GoHalves
Co-marketing matchmaking for B2B tech brands
GoHalves matches non-competing B2B tech brands whose audiences overlap, then drafts the joint campaign for both sides.
You give it your product, your ideal customer and your goals. It scores every other company in the pool on audience overlap and size parity, rules out anyone selling too close to what you sell, and introduces the ones worth a conversation. Both sides have to accept before anything happens, and neither sees the other's full profile until they do.
Every campaign type is a two-way exchange. Newsletter swaps, joint content, shared audiences. Nobody borrows an audience without giving one back, which is the difference between co-marketing and paid placement.
Features
- Audience matching: Companies are scored on audience overlap and size parity rather than keyword tags, so you are matched with a peer rather than someone far bigger or far smaller.
- Competitor filtering: Anyone selling too close to what you sell is ruled out before you ever see them.
- Two-way by design: If nothing flows both ways, the campaign cannot be created.
- Drafted campaigns: Once two brands match, the campaign is written from both profiles. Subject lines, host read scripts, guest article briefs, depending on the format.
- Vetted membership: Every member is reviewed before entering the pool.
- Mutual accept: Neither side sees the other's full profile until both have accepted.
- Voice onboarding: Answer the profile interview by speaking rather than typing. Audio is transcribed and never stored.
- Tracking links: Each campaign generates links for both sides, with clicks recorded automatically.
Use Cases
- Newsletter swaps with a brand that sells to your buyers but not your product.
- Joint content, a report or guide co-authored and published by both sides.
- Podcast and webinar guest exchanges.
- Reaching a new audience without buying it, when paid acquisition costs more than it returns.
- Finding partners without the three weeks of research and the awkward cold email.
- Vetting a potential partner before you commit, including checking they are not a competitor.
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Comments
I spent fourteen years running marketing in-house, and co-marketing was the one channel that worked every time I got it off the ground. It just took three weeks of research and an awkward cold email to start, so I almost never did it. GoHalves is the part I kept doing by hand. It scores companies on who they sell to, rules out anyone selling too close to what you sell, and drafts the campaign once both sides accept. The surprise in building it was that the hard problem is exclusion rather than ranking. Match on similarity alone and you hand someone their closest competitor. Happy to answer anything, including the bits that are not finished yet.
The audience-overlap scoring before competitor filtering is the smart order to do it in - most matchmaking tools I've seen filter by category tags first and only check fit after. Curious how GoHalves verifies the audience-size numbers a brand self-reports during the voice interview - is there any check against public traffic/subscriber data, or is it trust-based until a campaign actually runs?
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Comments
I spent fourteen years running marketing in-house, and co-marketing was the one channel that worked every time I got it off the ground. It just took three weeks of research and an awkward cold email to start, so I almost never did it. GoHalves is the part I kept doing by hand. It scores companies on who they sell to, rules out anyone selling too close to what you sell, and drafts the campaign once both sides accept. The surprise in building it was that the hard problem is exclusion rather than ranking. Match on similarity alone and you hand someone their closest competitor. Happy to answer anything, including the bits that are not finished yet.
The audience-overlap scoring before competitor filtering is the smart order to do it in - most matchmaking tools I've seen filter by category tags first and only check fit after. Curious how GoHalves verifies the audience-size numbers a brand self-reports during the voice interview - is there any check against public traffic/subscriber data, or is it trust-based until a campaign actually runs?
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