Mining keywords from competitor reviews

Every ASO tool scans rival titles. It is a good source and a closed one: everybody sees the same word pool, reaches the same conclusions, and ends up competing over the same ten words.

There is a source nobody looks at: reviews. A user describing an app writes in their own language, not in marketing language. And when they sit down to search, they use the same language.

Store text is written in category language: “language learning platform”, “comprehensive course content”. Reviews are written in outcome language: “to learn sign language”, “pronunciation practice”.

The second group is far closer to what gets typed into the search box. When we ran this method on a language app, most of the keywords in its cusp band came from this channel - a title scan would have found none of them.

Collect rivals' 4-5 star reviews and extract 2-3 word phrases by frequency. Single words are usually too general; four words and up is a sentence.

The 1-2 star reviews serve a different purpose. The repeated complaints there produce no keywords but they do produce opportunity: where a rival is weak is where you position.

The method has an obvious problem. Phrases like “it helped me a lot” or “I am trying to learn” are frequent, and because of iTunes's loose matching they even show up in rankings. But nobody types them into the App Store.

The most reliable way to weed them out is the pronoun check: a phrase carrying a first or second person pronoun is almost never a search term. Put that in as a filter and what remains is genuinely searchable.

The second safety net is popularity verification: however good a candidate looks, if its measured popularity is below the floor it has not earned a place in your metadata.