Practice Test Geeks(GMC) Growth Marketing Certification Practice Test
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Data & Analytics Flashcards

7 cards from real GMC practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. What is the 'North Star Metric' concept in growth marketing?

    Answer: The single metric that best captures the core value a product delivers to customers

    The North Star Metric is one key metric that reflects the product's core value delivery and correlates strongly with long-term business growth.

  2. In the context of UTM parameters, what does 'utm_medium' represent?

    Answer: The marketing channel or medium (e.g., email, cpc)

    utm_medium identifies the marketing channel type, such as 'email', 'cpc', or 'social', helping categorize traffic sources in analytics.

  3. What is 'incrementality testing' in growth marketing?

    Answer: Measuring the true causal lift that a marketing activity adds beyond what would have happened organically

    Incrementality testing uses holdout groups to isolate and measure the true additional impact of a campaign versus organic baseline behavior.

  4. A product has a DAU/MAU ratio of 0.5. How should this be interpreted?

    Answer: 50% of monthly users engage with the product daily, indicating strong stickiness

    A DAU/MAU ratio of 0.5 means half of monthly active users also engage daily, which is considered strong product stickiness (Facebook historically targets ~0.5–0.6).

  5. Which approach best addresses the 'last-click attribution bias' problem in multi-channel marketing?

    Answer: Implementing data-driven attribution or multi-touch attribution models

    Multi-touch or data-driven attribution distributes credit across all touchpoints in the customer journey, correcting the bias of crediting only the final interaction.

  6. What is 'predictive analytics' used for in growth marketing?

    Answer: Using statistical models and machine learning to forecast future user behaviors and outcomes

    Predictive analytics applies machine learning and statistical modeling to historical data to forecast future behaviors like churn probability or purchase likelihood.

  7. A growth team tracks 'time to value' (TTV). What does reducing TTV achieve?

    Answer: Faster user activation and higher likelihood of long-term retention

    Reducing TTV means users reach the product's core value moment sooner, which strongly correlates with higher activation rates and long-term retention.