What step must be taken when configuring a model in Model Builder for predicting customer engagement with campaign emails?

When configuring a model in Model Builder for predicting customer engagement with campaign emails, it is critical to ensure that the model is trained on a diverse dataset that includes both engaged and unengaged customer interactions. This diversity in the training data allows the model to learn from a wide range of behaviors and outcomes, leading to more accurate predictions.

Training on a dataset that encompasses various customer responses helps the model to understand the factors that contribute to both positive and negative engagement. If the model is only trained on data from customers who are already highly engaged, it may fail to generalize effectively to the broader audience, leading to biased predictions. Likewise, ignoring past engagement data would undermine the model's ability to recognize patterns in customer behavior over time, weakening its predictive capabilities.

Implementing these practices ensures a more comprehensive understanding of customer engagement, ultimately enhancing the effectiveness of email campaigns.