Search advertising has been refined for twenty years. Conversational placement borrows its logic but changes the inputs.
What transfers
- The commercial logic: intent, relevance, message match, conversion tracking.
- Your negative keyword thinking, translated into conversation exclusions.
- Landing page discipline and offer testing.
- Your understanding of which customer segments are actually profitable.
What does not transfer
| ChatGPT Ads | Traditional search | |
|---|---|---|
| Input signal | Whole conversation, often several turns | One query string |
| Competitive context | One answer, one placement slot | Ten results plus ads |
| Query length | Long, natural language, comparative | Short, keyword-shaped |
| Match control | Intent-level, coarser | Keyword match types, precise |
| Creative | Conversational copy | Headline and description slots |
The practical implication
In search, you buy a keyword and hope intent is behind it. In a conversation, intent is explicit and stated at length, but you have less granular control over exactly which conversations you appear in. That trade means the winning lever moves from bidding precision toward message and offer quality.
Where to start if you already run search
Take your top-converting search themes, rewrite them as the questions a buyer would actually ask an assistant, and check which of those questions produce answers where a vendor is named. Those are the conversations worth entering first.
Frequently asked questions
Will this cannibalise our search spend?
Some overlap is likely at the decision stage. The way to know is to hold search steady while you test the new channel, then compare incremental qualified leads rather than raw click counts.
Do keyword match types exist here?
Not in the same form. Control is exercised through intent mapping, inclusions and exclusions rather than match types, which is why the strategy phase carries more weight than it does in search.