When improving lead qualification processes using Einstein Model Builder, what consideration is most important?

When improving lead qualification processes using Einstein Model Builder, including data points from both converted and not converted leads is crucial. This approach ensures that the model has a comprehensive understanding of what characteristics and behaviors differentiate successful conversions from those that did not convert.

By analyzing both types of leads, the model can identify patterns and factors that not only lead to successful sales but also highlight the reasons for failures. Relying solely on converted leads would create a biased model that could overlook important characteristics of leads that have not converted but may still have the potential to do so under different circumstances. This balanced perspective helps in building a more effective qualification model, ultimately improving the overall sales process.

In contrast, selecting data from only the most recent sales quarter could limit the historical context that is relevant for predicting future conversions. Using only converted leads might ignore valuable insights from leads that didn't convert, while focusing on demographic information alone wouldn’t address the diverse factors influencing lead qualification, which can be behavioral or related to their engagement with the product or service. Thus, incorporating a wider data set is vital to creating a robust model that can effectively support lead qualification efforts.