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This issue of TLDR Marketing covers nine new LinkedIn features and how to use them, argues that sentiment scores alone miss real social insights, and offers practical tips on subject lines, AI use, and experiment design. It also highlights debates around under-16 social media bans, AI’s role as an augment rather than replacement, confidence scoring flaws, and emerging hybrid AI verification models.

• Yale found zero job losses from AI automation over 33 months, but a 12.2% task completion boost and 25.1% speed gain when used as a helper
• Australia's under-16 social media ban still let 70% of kids retain partial access, showing enforcement limits
• Sentiment scores alone (positive/negative) miss the real insights buried in comment threads and recurring themes
• Rigid p<0.05 significance thresholds can leave up to 25% of potential experimental gains on the table

LinkedIn now lets multiple profiles or Pages co-author one post, listing every collaborator at the top and delivering the content across all their networks. It uses an invite-and-accept flow like Instagram’s collab posts and works for both personal profiles and brand Pages. The feature is in beta and aims to boost reach, credibility, and shared ownership in creator-brand partnerships.

• LinkedIn is beta-testing Collaborative Posts, letting two or more profiles/pages co-author a single post with equal billing (unlike a tag or mention), and it appears in each collaborator's feed.
• It requires opt-in via Me → Settings → Notifications → Collaborations, and page participation requires a super admin to enable and accept invites.
• Key details remain unknown—like the max number of collaborators or whether analytics are shared—though Instagram's version caps at six collaborators with shared metrics for comparison.
• Intended uses include brand-creator partnerships, employee amplification, event recaps, product launches, and customer spotlights, all leveraging shared authorship for added credibility.

Amazon now applies its shopping and browsing data to Netflix CTV ads and ties them back to Amazon purchase outcomes, though Netflix-level attribution via AMC is still on the roadmap. It also taps LinkedIn’s professional data for job-based B2B targeting on premium streaming, strengthening its closed-loop attribution and expanding advertiser reach.

• Amazon Audiences can now target Netflix viewers using Amazon shopping/browsing data (live since mid-April US, mid-May UK), but Netflix impression data isn't yet in Amazon Marketing Cloud, so purchase attribution isn't fully closed-loop.
• Amazon DSP now uses LinkedIn data (job titles, seniority, industry) to enable B2B targeting on CTV—live in the US, coming to UK—filling a gap since no scaled professional-targeting option existed for CTV before.
• The closed-loop attribution advantage mainly benefits brands that sell through Amazon; others still need third-party attribution or clean rooms.
• Early EMEA tests show solid scale for Amazon Audiences on Netflix, with the next test being whether CTV exposure actually drives Amazon add-to-carts and purchases.

This article shows how to merge organic LinkedIn posts and paid ads into a single feedback loop. You write content for your target accounts, boost it with tight ad targeting, then use ad metrics like clicks and dwell time to guide your next round of posts.

• Writing organic LinkedIn posts for a specific ICP and then boosting them with paid ads turned even weak organic posts into pipeline drivers, getting KlientBoost qualified booked calls for about $250 and overall CAC to roughly $1,000
• Running parallel brand awareness (capped at 3 impressions/7 days) and engagement (manual bid) campaigns on the same ads/audience lets engaged users get funneled into the engagement campaign while awareness reaches fresh qualified prospects
• Success is measured beyond likes/clicks, using landing-page CTR and ad dwell time as leading indicators alongside attributed pipeline and in-platform CAC as lagging indicators
• Monthly reviews of top content themes/formats plus insights from discovery calls feed back into new post ideation, creating a continuous loop between organic content and paid performance data

This article emphasizes the untapped potential of LinkedIn for founders seeking customers and investors. It offers practical tips for creating engaging posts that highlight metrics, build narratives, and leverage pre-existing credibility, while addressing the initial discomfort of posting.

• LinkedIn's algorithm rewards original posts, letting founders with small followings get big engagement—like Salar Shahini's 850+ likes on a funding announcement.
• Posts with concrete metrics (e.g., Rork's 500,000 user-created projects) outperform vague updates and generate real leads.
• Personal, narrative-driven posts about the founding journey build emotional investment from followers, as with Taylor Offer.
• The "trough of cringe" that stops most people from posting is actually a competitive advantage, since simple, genuine posts beat polished ones and most competitors avoid posting at all.