MLS-C01 Question 129
Select 2A retail company wants to improve its customer experience by predicting which customers are likely to stop purchasing from their platform. They have a large dataset of customer transactions spanning the last 5 years. However, they also want to know if using machine learning is the right approach for this problem. Which of the following considerations indicate that machine learning is appropriate for this scenario?
- A
The problem requires identifying patterns in historical customer behavior to make predictions about future behavior.
- B
There is a well-defined set of rules that can accurately determine if a customer will stop purchasing.
- C
The dataset is large and contains a variety of features, such as purchase frequency, product categories, and demographics.
- D
The company wants to create a static checklist that customer service representatives can use to identify at-risk customers.
Show answer and explanation
Correct answers: A, C
Explanation
Machine learning is most appropriate when a problem involves identifying patterns in data to generate predictions or insights, particularly when these patterns are complex and not easily captured by static rules. In this scenario, the company has a large dataset and needs to predict customer churn based on historical behavior, making it a good fit for machine learning. However, if there were a simple set of deterministic rules or if the goal were to create a static checklist, machine learning would not be necessary.
- A. Correct.
Correct. Machine learning is well-suited for problems where patterns or trends in historical data can be used to predict future outcomes, such as customer churn.
- B. Incorrect.
Incorrect. If a well-defined set of rules exists and can address the problem accurately, machine learning is not necessary.
- C. Correct.
Correct. Machine learning performs well with large, diverse datasets that provide enough features to identify complex patterns.
- D. Incorrect.
Incorrect. A static checklist suggests a rule-based or heuristic approach, which does not require machine learning.