Zovda
Brand monitoring starts with a query, and the query is usually one word: the brand name, spelled the way the company spells it.
That query returns the mentions written by people who care about spelling. Everyone else is invisible.
Build the alias list from data
Do not brainstorm the variants. Pull a large sample of posts containing the canonical name and read how people actually write it: abbreviations, spacing, punctuation, the product name used as a stand-in, the common typo.
Then add the variants that never contain the brand name at all, such as the flagship product, the slogan, or the founder, if those are what the audience actually talks about.
Precision and recall pull in opposite directions
Every alias you add increases recall and adds collisions. Short names and common words generate more noise than they are worth, and a monitor full of noise gets ignored within a month.
Keep two lists: broad, for discovery, and strict, for reporting. Measure both.
Separate the product from the company
Corporate mentions and product mentions answer different questions. A company that is praised for its hiring and criticized for its flagship product has one reputation and two very different signals.
Tag which one a mention is about, even if the tag is coarse.
Re-audit the alias list every quarter
Language moves. A nickname that appeared six months ago now carries a large share of the conversation, and a retired product name is still generating noise.
Sampling a hundred recent mentions and checking what the query missed is an hour of work with a high return.
Zovda assembles these collections through Datuva, where the query and the alias list stay with the export. The social media data collection shows the fields behind it.
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