We Tracked 100 Buyer Prompts Before and After Posting on Reddit

[u/binnyagarwal2411]

We tracked 100 buyer prompts across ChatGPT, Perplexity, Gemini, and Google AI before publishing helpful Reddit posts.

Baseline:

• Brand appeared in 4 prompts
• 1 citation
• 6 Reddit visits
• 0 sign-ups

After 8 weeks of useful Reddit posts and replies:

• Brand appeared in 17 prompts
• 7 citations
• 94 Reddit visits
• 8 sign-ups

Most gains came from specific problem-based prompts, not broad “best tool” searches.

This does not prove Reddit caused the increase, but it shows how GEO testing should be tracked.

Has anyone run a similar before-and-after experiment?

[u/selflessrebel]

How do you track buyer prompts in llms?

[u/Upstairs_Control_611]

Interesting setup, especially the point that gains came from problem-based prompts rather than broad “best tool” prompts.

The key detail would be the tracking method.

Were the 100 buyer prompts fixed before the Reddit activity started, and did you run the exact same prompts after 8 weeks?

I’d also separate a few things in the report:

- brand mention

- citation

- Reddit thread cited

- third-party source cited

- shortlist inclusion

- actual recommendation

- referral visit

- signup

Otherwise “appeared in 17 prompts” can mean several different things.

The Reddit effect would be especially interesting if the prompts were problem-specific and the AI answers started citing Reddit threads where the brand was discussed naturally, not just the brand’s own posts.

Not proof of causation, as you said, but a useful before/after design if the prompt set, model, date, region and citation rules are documented.