Learners will systematically plan, execute, and analyze A/B tests for email campaigns to make data-driven decisions that improve campaign performance.

Historical performance trends rarely tell the full story without accounting for seasonality, promotions, and external events that influence campaign performance. This module focuses on refining forecasting models by identifying recurring seasonal patterns, applying seasonal adjustments, and incorporating promotional impacts into budget planning workflows. Learners examine how to distinguish meaningful trends from random fluctuations and improve forecast accuracy during high-variance periods such as holiday campaigns or promotional peaks. The module emphasizes practical decision-making and forecast refinement techniques used in real marketing planning environments. By the end of this module, you will be able to adjust forecasting models for seasonality and produce more reliable campaign budget projections.

Forecasting is a critical marketing planning skill because campaign budgets and performance targets often need to be defined before complete data is available. This module introduces practical forecasting methods used to project ad spend, lead volume, and conversion outcomes from historical campaign data. Learners explore core forecasting concepts, including linear regression, moving averages, and data preparation, while comparing spreadsheet-based and AI-assisted forecasting workflows. The module emphasizes evaluating assumptions, interpreting outputs, and selecting the right forecasting approach for different planning scenarios. By the end of this module, you will be able to construct a forecast model and project next quarter's spend and conversion volumes.

Learners will master the systematic process of using Google Analytics model comparison tools to analyze user behavior patterns and align marketing campaigns with specific market demand phases for optimal targeting and performance.

Learners will master comprehensive attribution frameworks to evaluate true campaign effectiveness across multiple channels, quantify cross-channel impact beyond simple last-click analysis, and develop data-driven budget reallocation strategies.

Standard analytics reports often provide surface-level metrics but fail to explain how users actually move through a digital experience. In this module, you will use GA4 Explorations to uncover deeper behavioral insights through funnels, pathing analysis, and free-form reporting. You will learn how to analyze users, sessions, events, and traffic sources while identifying where conversion journeys break down. Through guided demonstrations and hands-on practice in the GA4 Demo Account, you will apply practical analysis workflows used by marketing teams to improve campaign and website performance. By the end of this module, you will be able to build GA4 explorations and identify actionable funnel and pathing insights from behavioral data.

Reliable analytics depends on accurate event tracking and consistent data instrumentation across the customer journey. In this module, you will audit GA4 event implementations, identify tagging gaps, and evaluate whether collected data supports meaningful reporting and attribution analysis. You will examine event structures, parameter naming conventions, Enhanced Measurement configurations, and real-time validation workflows using DebugView and browser inspection tools. The module emphasizes practical troubleshooting and communication skills needed to create actionable tagging recommendations for technical teams. By the end of this module, you will be able to analyze GA4 instrumentation issues, identify critical tracking gaps, and recommend improvements that strengthen reporting accuracy and marketing decision-making.

When growth plateaus, the instinct is to run more campaigns — but without a clear diagnostic of where the funnel is breaking down and what is actually worth testing, additional spend compounds the problem rather than solving it. This project module places learners in the role of Senior Performance Analyst at ScaleUp SaaS, tasked with building the data-backed case for Q4 investment before leadership commits the budget. The work spans four connected analytical disciplines: deriving a growth multiplier from historical data to establish what the business can expect if nothing changes, using GA4 path exploration to identify and prioritize the single highest-impact conversion bottleneck, designing a structured A/B test with a specific, behavior-grounded hypothesis to address it, and translating all three into a seasonally adjusted budget proposal and a set of concrete tracking fixes that will make the results measurable. Each section sharpens the next, so the final document reads as one coherent argument rather than four separate analyses. By the end of this module, you will be able to build a forecast from historical trend data, diagnose a conversion funnel bottleneck with supporting evidence, design a testable A/B experiment hypothesis, and produce a growth plan with resource and instrumentation recommendations a VP-level stakeholder can act on.

Technical skill without visible evidence is invisible to a hiring manager. This module focuses on closing the gap between what you have built and how the market sees it — translating program projects, SQL queries, and dashboards into a professional brand that passes both algorithmic and human review. Learners build an analytics-focused portfolio using the business impact formula, craft ATS-optimized resume language that connects technical methods to measurable outcomes, and apply AI-era framing that signals responsible, strategic tool use. The emphasis throughout is on the distinction between listing capabilities and demonstrating them: a dashboard with annotated findings, a hypothesis-first experiment write-up, and a power verb paired with a business outcome tell a fundamentally different story than a skills list. By the end of this module, you will be able to frame your analytical work as impact-first portfolio evidence, write resume bullets that pass ATS screening and hold a hiring manager's attention, and present a public-facing Data Studio dashboard that communicates analytical judgment within seconds of being opened.

Getting to the final round of a marketing analytics interview requires three distinct capabilities — and most candidates prepare for only one or two of them. This module breaks down the full CB2 interview structure: the technical screen that tests whether you can work with data, the case study that tests whether you can think strategically under ambiguity, and the behavioral stage where technically strong candidates most often lose ground. Using a four-step framework for structuring case study responses and a realistic role-play with a Director of Analytics persona, learners practice diagnosing a rising CPA problem, connecting attribution logic to their findings, and responding credibly when the conversation turns to AI tools in their workflow — an increasingly common final-round question that most candidates are not prepared for. By the end of this module, you will be able to structure any marketing analytics case study using a repeatable diagnostic framework, explain complex analytical concepts to non-technical stakeholders using business-first framing, and demonstrate the kind of pressure-stable reasoning that distinguishes a CB2-level hire.