Practice Test Geeks(CCRM) Certified Client Relationship Manager Practice Test
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Data Analytics Flashcards

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

Read the first 7 Data Analytics flashcards as text

  1. Which technique is used to reduce the number of variables in a client dataset while retaining the most important information?

    Answer: Principal Component Analysis (PCA)

    PCA is a dimensionality reduction technique that transforms correlated variables into a smaller set of uncorrelated principal components.

  2. A CCRM professional wants to evaluate the effectiveness of a new onboarding program. Which analysis framework is most appropriate?

    Answer: Funnel analysis comparing pre- and post-program conversion rates

    Funnel analysis tracks clients through sequential stages, making it ideal for measuring onboarding program effectiveness at each step.

  3. In client data analytics, 'data wrangling' refers to:

    Answer: The process of cleaning, transforming, and organizing raw data for analysis

    Data wrangling encompasses the preprocessing steps needed to convert raw, messy data into a structured, analysis-ready format.

  4. A cohort analysis of clients acquired in Q1 shows lower 12-month retention than those acquired in Q2. What is the MOST actionable next step?

    Answer: Investigate differences in acquisition channel, onboarding experience, and early engagement for each cohort

    Diagnosing why cohorts differ requires examining the variables that distinguish them, such as channel, onboarding, and early behavior.

  5. Which key performance indicator (KPI) directly measures how willing existing clients are to recommend the firm to others?

    Answer: Net Promoter Score (NPS)

    NPS measures the likelihood that clients will recommend the firm, classifying them as Promoters, Passives, or Detractors.

  6. When building a client risk model, overfitting occurs when:

    Answer: The model performs well on training data but poorly on new client data

    Overfitting means the model has memorized training data noise rather than learning generalizable patterns, causing poor performance on unseen data.

  7. A relationship manager uses a decision tree model to classify clients as 'at-risk' or 'stable.' The model has 90% accuracy on training data but 62% on test data. What should the analyst do?

    Answer: Apply regularization or pruning techniques to reduce overfitting

    The large accuracy gap between training and test sets is a classic sign of overfitting, which can be addressed through pruning or regularization.