Can I export the raw posts, not just a summary?

Yes. Every run returns structured rows with full post text, author, and engagement metrics, exportable as CSV or JSON. The AI summarisation is an optional layer on top, never a replacement for the underlying data.

How do I find the right communities?

Use Reddit Pro's discover mode with your topic terms rather than guessing subreddit names. It routinely surfaces small niche communities that carry better signal than the large obvious ones.

Is this useful for positioning work specifically?

It is one of the strongest applications. Positioning depends on knowing the words your market uses for its problem, and those words are in the posts. Collecting a few hundred and summarising them gives you the vocabulary rather than an internal approximation of it.

Can we monitor competitors continuously?

Yes. Twitter at one credit per result makes continuous monitoring genuinely affordable, and Intent Detection flags the posts worth reacting to so the team reviews a ranked shortlist rather than a feed.

HomeMarketing Teams

Intent data and audience insights, on demand.

Growth marketers and demand gen teams use VoxScrape to research audiences, monitor brand mentions, and build enriched lists for ABM campaigns.

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Why marketing teams use VoxScrape

Marketing teams are asked to know things about the market that nobody has written down. What language do buyers use for this problem? Which objection comes up first? Is the category conversation moving toward us or away?

Those answers exist in public — in Reddit threads, in Twitter complaints, in what commenters say under a competitor's LinkedIn post. The obstacle is that reading enough of it to see a pattern is a research project nobody has time to run.

VoxScrape collects that raw material at volume and applies AI summarisation and intent detection to it, so a week of manual reading becomes a run you launch before lunch.

Who this is for

Demand-gen and growth marketers who need audience insight before a campaign rather than performance data after it.

Product marketers writing positioning and messaging, who need the market's own words rather than an internal guess at them.

Content and brand teams deciding what to publish, and competitive-intelligence owners tracking how rivals are being received.

Sound familiar?

Audience research takes days of manual work before a campaign can launch.

Why it happens: Reading enough threads to find a genuine pattern is slow, and it is the first thing cut when a launch date moves. So campaigns ship on assumption, and the messaging gets corrected after spend rather than before it.

What changes: Collect several hundred posts with comments in one run, then summarise batches with AI at fifteen credits each. The reading week compresses into a run, and the pattern is derived from the full sample rather than the six threads someone had time to skim.

Brand monitoring tools are expensive and don't export usable data.

Why it happens: Most monitoring products are dashboards. They tell you mention volume moved, then trap the underlying posts behind an interface you cannot pipe into anything else. You get a chart when what you needed was the text.

What changes: Every run returns structured rows you export as CSV or JSON. The post text, the author, the engagement metrics — all of it goes into your own analysis, your own spreadsheet, or your own AI pass.

ABM lists are stale the moment they're built.

Why it happens: A list assembled in January describes January. People change roles, companies change priorities, and the intent signal that justified including an account has usually expired by the time the campaign runs.

What changes: Rebuild rather than maintain. A filtered run costs a few hundred credits, so refreshing a target list monthly is cheaper than the meeting where you discuss whether the old one is still accurate.

What a week actually looks like

Campaign research starts with discovery rather than a keyword guess. Reddit Pro's discover mode finds the communities where the audience actually gathers, which regularly turns up small niche subreddits with far better signal than the obvious large ones.

A collection run pulls several hundred posts with comments, because comments carry the recommendations and objections that posts alone omit. The Post Summarizer condenses batches into recurring themes at fifteen credits per batch.

In parallel, LinkedIn Posts surfaces who is publishing about the category and which posts drove real discussion. Those posts become the input for LinkedIn Comments, which returns every person who engaged.

The output is a messaging brief grounded in observed language rather than internal assumption, plus a warm list of people already discussing the problem — assembled in a morning rather than over a fortnight.

What it costs in practice

A typical research cycle: 300 Reddit posts with comments at three credits each, plus several Post Summarizer batches at fifteen each, plus a LinkedIn Posts pass. That is roughly 1,200 credits for a full messaging research round.

Starter's 10,000 monthly credits supports several such cycles a month alongside ongoing monitoring. Pro's 50,000 suits teams running continuous category tracking rather than campaign-triggered research.

Compare against the alternative honestly: a single commissioned message-testing study typically costs more than a year of Pro, and describes one moment rather than a continuous signal you can re-run whenever the market moves.

Frequently asked questions

Can I export the raw posts, not just a summary?: Yes. Every run returns structured rows with full post text, author, and engagement metrics, exportable as CSV or JSON. The AI summarisation is an optional layer on top, never a replacement for the underlying data.
How do I find the right communities?: Use Reddit Pro's discover mode with your topic terms rather than guessing subreddit names. It routinely surfaces small niche communities that carry better signal than the large obvious ones.
Is this useful for positioning work specifically?: It is one of the strongest applications. Positioning depends on knowing the words your market uses for its problem, and those words are in the posts. Collecting a few hundred and summarising them gives you the vocabulary rather than an internal approximation of it.
Can we monitor competitors continuously?: Yes. Twitter at one credit per result makes continuous monitoring genuinely affordable, and Intent Detection flags the posts worth reacting to so the team reviews a ranked shortlist rather than a feed.

Workflows built for Marketing Teams

Research

Competitor Intelligence

Your competitors are being discussed on Reddit, Twitter, and LinkedIn every day — by the exact buyers you're targeting. You're missing every one of those conversations.

See the workflow →Email