With 1.3 billion users, Instagram (IG) has become an essential business tool. IG influencer marketing, expected to generate $33.25 billion in 2022, encourages companies and influencers to create trending content. Various methods have been proposed for predicting a post's popularity, i.e., how much engagement (e.g., Likes) it will generate. However, these methods are limited: first, they focus on forecasting the likes, ignoring the number of comments, which became crucial in 2021. Secondly, studies often use biased or limited data. Third, researchers focused on Deep Learning models to increase predictive performance, which are difficult to interpret. As a result, end-users can only estimate engagement after a post is created, which is inefficient and expensive. A better approach is to generate a post based on what people and IG like, e.g., by following guidelines. In this work, we uncover part of the underlying mechanisms driving IG engagement. We rely on statistical analysis and interpretable models rather than Deep Learning (black-box) approaches to achieve this goal. Leveraging innovative domain-relevant features, we first build classifiers to predict posts' engagement. Then, we interpret the best models to determine which type of content will generate the most engagement, maximizing influencers' and companies' profits. We conduct extensive experiments using a worldwide dataset of 10 million posts created by 34K global influencers in nine different categories. Our simple yet powerful algorithms can effectively predict engagement, making us comparable and even superior to Deep Learning-based methods, reaching up to 94% F1-Score. Furthermore, we propose a novel unsupervised algorithm for finding highly engaging topics on IG. Thanks to our interpretable approaches, we conclude by outlining guidelines for creating successful posts.