Coursera | Customer Analytics

Predictive Analytics Quiz

Predictive Analytics Quiz >> Customer Analytics

1. Please note: Some of the questions you’ll find in this quiz aren’t just straight “regurgitation” of the materials presented in the videos – and this is exactly what I had in mind. These questions will force you to think a bit about the concepts/methods covered, but careful consideration of the course material should lead you to an indisputable correct answer in every case.Here’s one hint: make sure you look at the presentation decks as well as the videos.
In which of these situations would it be more appropriate to use a probability model rather than a regression/data-mining approach?

  • Predicting whether the customer will buy the brand at least once in the next year
  • Predicting the brand that the customer will buy during her next category purchase
  • Predicting when the customer will make her next purchase✅
  • Predicting which customer is most likely to churn in the next year
  • Predicting whether the customer will churn in the next year

2. Please note: Some of the questions you’ll find in this quiz aren’t just straight “regurgitation” of the materials presented in the videos – and this is exactly what I had in mind. These questions will force you to think a bit about the concepts/methods covered, but careful consideration of the course material should lead you to an indisputable correct answer in every case.Here’s one hint: make sure you look at the presentation decks as well as the videos.
Which of the following are genuine data-mining procedures? (Please check all that apply)

  • All answers are correct
  • SCAN
  • MARS✅
  • CART✅

3. Please note: Some of the questions you’ll find in this quiz aren’t just straight “regurgitation” of the materials presented in the videos – and this is exactly what I had in mind. These questions will force you to think a bit about the concepts/methods covered, but careful consideration of the course material should lead you to an indisputable correct answer in every case.Here’s one hint: make sure you look at the presentation decks as well as the videos.
Which of these statements is most aligned with our assumption(s) about randomness when it comes to modeling/explaining customer behavior?

  • Most customers are predictable but there is usually a segment of “as if” random ones that should be accounted for
  • Any given customer is quite predictable, but the randomness exists across customers
  • We make some assumptions about randomness in order to derive the mathematical model, but when it comes to actually estimating the model they no longer apply
  • Customers are not truly random but appear to be “as if” random from an outsider observer’s perspective✅
  • Each customer is assumed to behave randomly in accordance with a standard normal (“bell-shaped”) distribution

4. Please note: Some of the questions you’ll find in this quiz aren’t just straight “regurgitation” of the materials presented in the videos – and this is exactly what I had in mind. These questions will force you to think a bit about the concepts/methods covered, but careful consideration of the course material should lead you to an indisputable correct answer in every case.Here’s one hint: make sure you look at the presentation decks as well as the videos.
Among the explanations below, which one is a reason to favor a probability model over a regression-like (e.g., data-mining) model for long-run projections of customer behavior?

  • Probability models are more accurate than regression models
  • Probability models can determine customer motivations
  • If the observed behavior is viewed in an “as if” random manner, it would be wrong to put it into a regression-like model as if it’s deterministically true
  • Regression-like models are fine for a one-period-ahead prediction, but not beyond that horizon✅

5. Please note: Some of the questions you’ll find in this quiz aren’t just straight “regurgitation” of the materials presented in the videos – and this is exactly what I had in mind. These questions will force you to think a bit about the concepts/methods covered, but careful consideration of the course material should lead you to an indisputable correct answer in every case.Here’s one hint: make sure you look at the presentation decks as well as the videos.
Why does the “RFM” rubric present the three key measures (recency, frequency, and monetary value) in that order?

  • This is the order in which they were discovered/identified as being highly predictive of future behavior
  • Recency is the easiest of the three to observe/measure
  • Recency is the most predictive of the three✅
  • Recency and frequency are equally important, and monetary value is far less important than both of them
  • There is no particular reason; it’s just an arbitrary order

6. Please note: Some of the questions you’ll find in this quiz aren’t just straight “regurgitation” of the materials presented in the videos – and this is exactly what I had in mind. These questions will force you to think a bit about the concepts/methods covered, but careful consideration of the course material should lead you to an indisputable correct answer in every case.Here’s one hint: make sure you look at the presentation decks as well as the videos.
When we refer to a “cohort,” we are talking about a group of customers who:

  • Share similar acquisition characteristics (e.g., time of acquisition)✅
  • Share similar purchasing propensities
  • Share similar observable personal characteristics (e.g., demographics)
  • Share similar responsiveness to marketing tactics
  • Share similar churn propensities

7. Please note: Some of the questions you’ll find in this quiz aren’t just straight “regurgitation” of the materials presented in the videos – and this is exactly what I had in mind. These questions will force you to think a bit about the concepts/methods covered, but careful consideration of the course material should lead you to an indisputable correct answer in every case.Here’s one hint: make sure you look at the presentation decks as well as the videos.
Referring back to the dataset (and model) we covered extensively, how would these two customers (both “acquired” in 1995) compare to each other, in terms of their expected future purchasing?
1995 1996 1997 1998 1999 2000 2001 2002Vrinda 1 0 0 0 1 1 1 0Yoshinori 1 1 1 1 0 0 1 0

  • Vrinda would likely be more valuable
  • There’s not enough information here to make the decision
  • They would be expected to be roughly equal
  • Yoshinori would likely be more valuable✅

8. Please note: Some of the questions you’ll find in this quiz aren’t just straight “regurgitation” of the materials presented in the videos – and this is exactly what I had in mind. These questions will force you to think a bit about the concepts/methods covered, but careful consideration of the course material should lead you to an indisputable correct answer in every case.Here’s one hint: make sure you look at the presentation decks as well as the videos.
What does a “BTYD” model refer to?

  • Buy Till You Die
  • Bayesian Transformation of Yearly Data✅
  • Beta Time-Yield Distribution
  • Back-Test Your Data

9. Please note: Some of the questions you’ll find in this quiz aren’t just straight “regurgitation” of the materials presented in the videos – and this is exactly what I had in mind. These questions will force you to think a bit about the concepts/methods covered, but careful consideration of the course material should lead you to an indisputable correct answer in every case.Here’s one hint: make sure you look at the presentation decks as well as the videos.
Referring back to the dataset (and model) we covered extensively, how would these two customers (both “acquired” in 1995) compare to each other, in terms of their expected future purchasing?
1995 1996 1997 1998 1999 2000 2001 2002Ted 1 0 0 0 1 0 1 0Jane 1 1 1 1 0 0 0 0

  • Jane would likely be more valuable✅
  • Ted would likely be more valuable
  • There’s not enough information here to make the decision
  • They would be expected to be roughly equal

10. Please note: Some of the questions you’ll find in this quiz aren’t just straight “regurgitation” of the materials presented in the videos – and this is exactly what I had in mind. These questions will force you to think a bit about the concepts/methods covered, but careful consideration of the course material should lead you to an indisputable correct answer in every case.Here’s one hint: make sure you look at the presentation decks as well as the videos.
Which of these real actions would not be represented by the “buy” in the BTYD model?

  • When a customer files an insurance claim
  • When a customer renews a subscription
  • When a customer participates in a promotional sale
  • When a customer attends a sales event
  • All answers are possibilities✅

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