Feature Engineering — AI for Marketers

Feature Engineering: What is it?

Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.

What are some use cases for Marketers?

Marketers can use feature engineering in predictive models for customer segmentation, churn prediction, or sales forecasting. For instance, features like customer lifetime value, purchase frequency, or days since last purchase could be derived from raw customer transaction data.

What are the advantages for Marketers who understand Feature Engineering?

Proper feature engineering can lead to more accurate models, making the marketing strategies based on these models more effective.

What are the challenges related to Feature Engineering?

Feature engineering is often domain-specific, requiring knowledge of the specific marketing context. Also, it can be time-consuming and requires a deep understanding of the data.

Examples of applying Feature Engineering for Marketers

In predicting house prices, features could be derived from raw data like the number of rooms, location, size of the house, age of the house, etc.

The future of Feature Engineering

The rise of automated feature engineering and deep learning (which can automatically learn useful features) may change how marketers approach feature engineering.
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