AI growth manager for Google Ads accounts. GoodLads generates hypotheses to improve your ROAS or CPA, ships each one in one click - never without your approval - and tracks it on a kanban board to a verdict.
Replies
Best
Does it change bids and budgets on its own, or queue them for approval?
"Never without your approval" is the right guardrail for something that touches live ad spend, but I'd want to know how the hypotheses themselves get generated before trusting the approval step alone - if the suggestions are just generic "raise budget on the winning ad group" advice that any account manager would already do, the AI layer isn't adding much beyond a nicer kanban board. What's an example of a hypothesis it shipped that a human account manager would have missed?
@galdayan I would not say would have missed - there are genius account managers out there :) Few examples: - limited by budget - it recommends to reduce the click, so we get more clicks with the same budget. Not every account manager knows that trick - targeting business clients rather than consumers - it suggested to target API related keywords, which the person running the ads did not think about, and found that recommendation very relevant.
Those are both good examples, thanks for laying them out. The B2B keyword one especially - that's a blind spot that's easy to miss when you're deep in day to day account management. Does GoodLads surface the reasoning behind a suggestion, or just the recommended action itself? I'd want to see the "why" before approving a change on a live account, even with the one-click approval gate.
Yes it does. I did a PhD 10 years ago. And I decided to apply the academic approach to explaining each hypothesis. it shows a series of observations either from the account data, or external learnings, and generates a hypothesis based on that, so there is a relatively strong understanding what the hypothesis is based on.
Replies
Does it change bids and budgets on its own, or queue them for approval?
TimeTuna
@tomveber Hi Tom,
It acts in a kanban way proposing ideas, you approve them, then go implemented, then they are tested.
Dial
"Never without your approval" is the right guardrail for something that touches live ad spend, but I'd want to know how the hypotheses themselves get generated before trusting the approval step alone - if the suggestions are just generic "raise budget on the winning ad group" advice that any account manager would already do, the AI layer isn't adding much beyond a nicer kanban board. What's an example of a hypothesis it shipped that a human account manager would have missed?
TimeTuna
@galdayan I would not say would have missed - there are genius account managers out there :)
Few examples:
- limited by budget - it recommends to reduce the click, so we get more clicks with the same budget. Not every account manager knows that trick
- targeting business clients rather than consumers - it suggested to target API related keywords, which the person running the ads did not think about, and found that recommendation very relevant.
Dial
Those are both good examples, thanks for laying them out. The B2B keyword one especially - that's a blind spot that's easy to miss when you're deep in day to day account management. Does GoodLads surface the reasoning behind a suggestion, or just the recommended action itself? I'd want to see the "why" before approving a change on a live account, even with the one-click approval gate.
TimeTuna
@galdayan Hey Gal,
Yes it does. I did a PhD 10 years ago. And I decided to apply the academic approach to explaining each hypothesis. it shows a series of observations either from the account data, or external learnings, and generates a hypothesis based on that, so there is a relatively strong understanding what the hypothesis is based on.