CAM Data Analysis & Decision Making 3 — Questions and Answers
Question 1: A CAM wants to forecast next quarter's expansion revenue using historical data. Which approach provides the most statistically rigorous forecast?
- Gut feeling based on recent calls
- Linear regression on historical expansion trends (Correct answer)
- Copying last year's number with a 10% increase
- Averaging the highest and lowest past quarters
Correct answer: Linear regression on historical expansion trends
Linear regression identifies the relationship between time and revenue trends, producing a statistically grounded forecast based on historical patterns.
Question 2: When a CAM's monthly business review data shows conflicting signals — rising usage but declining satisfaction scores — the recommended approach is to:
- Report only the positive usage data to the customer
- Triangulate both data points to identify the root cause (Correct answer)
- Discard the satisfaction data as unreliable
- Wait for the conflict to resolve itself
Correct answer: Triangulate both data points to identify the root cause
Triangulating multiple data points reveals whether the conflict reflects a temporary lag, a feature-fit issue, or an unmet expectation that needs addressing.
Question 3: A CAM uses a weighted scoring model to prioritize renewal risk. An account scored 85/100 in product adoption but 20/100 in executive sponsor engagement. The overall risk assessment should:
- Default to the higher score as the account is low risk
- Weight engagement scores heavily since sponsor loss is a top churn driver (Correct answer)
- Average all scores equally regardless of churn impact
- Ignore engagement scores until renewal is 30 days away
Correct answer: Weight engagement scores heavily since sponsor loss is a top churn driver
Weighting factors by their impact on churn ensures the risk score reflects real-world outcomes; executive sponsor loss is consistently a top churn predictor.
Question 4: Which metric BEST indicates a customer's likelihood to expand their contract in the next quarter?
- Number of support tickets submitted
- Product adoption rate above 80% with growing user count (Correct answer)
- Length of time since last QBR
- Number of contacts in the CRM
Correct answer: Product adoption rate above 80% with growing user count
High product adoption combined with a growing user base signals the customer is realizing value and has capacity to expand usage or seats.
Question 5: A CAM is asked to explain why customer lifetime value (CLV) dropped 15% year-over-year. Which analysis would be MOST revealing?
- A headcount report of the CAM team
- Cohort analysis comparing retention and expansion rates by acquisition year (Correct answer)
- A pie chart of revenue by product line
- A list of all accounts with zero support tickets
Correct answer: Cohort analysis comparing retention and expansion rates by acquisition year
Cohort analysis isolates how different groups of customers behave over time, revealing whether new cohorts retain and expand at lower rates than older ones.
Question 6: A CAM observes that accounts using three or more product features have a 90% renewal rate, while single-feature accounts renew at 55%. This data should inform:
- Reducing the product to a single feature
- A proactive multi-feature adoption strategy during onboarding (Correct answer)
- Charging more for accounts using fewer features
- Waiting for customers to discover features on their own
Correct answer: A proactive multi-feature adoption strategy during onboarding
Data showing a strong correlation between multi-feature adoption and renewal should drive a proactive strategy to broaden feature usage early in the customer journey.
Question 7: When presenting account data to an executive sponsor, a CAM should primarily use:
- Detailed raw data tables with all metrics
- A concise summary of 3–5 key metrics tied to business outcomes (Correct answer)
- Technical product usage logs
- Internal team performance dashboards
Correct answer: A concise summary of 3–5 key metrics tied to business outcomes
Executives need concise, outcome-focused insights rather than operational detail; limiting to 3–5 key metrics tied to their goals drives clearer decisions.
CAM Data Analysis & Decision Making 3
A CAM wants to forecast next quarter's expansion revenue using historical data.
Which approach provides the most statistically rigorous forecast?