Question 1

Within a neural network, nodes or neurons

Accepted Answer

A)  are values that represent features from the dataset. 
B)  determine the amount of adjustment made to the weights in the network. 
C)  determine the constant value given to the weighted input of each feature. 
D)  are a set of inputs that are multiplied by their weights. 
A)  are values that represent features from the dataset. 
B)  determine the amount of adjustment made to the weights in the network. 
C)  determine the constant value given to the weighted input of each feature. 
D)  are a set of inputs that are multiplied by their weights. D

Question 2

An advertising company uses a neural network software to determine which buyers are most likely to open their promotional emails based on past purchase behavior. Using data from 20 previous email campaigns, the neural network trains itself to examine the impact of 25 features and develop recommended solutions. The results almost doubled customers' response rates to 8.2 percent, which in turn, reduced product promotion costs by 35 percent. This example illustrates the use of neural network technology to

Accepted Answer

A)  increase new customers that often request online product quotes. 
B)  determine customer lifetime value and well-defined customer segments. 
C)  develop new products or make product and service recommendations to customers. 
D)  classify customers by their likely profitability when planning direct marketing strategies. 
A)  increase new customers that often request online product quotes. 
B)  determine customer lifetime value and well-defined customer segments. 
C)  develop new products or make product and service recommendations to customers. 
D)  classify customers by their likely profitability when planning direct marketing strategies. D

Question 3

In a neural network, inputs that are important in predicting the output have smaller weights, whereas the less important inputs have larger weights.

Accepted Answer

A) True 
 B)False  False

Question 4

An automotive insurance company hired Cognitiv to apply their deep learning platform, Neuralmind, to drive prospects to log on to their website and complete an insurance quote online. This is an example of using a neural network to

Accepted Answer

A)  analyze audience sentiment. 
B)  increase new customers. 
C)  entice customers to a brick-and-mortar store. 
D)  predict lead scoring. 
A)  analyze audience sentiment. 
B)  increase new customers. 
C)  entice customers to a brick-and-mortar store. 
D)  predict lead scoring.

Question 5

Which of the following is true of neural networks?

Accepted Answer

A)  They are algorithms trained to recognize patterns in large volumes of data. 
B)  They are cheaper and simpler to execute as compared to linear regression methods. 
C)  They are based on the chemical reactions that make up brain signals. 
D)  They are most useful when predictive relationships are linear. 
A)  They are algorithms trained to recognize patterns in large volumes of data. 
B)  They are cheaper and simpler to execute as compared to linear regression methods. 
C)  They are based on the chemical reactions that make up brain signals. 
D)  They are most useful when predictive relationships are linear.

Question 6

A neural network is constantly learning until the final optimized solution is obtained, which is when model prediction is no longer improving.

Accepted Answer

A) True 
 B)False

Question 7

Shield, a fitness products company, has a health-tracking mobile application called GoFit. The app collects health-related data from sources such as wearable devices and manually entered user data. The data includes sleeping patterns, workouts, nutrition, and related information. Shield can best use this information in a neural network to

Accepted Answer

A)  entice customers to visit a brick-and-mortar store. 
B)  predict lead scoring. 
C)  develop customized digital content, such as exercise and diet recommendations for its app users. 
D)  classify new customers by their potential profitability when planning direct marketing strategies. 
A)  entice customers to visit a brick-and-mortar store. 
B)  predict lead scoring. 
C)  develop customized digital content, such as exercise and diet recommendations for its app users. 
D)  classify new customers by their potential profitability when planning direct marketing strategies.

Question 8

Neural networks

Accepted Answer

A)  often do not work well with out-of-range data. 
B)  have simple architectures. 
C)  require small datasets for optimum prediction. 
D)  do not perform properly when only key predictors are included. 
A)  often do not work well with out-of-range data. 
B)  have simple architectures. 
C)  require small datasets for optimum prediction. 
D)  do not perform properly when only key predictors are included.

Question 9

The logic of neural networks is based on patterns recognized by observing biological activities in the human brain.

Accepted Answer

A) True 
 B)False

Question 10

Describe how marketing strategists employ neural networks to answer business problems and make decisions.

Accepted Answer

Neural networks are a type of artificial

Question 11

HealthX, a fitness products company, launched a mobile application that enables customers to obtain fitness assessments. The app analyzes customers' data to determine potential health issues and then recommends specific measures and products to address areas of concern. The digital interaction mimics an in-person transaction while allowing customers to remain in the comfort of their home. This example illustrates the use of neural network technology to

Accepted Answer

A)  analyze audience sentiment to improve a product. 
B)  entice customers to visit a brick-and-mortar store. 
C)  personalize customer experiences. 
D)  predict lead scoring. 
A)  analyze audience sentiment to improve a product. 
B)  entice customers to visit a brick-and-mortar store. 
C)  personalize customer experiences. 
D)  predict lead scoring.

Question 12

Compare and contrast linear relationships and non-linear relationships.

Accepted Answer

Linear relationships and non-linear rela

Question 13

Identify a true statement about the hidden layer of a neural network.

Accepted Answer

A)  Calculations are carried out in this method and weights are produced from the input layer. 
B)  This layer is used to input data into the neural network for analysis. 
C)  The neural network model arrives at a prediction in the hidden layer. 
D)  Data from the output layer is transferred to the hidden layer. 
A)  Calculations are carried out in this method and weights are produced from the input layer. 
B)  This layer is used to input data into the neural network for analysis. 
C)  The neural network model arrives at a prediction in the hidden layer. 
D)  Data from the output layer is transferred to the hidden layer.

Question 14

A marketing company uses BrainMaker to identify the customers who are most likely to click on their direct mail based on their past purchase behavior. This resulted in a 35 percent drop in advertising costs. This is an example of using a neural network to

Accepted Answer

A)  analyze market sentiment to alter advertising approaches. 
B)  innovate and design new products and categories. 
C)  classify new customers by their likely profitability when planning direct marketing strategies. 
D)  entice customers to visit a brick-and-mortar store. 
A)  analyze market sentiment to alter advertising approaches. 
B)  innovate and design new products and categories. 
C)  classify new customers by their likely profitability when planning direct marketing strategies. 
D)  entice customers to visit a brick-and-mortar store.

Question 15

Summarize practitioner Stephen Brobst's thoughts on the uses and advantages of neural networks.

Accepted Answer

Stephen Brobst, a practitioner in the fi

Question 16

DialogTech provides neural network-driven marketing analytics solutions to manage customer inbound call centers. The collected data includes incoming caller objectives, interactions between the callers and salespersons, and assessment of conversation outcomes. This example from the text illustrates the use of neural network technology to

Accepted Answer

A)  generate personalized content. 
B)  categorize customers. 
C)  predict lead scoring. 
D)  build a dataset. 
A)  generate personalized content. 
B)  categorize customers. 
C)  predict lead scoring. 
D)  build a dataset.

Question 17

Neural networks can aid marketers in predicting customer behavior, understanding buyer segmentation, developing brand strategies, forecasting sales, optimizing inventory, improving marketing automation, developing digital content, and much more.

Accepted Answer

A) True 
 B)False

Question 18

Which of the following is true of training a neural network?

Accepted Answer

A)  A neural network will stop training itself when it reaches a particular threshold or maximum run limits. 
B)  If a neural network is trained too much, the result is underfitting leading to underprediction. 
C)  A neural network is considered to be optimally trained when model prediction no longer improves. 
D)  If a neural network is not trained enough, the result will be overfitting of the model. 
A)  A neural network will stop training itself when it reaches a particular threshold or maximum run limits. 
B)  If a neural network is trained too much, the result is underfitting leading to underprediction. 
C)  A neural network is considered to be optimally trained when model prediction no longer improves. 
D)  If a neural network is not trained enough, the result will be overfitting of the model.

Question 19

In a neural network, the learning rate consists of negative values that generally range between -1 and -10.

Accepted Answer

A) True 
 B)False

Question 20

Regression is useful for forecasting consumer behavior when predictive relationships are linear.

Accepted Answer

A) True 
 B)False

Within a neural network, nodes or neurons

In a neural network, inputs that are important in predicting the output have smaller weights, whereas the less important inputs have larger weights.

An automotive insurance company hired Cognitiv to apply their deep learning platform, Neuralmind, to drive prospects to log on to their website and complete an insurance quote online. This is an example of using a neural network to

Which of the following is true of neural networks?

A neural network is constantly learning until the final optimized solution is obtained, which is when model prediction is no longer improving.

Neural networks

The logic of neural networks is based on patterns recognized by observing biological activities in the human brain.

Describe how marketing strategists employ neural networks to answer business problems and make decisions.

Compare and contrast linear relationships and non-linear relationships.

Identify a true statement about the hidden layer of a neural network.

A marketing company uses BrainMaker to identify the customers who are most likely to click on their direct mail based on their past purchase behavior. This resulted in a 35 percent drop in advertising costs. This is an example of using a neural network to

Summarize practitioner Stephen Brobst's thoughts on the uses and advantages of neural networks.

Neural networks can aid marketers in predicting customer behavior, understanding buyer segmentation, developing brand strategies, forecasting sales, optimizing inventory, improving marketing automation, developing digital content, and much more.

Which of the following is true of training a neural network?

In a neural network, the learning rate consists of negative values that generally range between -1 and -10.

Regression is useful for forecasting consumer behavior when predictive relationships are linear.

Introduction to Marketing Analytics

Data Management

Exploratory Data Analysis Using Cognitive Analytics

Data Visualization

Regression Analysis

Automated Machine Learning

Cluster Analysis

Market Basket Analysis

Natural Language Processing - Text Mining and Sentiment Analysis

Social Network Analysis

Web Analytics

Filters

Exam 6: Neural Networks

Within a neural network, nodes or neurons

In a neural network, inputs that are important in predicting the output have smaller weights, whereas the less important inputs have larger weights.

An automotive insurance company hired Cognitiv to apply their deep learning platform, Neuralmind, to drive prospects to log on to their website and complete an insurance quote online. This is an example of using a neural network to

Which of the following is true of neural networks?

A neural network is constantly learning until the final optimized solution is obtained, which is when model prediction is no longer improving.

Neural networks

The logic of neural networks is based on patterns recognized by observing biological activities in the human brain.

Describe how marketing strategists employ neural networks to answer business problems and make decisions.

Compare and contrast linear relationships and non-linear relationships.

Identify a true statement about the hidden layer of a neural network.

A marketing company uses BrainMaker to identify the customers who are most likely to click on their direct mail based on their past purchase behavior. This resulted in a 35 percent drop in advertising costs. This is an example of using a neural network to

Summarize practitioner Stephen Brobst's thoughts on the uses and advantages of neural networks.

Neural networks can aid marketers in predicting customer behavior, understanding buyer segmentation, developing brand strategies, forecasting sales, optimizing inventory, improving marketing automation, developing digital content, and much more.

Which of the following is true of training a neural network?

In a neural network, the learning rate consists of negative values that generally range between -1 and -10.

Regression is useful for forecasting consumer behavior when predictive relationships are linear.

Introduction to Marketing Analytics

Data Management

Exploratory Data Analysis Using Cognitive Analytics

Data Visualization

Regression Analysis

Automated Machine Learning

Cluster Analysis

Market Basket Analysis

Natural Language Processing - Text Mining and Sentiment Analysis

Social Network Analysis

Web Analytics

Filters