LinkedIn just made its biggest Campaign Manager update in years. The platform is addressing two problems marketers have complained about for a decade: confusing nomenclature that didn't match industry standards, and limited tools for proving ROI to budget-conscious executives.
The changes include industry-standard naming that matches Facebook and Google, six new advertising features ranging from AI-powered targeting to premium placement options, and a Company Intelligence API that showed 287% more companies reached in beta testing.
According to Dreamdata's 2025 LinkedIn Ads Benchmarks Report, LinkedIn's share of B2B advertising budgets grew from 31% in H1 2024 to 39% by year-end, making it the largest single platform for B2B spend when you separate Google's network into Search, Display, and YouTube. The report analyzed 23 million sessions across 220,000+ customer journeys from hundreds of B2B companies and found LinkedIn delivers 113% return on ad spend, the only major platform showing positive ROI.
For context: Google Search delivered 78% ROAS and Meta delivered 29% ROAS in the same analysis. LinkedIn costs more per click, but converts at higher rates with better-qualified leads.
The Naming Fix Everyone Wanted
If you've managed ads on multiple platforms, you know the frustration. Facebook calls the top level "Campaign." Google calls it "Campaign." LinkedIn called it "Campaign Group." Then LinkedIn's middle tier was "Campaign," which corresponded to "Ad Set" everywhere else.
Starting January 2025, LinkedIn adopted industry standards:
- Campaign Group → Campaign
- Campaign → Ad Set
- Ad → Ad (this one stayed the same)
Your historical data stays intact. LinkedIn is just relabeling the containers. If you export reports or use APIs, field names will update automatically in most tools within 2-3 weeks.
Why this helps: Cross-platform reporting gets simpler. Instead of maintaining translation layers that convert "Campaign Group" to "Campaign" when pulling data from LinkedIn and Facebook into the same dashboard, everything aligns. For teams managing Google Ads, Meta, and LinkedIn together, this cuts out one annoying step in every report.
Six Features Worth Your Attention
1. Auto-Targeting and AI Draft Creation (SMBs)
LinkedIn's AI analyzes your company page and campaign objective to suggest audience targeting and draft ad copy. You set a goal (awareness, leads, conversions), review recommendations, and edit as needed.
Who should test it: New advertisers without historical data, or anyone launching a product in an unfamiliar market. If you already have six months of LinkedIn performance data, your own learnings probably beat generic AI suggestions.
Real constraint: The AI learns from aggregate patterns across millions of campaigns. It's smart about general trends ("companies in this industry respond to case studies") but doesn't know your specific customers' quirks.
2. Thought Leader Event Ads
Instead of creating a standard event ad from your company page, you sponsor an organic post from an employee or executive that mentions your event. The post runs as an ad but keeps the personal profile picture and name.
Setup requirements:
- Person must be a verified thought leader on your company page
- Post must link to an event landing page
- Minimum $25/day budget
The catch: Currently limited to posts with event links. If your landing page doesn't have event schema markup (registration form, specific date, calendar functionality), LinkedIn won't approve it for this format.
3. First Impression Ads (Premium Placement)
Your ad appears in the first feed slot every time someone opens LinkedIn, before organic posts, before other ads. It's expensive. Beta testers haven't disclosed exact rates publicly, but premium placement typically costs 5-10x standard CPMs.
Makes sense for:
- Product launches with 48-72 hour windows
- Account-based campaigns targeting under 1,000 people
- Executive announcements where timing matters
Doesn't make sense for: Evergreen campaigns, lead gen at scale, or testing new creative concepts.
4. Company Intelligence API
This API lets you pull firmographic data and intent signals into your CRM: company size, growth rate, technologies used, hiring patterns, and engagement with your content.
Beta users reported:
- +287% companies reached
- +75% marketing qualified leads
- -43% customer acquisition cost
Access: Only available through LinkedIn's certified partners including Channel99, Dreamdata, and Factors.ai. You can't pull this data directly, it requires integration through these platforms.
Practical use: When a target account shows multiple intent signals (pricing page visit, whitepaper download, job posting for a relevant role), your CRM alerts sales. The SDR reaches out with context instead of cold calling.
5. Dynamic UTMs
LinkedIn automatically appends UTM parameters based on your campaign structure. Define the format once, and every ad click includes campaign name, ad set name, creative ID, and optional targeting criteria.
What this fixes: Manual UTM building is tedious and error-prone. When you're running 50+ ad sets, typing out UTM strings for each one creates typos, inconsistencies, and gaps in your analytics. Someone forgets the underscore, someone else uses dashes, another person abbreviates campaign names differently. Your Google Analytics data becomes a mess.
Dynamic UTMs solve this by generating parameters automatically from Campaign Manager fields. If your campaign is named "Q1_LeadGen_Enterprise," every ad in that campaign gets utm_campaign=Q1_LeadGen_Enterprise without manual entry.
Migration consideration: If you're already using manual UTM parameters with a specific format, decide whether to switch completely or keep your existing system. Mixing both creates duplicate tracking in Google Analytics. Most teams either migrate everything to dynamic or keep everything manual, the hybrid approach causes more problems than it solves.
6. Data Driven Attribution
LinkedIn's previous attribution was last-touch only. The new model tracks first touch, last touch, linear, and time decay across devices. You can see every touchpoint in a conversion path: LinkedIn ad (day 1) → organic search (day 3) → email (day 7) → LinkedIn retargeting (day 14) → conversion.
Why it changes budget decisions: One marketing director found LinkedIn had the worst ROI by last-touch analysis. After implementing Data Driven Attribution, they discovered LinkedIn influenced 64% of conversions but only closed 18%. They reallocated budget from bottom-funnel Google to mid-funnel LinkedIn content, resulting in 29% more pipeline.
For teams managing attribution across multiple platforms, tools that consolidate data from LinkedIn, Google Ads, and Meta can show the complete picture instead of stitching together partial views from each platform's dashboard.
Why Budget Shifted to LinkedIn
According to Dreamdata's report, LinkedIn captured 39% of B2B ad spend by late 2024, up from 31% six months earlier. That's the largest single platform allocation, surpassing Google Search (37%) when you break Google's network into separate components.
This isn't just LinkedIn's marketing claims. The data comes from hundreds of B2B companies using Dreamdata's attribution platform, tracking actual spend allocation across platforms. The 8 percentage point increase in six months represents billions in shifted budget at industry scale.
Three Drivers
Better targeting at account level: LinkedIn knows company size, industry, job title, and seniority in ways consumer platforms can't match. When you target "Directors of Marketing at SaaS companies with 200-1,000 employees," you're reaching specific buying committees, not demographic approximations.
Facebook and Google target individuals. LinkedIn targets professionals in a business context. The platform knows what company someone works for right now, what their actual job title is (not what they claim in a profile), and whether they have budget authority. That precision matters when your average deal size is $50,000+ and buying committees have 6-8 decision-makers.
Proof of pipeline impact: LinkedIn is the only major platform delivering positive ROAS in Dreamdata's data, 113% compared to Google Search at 78% and Meta at 29%. When you can show "$50,000 ad spend → $1.4M pipeline → $340K closed revenue," CFOs approve budget increases.
The report found that LinkedIn influences 36% of all sales qualified leads and 35% of closed deals in B2B. Google Ads comes second at 21% of SQLs and 25% of deals. Meta influences 12% of SQLs and 14% of deals. If you're optimizing for pipeline contribution rather than click volume, the numbers favor LinkedIn.
Changing buyer behavior: B2B buyers do 70% of research before talking to sales. LinkedIn is where they're doing it, reading industry posts, downloading comparison guides, watching demos. The platform sees 700+ million weekly active users, with most in business contexts during work hours.
Compare that to Facebook, where people scroll during lunch breaks and evenings. Or Google Search, where they're actively looking for solutions but you're competing on keywords with dozens of competitors. LinkedIn catches buyers in research mode, reading content from peers and industry experts.
What This Means for Your 2025 Strategy
If You're Already Running LinkedIn Ads
- Enable Dynamic UTMs for new campaigns starting January 2025. Keep manual UTMs on active campaigns to avoid breaking historical data.
- Test Thought Leader Event Ads if you run webinars or conferences. Start with one sponsored post per event and compare performance to standard event ads.
- Request Company Intelligence API access if you're doing account-based marketing. Start by syncing data for your top 100 target accounts.
- Turn on Data Driven Attribution and compare 30-60 days of data to your previous attribution model before making budget changes.
If You're New to LinkedIn Ads
- Start with AI-powered drafts for ad copy suggestions, then customize for your brand voice.
- Use auto-targeting for your first 30 days while learning which audiences respond.
- Set up conversion tracking immediately. LinkedIn's pixel and Conversions API are straightforward but need implementation before Data Driven Attribution works.
If You're Deciding Budget Allocation
Run a 30-day test with Data Driven Attribution enabled. Many teams discovered LinkedIn was undervalued because they weren't tracking assisted conversions. Compare your actual cost-per-MQL across channels with attribution data included before cutting or increasing spend.
Feature Comparison: Impact vs Effort
Common Questions
Q: Will my historical data change with the new naming?
No. LinkedIn relabels your "Campaign Group" as "Campaign," but it's the same object with the same performance history. If you use automated reporting, check your field mappings in February 2025, most platforms update within a few weeks. Custom scripts or SQL queries might need updates if they reference "campaign_group" explicitly.
Q: How does the Company Intelligence API differ from intent data providers like ZoomInfo?
LinkedIn tracks direct engagement with your brand (who clicked your ads, viewed your page, downloaded content). Intent providers like ZoomInfo or 6sense rely on third-party signals from content consumption across their network. LinkedIn's advantage is real-time, first-party engagement data. The API is only available through certified partners and costs approximately $2,000/month for up to 100,000 API calls.
Q: Does Data Driven Attribution work for offline conversions?
Yes, but you need to match LinkedIn's Click ID (appended to URLs automatically) to your CRM records. When someone converts offline (calls sales, fills out a paper form at a trade show, meets at an event) your CRM syncs the conversion back to LinkedIn via the Conversions API using the stored Click ID. Most CRMs like HubSpot, Salesforce, and Marketo have LinkedIn integrations built in. Custom implementations typically require 20-40 hours of dev work.
Q: Are Thought Leader Event Ads restricted to actual events?
Technically yes. LinkedIn verifies that the landing page has event schema markup (registration functionality, specific date). In practice, some marketers create "virtual event" landing pages for webinars, product demos, and video series that unlock on specific dates. As long as there's a calendar date and registration form, it typically qualifies. Regular content downloads or free trial signups don't qualify, use standard Sponsored Content for those.
Q: Is LinkedIn's auto-targeting better than manual audience selection?
For brand new campaigns with zero history, AI auto-targeting often outperforms first-attempt manual targeting. If you've been running LinkedIn ads for 6+ months, your own conversion data is more valuable. One test showed manual audiences built from 18 months of data had 34% lower cost-per-lead than AI suggestions. Best approach: use AI for the first 30-45 days, then refine manually based on which segments actually convert.
Making This Work
The nomenclature change removes friction for teams managing multiple platforms. AI targeting and attribution improvements help prove ROI to CFOs who care about pipeline, not impressions. Premium placement options like First Impression Ads work for high-value, time-sensitive campaigns but aren't cost-effective for evergreen lead gen.
The 39% budget allocation isn't accidental. It reflects LinkedIn's ability to reach decision-makers, track influence across long sales cycles, and prove pipeline contribution. For B2B marketers in 2025, the platforms that can demonstrate actual revenue impact will capture budget, regardless of surface-level metrics like CPC or CPM.
Next steps:
- Update team training on the new naming structure (especially if you're onboarding marketers from other platforms)
- Test one new feature per quarter rather than trying everything simultaneously
- Audit your attribution model, if you're only tracking last-touch, you're probably undervaluing top and mid-funnel channels
- Set up Dynamic UTMs for campaigns launching after January 2025
If you're managing LinkedIn alongside Google Ads, Meta, and other platforms, automated reporting tools can save hours of manual exports each week. Consolidating data from multiple sources makes it easier to see which platforms drive pipeline versus which ones just look good in their own dashboards.
