Module 3 Quiz :Digital Marketing Analytics in Practice (Digital Marketing Specialization) Answers 2025

Question 1

Which is NOT a potential benefit of a loyalty loop?

❌ Improved product quality
❌ Improved brand awareness
❌ Improved brand perception
✅ None of the above are potential benefits

Explanation:
A strong loyalty loop can improve product quality (via feedback), brand perception, and even awareness through advocacy. All listed items are benefits.

Question 2

Which of the following is true of qualitative data?

❌ It can never be operationalized into quantitative data
❌ It cannot be expressed in figures
❌ No statistical inferences can be made
✅ None of the above

Explanation:
Qualitative data can be coded into quantitative form, summarized numerically, and analyzed statistically when appropriately designed.

Question 3

Which of the following characterizes a well-functioning loyalty loop?

❌ Customers become employees
❌ Loyalty to an ineffective manager worsens decisions
❌ Suppliers offer bulk discounts
✅ Customers advocate my product

Explanation:
A key sign of a healthy loyalty loop is customer advocacy, where loyal customers promote the brand.

Question 4

Over the last 30 years, the amount of available consumer data has…

❌ Stayed the same
❌ Increased, then sharply decreased
❌ Decreased
✅ Increased

Explanation:
Digital platforms, devices, and tracking have caused an explosive growth in consumer data.

Question 5

R is used primarily for biologists and never for statistics or regressions.

❌ True
✅ False

Explanation:
R is widely used for statistics, regressions, data analysis, and visualization across many fields.

Question 6

The visual appeal of a figure is unimportant as long as it is accurate.

❌ True
✅ False

Explanation:
Poor visual design can obscure insights or mislead, even if the data is accurate.

Question 7

X is positively correlated with Y. From this we can determine that…

❌ X causes Y
❌ Y causes X
❌ X and Y are the same thing
✅ None of the above

Explanation:
Correlation does not imply causation. No causal conclusion can be drawn from correlation alone.

Question 8

Which is sometimes appropriate when refining data for presentation?

❌ Excluding categories you find unimportant
❌ Removing troublesome data points for convenience
❌ Fabricating data to strengthen conclusions
✅ None of the above

Explanation:
Data refinement must be methodologically justified. Arbitrary exclusion or fabrication is unethical.

Question 9

If Bellabeat advertised online and on TV and saw increased sales…

✅ DMA can help identify which channel delivered better ROI
❌ They should discontinue one channel immediately
❌ They should add billboards expecting similar results
❌ They should invest equally in both going forward

Explanation:
DMA (Digital Marketing Analytics) helps attribute performance and optimize spend across channels.

Question 10

Fill in the blanks: Omitting data is ____, while altering data is ____

✅ Sometimes justified; never justified
❌ Never justified; sometimes justified
❌ Never justified; also never justified
❌ Sometimes justified; also sometimes justified

Explanation:
Omitting data can be justified with transparency (e.g., outliers with rationale). Altering data is unethical.

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