Provide a URL, campaign goal, and target audience. Get five LinkedIn ad copy variants following best practices, labeled for A/B testing in Campaign Manager.
Paste in the client's URL, the campaign goal, and the target audience on LinkedIn. Juma pulls information from the client's site and returns five complete LinkedIn ad copy variants, each built around a different hook angle. Every variant includes intro text, a headline, a CTA, and character counts so the team can move straight to Campaign Manager without reformatting.
Writing five complete ad variants manually, each testing a distinct hook, headline, and CTA, typically takes two to four hours per round. This Flow returns the same output in under two minutes. The brief also includes a run-first recommendation, which normally requires a separate strategy conversation with the team.
Each set of LinkedIn ad copy examples includes five variants testing different angles: a statistic-led hook, a pain-point opener, a social proof approach, a curiosity gap, and a direct benefit statement. The mix adapts to the client's product and audience, and additional angles can be added in a follow-up step. Every variant follows LinkedIn ad copy best practices for hook structure and carries a labeled hypothesis so the team knows what each is designed to prove.
LinkedIn truncates intro text after 150 characters on mobile, placing a "see more" break before the reader reaches the rest of the ad. That makes the opening two sentences carry the full weight of the hook. Unlike platforms where a strong visual can compensate for weak copy, LinkedIn ads play in a passive scroll environment where attention has to be earned before that cut.
Intro text supports up to 600 characters and headlines are capped at 70 characters. The Flow writes within these linkedin ad copy character limits by default and includes per-element character counts in every variant. Teams can verify limits and paste copy directly into Campaign Manager without reformatting.
The Flow pulls information from the client's site to align variants with the brand's actual positioning, product language, and audience signals. This means variants reflect what the client actually says and sells, not just what you brief in. A brief with no URL produces more generic output.
The brief identifies which 2 to 3 variants to prioritize in Campaign Manager's first test, based on the campaign goal and audience segment. LinkedIn needs enough budget per variant to generate statistically meaningful data, so the recommendation focuses initial spend where signal is most likely. Remaining variants rotate in once the first test concludes.
After reviewing the first set of variants, use this step to explore alternative directions before committing to a test. The context from your initial brief carries over automatically, so there is no need to re-enter client details. Use it to replace the two weakest variants with data-driven angles, such as customer outcome stats or same-day delivery claims.
Awareness copy and conversion copy require different angles, CTAs, and format choices. The context from the previous step carries over, so the Flow returns a new set of variants for the next funnel stage without re-briefing the client. Use this step when the first round is approved and the campaign needs creatives for an additional stage.
Once copy is confirmed, this step generates an ad visual matched to the format dimensions for the approved variant. The context and confirmed copy carry over automatically. Use it to produce a professional, scroll-stopping image aligned to the LinkedIn feed environment.
LinkedIn ad copy gets written on repeat: new campaigns, new offers, same client voice and audience. A Juma project stores the brand context and performance history so every round of variants starts from what's already known about the client, not from scratch. If the client project already exists, add these items to it.
Brand Voice Guide
How the client sounds in ads: tone, vocabulary, what to avoid. With this in the project, copy drafts land in the right voice from the first pass without rounds of corrections.
Audience Profile
Who the ads target on LinkedIn: job titles, seniority, company size, and pain points. This context shapes which angles the AI leads with and which CTAs it recommends for the audience segment.
Ad Performance History
Past campaign results: CTR by variant, format, and hook type. The AI uses this to weight recommendations toward what has worked and away from what hasn't for this specific audience.
Output Examples
Two to four high-performing ads from past campaigns, with brief notes on what each was testing. These serve as structural references so new variants match the format and length the team knows performs.
Add a short description to each knowledge item in the project's info field so Juma knows what each file contains and when to use it. For example:
