MLS-C01 · Question #24
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora…
The correct answer is B. Use clustering on customer profile data to understand key characteristics of consumer segments. The goal is to identify what characteristics define the company's existing customers so that similar people can be targeted on social media - a classic lookalike audience problem. Clustering (unsupervised learning) groups existing customers into behaviorally or demographically…
Question
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:
- Profiles for all past and existing customers
- Profiles for all past and existing insured pets
- Policy-level information
- Premiums received
- Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?
Options
- AUse regression on customer profile data to understand key characteristics of consumer
- BUse clustering on customer profile data to understand key characteristics of consumer segments.
- CUse a recommendation engine on customer profile data to understand key characteristics of
- DUse a decision tree classifier engine on customer profile data to understand key characteristics of
How the community answered
(25 responses)- A12% (3)
- B80% (20)
- C4% (1)
- D4% (1)
Explanation
The goal is to identify what characteristics define the company's existing customers so that similar people can be targeted on social media - a classic lookalike audience problem. Clustering (unsupervised learning) groups existing customers into behaviorally or demographically similar segments without requiring pre-labeled 'potential customer' data, revealing the key attributes of each segment. Those attributes can then be used to target matching users on social media. Regression (A) predicts a continuous output, not customer segments. A recommendation engine (C) suggests products to users. A decision tree classifier (D) requires labeled examples of the target class ('potential new customer'), which does not exist.
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