My approach is to treat feedback and analytics as complementary signals that guide continuous improvement. I start by reviewing quantitative data—such as engagement, conversion rates, and common drop-off points—to identify patterns in how content is performing. Then I pair that with qualitative feedback from users, looking for recurring themes in what people find helpful, unclear, or missing. From there, I prioritise changes based on impact. For example, if multiple users highlight the same confusion point, or analytics show a consistent drop-off in a specific section, I focus on improving clarity, structure, or relevance in that area first. I also test changes iteratively rather than overhauling everything at once. This makes it easier to measure what actually improves performance. Over time, this creates a feedback loop where content becomes more useful, more targeted, and better aligned with user needs. Ultimately, my goal is to make content clearer, more actionable, and genuinely helpful, using both data and real user input to guide decisions.

I usually don't review or do promos for products i dont know about or use, I cant pretend i know everything about dogs or the bible.

get things wrong and ragebait so people comment more and it drives engagement up