargeting, and exclusion audiences for a sample business.

Choose placements, budget type, schedule, and attribution settings for a simulated campaign.

Create a performance-focused creative brief specifying audience, hook, benefit, proof, format, and call to action.

Write primary text, a headline, and a call to action for one Meta ad concept.

Explain how the Meta Pixel, Conversions API, and prioritized events support campaign measurement.

Diagnose a sample Meta campaign using CPM, CTR, CPC, conversion rate, frequency, CPA, and ROAS.

Write a SELECT query to retrieve specified columns from a marketing table.

Filter campaign records with WHERE conditions and logical operators.

Aggregate spend, clicks, conversions, and revenue with SQL functions.

Group marketing KPIs by campaign, channel, or date.

Use CASE expressions to create channel groups and performance categories.

Join campaign-performance data to campaign metadata using a shared key.

Write a query that ranks campaigns by CPA or ROAS while handling zero-conversion cases.

Distinguish attribution from incrementality when evaluating marketing impact.

Calculate first-touch, last-touch, and linear attribution for a sample set of customer paths.

Explain why Google Ads, Meta Ads, and GA4 may report different conversion totals.

Identify measurement limitations caused by consent choices, cookie loss, cross-device behavior, and offline conversions.

Calculate customer acquisition cost, average order value, and contribution margin from business data.

Estimate a simple customer lifetime value and payback period from stated assumptions.

Recommend a budget reallocation using attributed results, profitability, uncertainty, and campaign constraints.

Translate a stakeholder's business question into dashboard KPIs, dimensions, filters, and date ranges.

Reshape a campaign dataset into a structure suitable for dashboard reporting.

Build scorecards, a time series, and a performance table in Looker Studio using sample data.

Create calculated fields for CTR, CPC, conversion rate, CPA, and ROAS in a dashboard.

Add date controls and channel or campaign filters to an interactive dashboard.

Apply visual hierarchy and formatting that make performance changes and exceptions easy to detect.

Write a weekly campaign commentary covering results, causes, actions, and risks.

Present a performance recommendation while separating evidence, assumptions, and uncertainty.

Allocate a sample budget across Google Ads and Meta Ads using funnel role, audience intent, and forecast assumptions.

Create a 30-day launch and optimization schedule for a simulated cross-channel campaign.

Analyze a post-launch dataset to identify the three highest-priority performance problems.

Produce an optimization plan linking each proposed action to evidence, expected impact, and measurement criteria.

Assemble a campaign brief, build sheets, analysis, dashboard, and recommendations into a coherent portfolio case study.

Write concise résumé bullet points that translate the completed case study into performance-marketing competencies.

Answer a performance-marketing case question by stating assumptions, calculating relevant metrics, and defending a recommendation.