Customer Analytics Coursera Quiz Answers – Networking Funda

All Weeks Customer Analytics Coursera Quiz Answers

Data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts to customer loyalty programs and user-generated ratings/reviews, there is a staggering amount of data that can be used to describe our past buying behaviors, predict future ones, and prescribe new ways to influence future purchasing decisions.

In this course, four of Wharton’s top marketing professors will provide an overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few.

This course provides an overview of the field of analytics so that you can make informed business decisions. It is an introduction to the theory of customer analytics and is not intended to prepare learners to perform customer analytics.

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Customer Analytics Week 01 Quiz Answers

Practice Quiz : Descriptive Analytics Practice Quiz

Q!. What is a possible drawback of active data collection that should be avoided?

  • A drop in NPS (Net Promoter Score)
  • Low return on investment
  • Survey fatigue
  • All of these are correct

Q2. Causal research methods should be used when you have which type of problem?

  • A clear problem and research hypothesis
  • A clear problem without a hypothesis
  • An ambiguous problem

Q3. Which managerial question cannot be answered using only scanner data?

  • Which types of displays work better?
  • Which advertisement caused sales to increase or decrease
  • Will cherry pickers become loyal?
  • Who buys our products on promotions?

Q4. What question is used to find the NPS?

  • How likely is it that you would recommend [your company] to a friend or colleague?
  • How satisfied are you with this product?
  • How likely are you to buy this product again?

Q5. What does exploratory research help define?

  • The characteristics of relevant groups
  • Causal relationships
  • Areas to research in depth

Quiz : Descriptive Analytics Quiz

Q1. What is descriptive analytics?

  • All answers are correct
  • Information needed for actionable decisions
  • Links the market to the firm through information
  • Systematic collection and interpretation of data

Q2. When is exploratory analysis best utilized?

  • When the managerial problem is descriptive in nature
  • When the managerial problem is ambiguous
  • When the managerial problem is causal in nature

Q3. What is one problem with Marketing Research Online Communities?

  • Shorter deadlines are not possible
  • It does not enhance enagagement with customers
  • Return on Investment is quite uncertain

Q4. When are mobile surveys best?

  • For retrospective feedback
  • For context-specific, at the moment surveys
  • For generic context-free surveys

Q5. Why do people pay so much for Point of Sales (POS) data?

  • Accuracy of sales information
  • Completeness of the data
  • Timeliness of marketing activity
  • All answers are correct

Q6. What are some caveats with POS data?

  • Don’t have information on brand-level promotions
  • Cannot make causal claims
  • Don’t know customers purchases

Q7. What is sentiment analysis?

  • It is frequently used in social media data to quantify the valence of information
  • It finds errors in any customer-level data
  • It quantifies the amount of promotional activity in POS data

Q8. What is required for making the following causal statement: X causes Y?

  • Correlation between X and Y
  • All answers are correct
  • No third factor affecting both X and Y
  • Temporal antecedence of X

Q9. What is an example of active data collection?

  • Surveys
  • Mobile data
  • TV viewing data
  • Internet surfing data

Q10. In Net Promotor Score surveys, how much do “promoters” score on a scale of 0-10.

  • 9,10
  • 7,8
  • 0-6

Customer Analytics Week 02 Quiz Answers

Practice Quiz : Predictive Analytics Practice Quiz

Q1. In a simple regression formula, does R(2) represent?

  • The Randomization Ratio
  • The effect of X on Y
  • The strength of the regression analysis
  • The quality of the model’s out-of-sample forecast

Q2. If you have a trustworthy regression line, R(2) should be at or above what percentage?

  • .55
  • 1.0
  • 0
  • 0.7

Q3. What is optimal pricing?

  • The price that will maximize profits
  • The price that will retain the most customers
  • The price that creates the most satisfied customers
  • The price that yields the highest R2

Q4. What can regression analysis be used for?

  • Determining how dependent variables affect independent variables
  • Determining how independent variables affect a dependent variable
  • Choosing the right dependent variables to explain the independent variables
  • None of the above

Q5. Given only the four choices in the table below, what is the optimal price for maximizing profit (assume cost is zero)?

  • 2.75
  • 1.5
  • 5.75
  • 6.75

Q6. Why are KPIs important?

  • KPIs can indicate brand loyalty
  • KPIs can predict customer behavior beyond period 2
  • KPIs can help determine optimal price
  • All of the above

Q7. What does RFM stand for?

  • Reliability, Favorability, Monetary Value
  • Recency, Frequency, Monetary Value
  • Recency, Favorability, Marketability
  • Reliability, Favorability, Marketability

Q8. What limits the use of regression analysis?

  • It can only be used to determine the behavior of repeat customers
  • It can only predict profits
  • It is not very accurate
  • It can only be used to predict behavior over a period or two ahead

Q9. In the data set discussed in the video “Making Predictions Using Data Sets” why are the “Sarahs” (i.e., customers who have only made one purchase) so important?

  • There are many Sarahs in most datasets
  • They represent future potential sales
  • They can be used to determine actions that cause unhappy customers
  • They indicate the level of randomness of the data set

Q10. Why is it important to predict customer behavior far into the future?

  • To ensure that you are setting the optimal price
  • To determine customer lifetime value
  • To predict market variability

Main Quiz : Predictive Analytics Quiz

Q1. In which of these situations would it be more appropriate to use a probability model rather than a regression/data-mining approach?

  • Predicting when the customer will make her next purchase
  • Predicting whether the customer will churn in the next year
  • Predicting which customer is most likely to churn in the next year
  • 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

Q2. Which of the following are genuine data-mining procedures? (Please check all that apply)

  • CART
  • SCAN
  • All answers are correct
  • MARS

Q3. Which of these statements is most aligned with our assumption(s) about randomness when it comes to modeling/explaining customer behavior?

  • 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
  • Any given customer is quite predictable, but the randomness exists across customers
  • Most customers are predictable but there is usually a segment of “as if” random ones that should be accounted for
  • 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

Q4. 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 can determine customer motivations
  • Probability models are more accurate than regression models
  • Regression-like models are fine for a one-period-ahead prediction, but not beyond that horizon
  • 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

Q5. 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

Q6. 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

Q7. 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?

19951996199719981999200020012002
Vrinda10001110
Yoshinori11110010
  • They would be expected to be roughly equal
  • There’s not enough information here to make the decision
  • Yoshinori would likely be more valuable
  • Vrinda would likely be more valuable

Q8. What does a “BTYD” model refer to?

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

Q9. 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?

19951996199719981999200020012002
Ted10001010
Jane11110000
  • Jane would likely be more valuable
  • They would be expected to be roughly equal
  • There’s not enough information here to make the decision
  • Ted would likely be more valuable

Q10. 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 attends a sales event
  • When a customer renews a subscription
  • All answers are possibilities
  • When a customer participates in a promotional sale

Customer Analytics Week 03 Quiz Answers

Prescriptive Analytics Quiz

Q1. What is the goal of prescriptive analytics?

  • Make a recommendation on an action that will optimize a goal
  • Optimize a function
  • Develop a model to describe the data
  • Explain the relationship between actions and outcomes

Q2. When would descriptive and predictive results need additional analysis?

  • When there are strategic consumers involved
  • All answers are correct
  • When there is competition involved
  • When there are multiple explanations to the same data we observe
  • When the firm can make a choice of different actions to take

Q3. Which one of the following is an example of a goal/objective?

  • The quantity of a product sold
  • The price of a product
  • The color of a product
  • The shape of a product

Q4. What is an action? (Please check all that apply)

  • A choice that impacts the goal
  • A part of the model under the direct control of the firm
  • The price of a product

Q5. What does a model do?

  • Explain the relationship between actions and parameters to the goal
  • Finds a goal
  • Shows in a graph how price changes with quantity
  • Tells us how to make the maximum profit

Q6. When does maximizing revenue also maximize profit? (Please check all that apply)

  • When there is no cost to the product
  • When the marginal revenue equals marginal cost
  • When the marginal cost is zero

Q7. Why does it matter to know how a demand curve was generated?

  • Knowing the truth always helps
  • We may give a different recommendation for different models
  • It helps find errors in the data
  • Correlation does not imply causation

Q8. Which one of these is an example of a tradeoff?

  • Discounts on a product brings more buyers now and makes buyers wait for discounts in the future
  • Increasing the quality of a product increases it sales
  • Decreasing the cost of production increases profit
  • Showing more ads to consumers makes them buy more products

Q9. Why is it important to consider strategic interaction?

  • Strategic interaction can affect the validity of your model
  • All of the above
  • You need to ensure you are asking the right question
  • It can affect the recommendations you make

Q10. What does online retargeting do?

  • Show lots of ads to people
  • Targets consumers better with ads
  • Tries to remind people to buy more
  • Shows ads to people after they visited a specific website

Customer Analytics Week 04 Quiz Answers

Quiz : Applications

Q1. Which type of data provides the most granular level of information about a given individual’s customer behavior?

  • store-level data of the stores that they frequent
  • market-level sales from where they live
  • aggregate tracking data for the websites that the person frequently visits
  • household-level scanner data from their home

Q2. Which of the following is the biggest challenge to solving the “advertising attribution problem”?

  • There is not enough digital advertising so that the data is sparse.
  • There is not significant industry interest, hence no funding available.
  • Most websites don’t keep a record of customer visits.
  • Tracking customers across digital properties is difficult.

Q3. When setting optimal prices, which of the following is a concern when utilizing a regression of observed sales on observed prices to set them?

  • All of these answers apply.
  • Future prices might be outside the range of past prices.
  • Past observed prices are not randomly set.
  • There is not enough variation in observed prices.

Q4. Which of the following is not a method used to track customers across webpages?

  • IP address tracking
  • Cookie insertion
  • Registered user login
  • Pop-up advertising

Q5. Which of the following are threats to Amazons’s use of advanced predictive shipping?

  • A lack of local distribution centers
  • None of the answers are correct
  • Lack of data at the individual customer level
  • An inability to do prediction at the individual customer level

Q6. What technology is Comcast using to improve customer satisfaction?

  • GPS Tracking
  • Social Network Scraping
  • Software that reads customer intonation
  • Eye-tracking data

Q7. Which of the following customers would have a higher expected customer lifetime value?

  • A customer who spends $150 per year, but has a 20% churn propensity per year.
  • A customer who spends $200 per year, but has a 30% churn propensity per year.
  • A customer who spends $100 per year, but has a 10% churn propensity per year.
  • A customer who spends $250 per year but has a 40% churn propensity per year.

Q8. Which of the following statements are correct?

  • The most valuable customers to a firm in the future are those that currently spend the most.
  • None of these statements is always true.
  • The most valuable customers to a firm in the future are those with the highest referral value.
  • The most valuable customers to a firm in the future are those with the lowest propensity to churn in the future.

Q9. When targeting customers for optimal marketing, which of the following rank ordering of customers from highest to lowest is most appropriate to determine which customers to target?

  • Highest to lowest time with the firm
  • Highest to lowest current period spend
  • Highest to lowest CLV
  • Highest to lowest marketing effectiveness

Q10. Which of the following is GPS-based tracking likely to enable firms to do?

  • All of these are correct
  • Provide targeted advertisements at optimal times
  • Raise CLV
  • Lower churn rates
Customer Analytics Coursera Course Review:

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Customer Analytics course is available on Coursera for free, if you are stuck anywhere between quiz or graded assessment quiz, just visit Networking Funda to get Customer Analytics Coursera Quiz Answers.

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