Segment trees are really useful for certain situations where other types of data structures might not work as well. They are great for handling range queries and making updates when the data changes. Let’s break it down into simpler parts.

1. Range Queries
Segment trees are perfect for when you need to get information over a range of elements. For example, maybe you want to find the total, the smallest, or the largest number in a part of an array multiple times. Segment trees can help with that really well. Other structures, like simple arrays or something called Binary Indexed Trees, can handle these tasks too, but segment trees do it faster. They can update and answer your questions in about $O(\log n)$ time, which is much quicker when you have a lot of data to work with.

2. Dynamic Updates
If you often need to change the data, segment trees are the way to go. Imagine you need to change the value of a number and want that change to reflect in your range queries right away. Segment trees let you make those changes fast, while using a simple array would take way longer—about $O(n)$ time—since you’d need to adjust a lot of numbers after.

3. Multiple Operations
If your tasks need different types of operations on an array, like finding sums, smallest values, largest values, or even counting unique elements, segment trees can help. They can be customized to handle different questions you might have, showing a lot more flexibility than other data structures.

4. Non-static Data
Sometimes, the dataset you work with changes a lot, like in an online system. Segment trees can handle these changes easily without taking up extra resources. They are built in a way that allows them to manage memory well while still dealing with changing datasets.

5. Lazy Propagation
Segment trees also have a feature called lazy propagation. This means you can make range updates without having to do all the calculations right away. For example, if you need to add a number to a range of values over and over again, lazy propagation lets you wait to do those updates. This keeps your update and query times down to about $O(\log n)$.

In short, use segment trees when you need to efficiently handle range queries, make quick updates, perform different operations on ranges, or work with changing datasets. They really perform well when you need speed and flexibility with your data.