Search Ads Manager AI Agent

Hacker News

Hey HN! I'm Sidhant and I want to share SAM: Search Ads Manager AI agent. It's an AI agent-based workflow that lets you create search ad campaigns in a breeze. Users can research keywords for search ads, get ad copy suggestions, and make changes to your ad groups - all with a conversational AI agent. Back when I joined my current company (Houseware), I had to set up our marketing function - which was the first time I actually used Google Ads. I've plenty of gripes, but my biggest problem with search ads is the insane amount of time and effort it takes to come up with the right ad to run (from keyword to ad copy). You need to know user insights, trending topics and business context - then combine them all together into specific keywords. The default for any marketer is to spend days (or weeks) researching the right keywords. With LLMs, this can become a simpler problem to solve. A sneak peek on how it works: - Currently, the user gives a prompt about the kind of campaign they're running, and under the hood, a call is fired to GPT to suggest the best keywords for the campaign. This list of keywords is then passed to the Google Ads API and the most relevant keywords, along with their forecast metrics (monthly search volume, etc.) is shown on the web app Unlike Google Ads, which requires a set of keywords to suggest more keywords, I want the user to just be able to share the business context of the campaign that they're running, and let the AI agent create the rest of the campaign for them. The current app is quite basic, but I'm planning to improve the keyword research (do primary research on the web instead of just fire a call to the LLM). Do check it out, I'd love your feedback! :)

Visit product View on Hacker NewsAug 28, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, google, user · Missing: mac, agents, macos

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, google, monthly · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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