Mixed Deck — All MAC Topics Flashcards

In A/B testing, what is a 'false positive' (Type I error)?

Answer: Concluding a variant is better when the difference is actually due to chance

A false positive (Type I error) occurs when you incorrectly conclude that a variant outperforms the control when the observed difference is actually random.

A cross-functional marketing analytics project is delayed because the data engineering team and marketing team have conflicting priorities. As project lead, what is the BEST approach?

Answer: Facilitate a joint prioritization session with both teams to align on shared goals

A joint prioritization session surfaces each team's constraints and fosters collaborative solutions without requiring escalation.

Which type of attribution model typically uses machine learning algorithms to assign credit based on actual conversion data?

Answer: Data-Driven attribution

Data-Driven attribution uses machine learning to analyze conversion paths and assign credit based on the actual impact of each touchpoint.

Which technique is used to reduce dimensionality by transforming features into uncorrelated principal components?

Answer: Principal Component Analysis (PCA)

PCA projects data onto orthogonal principal components that capture the most variance, reducing the number of features while preserving information.

A marketing analytics team is consistently missing sprint deadlines. After reviewing workloads, you find tasks are unevenly distributed. What should you do?

Answer: Rebalance task assignments based on individual capacity and skill sets

Redistributing tasks based on capacity and skill ensures equitable workloads and sustainable sprint velocity.

In marketing analytics projects, what does a Work Breakdown Structure (WBS) primarily help a project manager accomplish?

Answer: Decompose project scope into manageable deliverables

A WBS breaks the total project scope into smaller, manageable components called work packages, making planning and control easier.

Which of the following best describes the concept of 'algorithmic attribution'?

Answer: A model that uses statistical analysis and machine learning to dynamically assign credit based on observed data

Algorithmic (data-driven) attribution uses machine learning and statistical methods to assess the true incremental contribution of each touchpoint.

Which technique segments customers based on their most recent purchase, purchase frequency, and total spend?

Answer: RFM analysis

RFM (Recency, Frequency, Monetary) analysis segments customers using three behavioral dimensions to identify high-value segments.

A stakeholder disagrees with your analytics conclusion. What is the best first response?

Answer: Ask the stakeholder to clarify what specific data they feel is missing or incorrect

Understanding the specific concern allows you to address it with data rather than creating conflict or abandoning valid analysis.

How does big data affect data collection and management in marketing?

Answer: It increases the volume and complexity of data

Big data refers to extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior. Its emergence has significantly impacted data collection and management in marketing by introducing unprecedented volumes, velocities, and varieties of data. This necessitates more sophisticated tools and strategies for storage, processing, and analysis, making data management more complex.

A marketing team uses RFM analysis. What does 'F' stand for?

Answer: Frequency

In RFM analysis, F stands for Frequency — how often a customer makes a purchase within a given time period.

In a customer churn prediction model, a high false negative rate means the model is:

Answer: Missing many customers who will actually churn

A high false negative rate means the model fails to predict churn for many customers who will actually leave, leading to missed retention opportunities.

Which of the following is the most effective way to structure a marketing analytics insight for a stakeholder email?

Answer: Situation → Complication → Resolution (SCR framework)

The SCR (Situation-Complication-Resolution) framework creates a narrative arc that quickly orients the reader and delivers a clear recommendation.

In gradient boosting, how are subsequent models trained?

Answer: On the residual errors of previous models

Gradient boosting trains each new model to correct the residual errors (pseudo-residuals) left by the combined ensemble of previous models.

An analyst presents a bar chart where the y-axis starts at 80 instead of 0. What concern does this raise?

Answer: It may visually exaggerate differences between groups and mislead decision makers

Truncating the y-axis amplifies visual differences that are proportionally small, which can mislead decision makers.

Which technique is commonly used to gather customer insights in marketing?

Answer: Surveys, focus groups, and feedback

Gathering customer insights involves understanding customer motivations, preferences, and behaviors through various methods. Surveys provide quantitative data and broad opinions, focus groups offer qualitative, in-depth discussions and immediate feedback, and general feedback mechanisms (like online reviews or customer service interactions) capture ongoing sentiment. Together, these techniques provide a rich and nuanced understanding of the customer base, informing strategic decisions.

Which approach best identifies the specific features that drive customer purchase decisions by asking customers to choose between product profiles?

Answer: Conjoint analysis

Conjoint analysis presents respondents with product profiles and uses their trade-off choices to calculate the relative importance of each feature.

Which scenario represents a data quality risk caused by schema drift in a marketing data pipeline?

Answer: An upstream CRM adds a new field format, breaking downstream joins and causing null values in reports

Schema drift occurs when upstream data structure changes unexpectedly, breaking downstream transformations and introducing silent data errors.

In the context of marketing analytics, what does 'incrementality' measure?

Answer: The additional conversions caused specifically by a marketing activity beyond what would have occurred organically

Incrementality isolates the lift attributable to a specific marketing action, separating it from organic or baseline conversions.

When documenting A/B test results for a stakeholder report, which element is most important to include alongside the winning variant?

Answer: The hypothesis tested, the confidence level, sample size, and the measured lift

A complete test report should document the hypothesis, confidence level, sample size, and lift so stakeholders can evaluate the reliability of the result and apply the learning to future decisions.