Alright, let’s talk about tracking LIV viewership. It wasn’t something I planned meticulously from the start, more like something I stumbled into.
I started doing some live streaming, mostly just for fun, sharing some gaming sessions and sometimes just tinkering with tech stuff. After a few sessions, I naturally got curious. You know, that little thought pops into your head: “Is anyone actually watching this?” So, I decided to pay a bit more attention to the numbers.
My First Steps
Initially, my “tracking” was super basic. I’d finish a stream, and glance at the summary the platform provided. Usually, it showed peak concurrent viewers and maybe total unique viewers. I didn’t really record it anywhere, just looked and thought, “huh, okay.”
But that wasn’t satisfying my curiosity. I wanted to see if there were any patterns. So, I got slightly more organized. I started a simple text file on my computer. After each stream, I’d jot down:
- The date and time I streamed.
- How long the stream lasted.
- What I was doing (which game, topic, etc.).
- The peak viewer number reported.
- The average viewer number, if available.
It wasn’t fancy, just raw data entry right after signing off.
Digging into the Process
Doing this consistently was the key. After a couple of weeks of logging this info, I started looking back through my notes. I wasn’t running complex analyses or anything, just scanning the numbers alongside the stream details.
I started noticing small things. For instance, streaming late on Saturday nights seemed to pull in a few more viewers compared to weekday afternoons. Makes sense, I guess. I also saw that when I played a specific multiplayer game with friends, the numbers usually ticked up a bit higher than my solo streams. It wasn’t earth-shattering data, but it was interesting to see it reflected in the numbers I’d jotted down.
The challenge was understanding what the numbers truly represented. A high peak could just be a temporary surge, maybe from a host or a random share. The average viewer count felt a bit more meaningful for understanding engagement over time. But even then, platforms calculate these things differently, and sometimes the numbers felt a bit abstract.
What I Found and How I Adjusted
After tracking for a while, I realised obsessing over the raw numbers wasn’t that helpful, especially since my streams were pretty casual. What became more useful was looking at the trends and comparing sessions.
Here’s what I started focusing on:
- Relative Performance: Did this stream do better or worse than the last time I streamed the same thing or at the same time?
- Chat Interaction: Honestly, this became a bigger indicator for me than the viewer count. If the chat was active, even with fewer viewers, it felt like a more successful stream.
- Viewer Retention (Roughly): Looking at the graph if the platform provided one, trying to see where people dropped off. Was it during a boring part? A technical issue?
So, I stopped just logging numbers and started adding small notes like “chat was lively” or “technical difficulties early on” or “viewership dipped when I switched games.” This qualitative info, combined with the basic numbers, gave me a much better feel for how things were going.
Where I’m At Now
Today, I still keep an eye on the viewership stats after each stream. I have a slightly more organized spreadsheet now instead of a messy text file, but the principle is the same. I log the basics, check the trends against my notes, and use it as a loose guide.
It helps me decide things like: maybe I should stream that game more often, or perhaps that stream time isn’t working out. But I don’t let the numbers dictate everything. Sometimes I stream stuff I know won’t get many viewers, just because I enjoy it. The tracking process itself was the valuable part – it made me think more consciously about my streams and what resonates, even if it’s just with a small audience. It’s been a practical way to learn and adjust.
