Graph Neural Networks (GNNs) have emerged as effective tools in recommender systems. Among various GNN models, LightGCN is distinguished by its simplicity and outstanding performance. Its efficiency has led to widespread adoption across different domains, including social, bundle, and multimedia recommendations. In this paper, we thoroughly examine the mechanisms of LightGCN, focusing on its strategies for scaling embeddings, aggregating neighbors, and pooling embeddings across layers. Our analysis reveals that, contrary to expectations based on its design, LightGCN suffers from inflexibility and inconsistency when applied to real-world data.We introduce LightGCN++, an enhanced version of LightGCN designed to address the identified limitations. LightGCN++ incorporates flexible scaling of embedding norms and neighbor weighting, along with a tailored approach for pooling layer-wise embeddings to resolve the identified inconsistencies. Despite its remarkably simple remedy, extensive experimental results demonstrate that LightGCN++ significantly outperforms LightGCN, achieving an improvement of up to 17.81% in terms of NDCG@20. Furthermore, state-of-the-art models utilizing LightGCN as a backbone for item, bundle, multimedia, and knowledge-graph-based recommendations exhibit improved performance when equipped with LightGCN++.