assignment 5

.docx

School

University of Nebraska, Omaha *

*We aren’t endorsed by this school

Course

8700

Subject

Marketing

Date

Jan 9, 2024

Type

docx

Pages

Uploaded by ChefHippopotamusMaster248

The following questions are to be graded based on completion: Give an example of a problem, from your work or something you are familiar with, that could be solved with regression algorithms. Describe what is the outcome, such as what it is and how it should be measured. Give a list of features you would include in your analysis. If possible, also describe which performance metric you will use for your analysis. Problem: Predicting Click-Through Rates in Social Media Advertising In social media marketing, a common challenge is optimizing the performance of ad campaigns to maximize user engagement and click-through rates (CTRs). To address this, regression algorithms can be employed to predict the likelihood of a user clicking on a specific ad based on various features. The goal is to develop a regression model that accurately predicts click- through rates for different ads. By leveraging such a model, social media marketers can optimize their advertising strategies, allocate budgets more effectively, and enhance user engagement by delivering content that is more likely to resonate with the target audience. Outcome: The outcome of this regression analysis would be a predicted click-through rate for each ad, indicating the probability that a user will click on the ad when exposed to it. Features for Analysis: ● Ad Content: The text, images, or videos used in the ad. ● Ad Placement: The location on the social media platform where the ad is displayed. ● Target Audience: Demographic information such as age, gender, location, and interests. ● Ad Format: Whether the ad is a carousel, image, video, etc. ● Day and Time: The day of the week and time of day when the ad is shown. ● Previous Engagement: User's historical interaction with similar ads or content. ● Device Type: Whether the user is on a desktop, mobile, or tablet. Performance Metric: The performance of the regression model could be evaluated using metrics like Mean Squared Error (MSE) or Mean Absolute Error (MAE). These metrics quantify the difference between the predicted click-through rates and the actual click-through rates. The lower the error, the better the model's predictive accuracy. Give an example of a problem, from your work or something you are familiar with, that could be solved with classification algorithms. Describe what is the outcome, such as what it is and how it should be measured. Keep in mind that, for classification, your outcome should be categorical. Give a list of features you would include in your analysis. If possible, also describe which performance metric you will use for your analysis.

Problem: Identifying High-Value Customers in Social Media Marketing Outcome: The outcome of the classification analysis is to categorize social media users into two groups: "High-Value Customers" and "Non-High-Value Customers." This classification helps identify users who are more likely to make valuable interactions, such as making purchases, subscribing to services, or engaging with premium content. Features for Analysis: ● Historical Engagement: User's past interactions with ads, posts, or content. ● Purchase History: If applicable, the user's history of making purchases through social media ads. ● Time Spent on Platform: The average time a user spends on the social media platform. ● Click-Through Rates: The frequency of a user clicking on ads. ● Demographics: Age, location, gender, and other demographic information. ● Ad Preference: The type of ads a user typically engages with. ● Frequency of Interaction: How often a user interacts with the platform. Performance Metric: For this classification problem, metrics like accuracy, precision, recall, and F1 score would be relevant. The choice would depend on the specific goals. In social media marketing, precision might be crucial to avoid wasting resources on users incorrectly identified as high-value. Simultaneously, recall could be important to ensure all potential high-value customers are correctly targeted. Therefore, a balance between precision and recall, such as F1 score, might be a suitable performance metric. The following questions are to be graded based on correctness: Predicted Positive Predicted Negative Actual Positive 62 38 Actual Negative 93 57 Based on the above confusion matrix, calculate the values of Accuracy Accuracy= (62+57)/(62+93+57+38)=119/250=0.476 or 47.6% Precision Precision= 62/(62+93)=62/155=0.4 or 40% Recall Recall= 62/(62+38)=62/100=0.62 or 62% F1 score F1 Score=2×(( 0.4*0.62)/(0.4+0.62))=2×0.243=0.486 or 48.6%

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version