LinkedIn Ads

By Yashasvi Saxena

A LinkedIn ads full funnel for ABM is easy to draw, but hard to run. Everyone draws the same triangle. Cold at the top, warm in the middle, hot at the bottom. Then they build it, and it falls apart in a specific way. The stages have no numbers attached, so nobody can say when... Continue reading Building a LinkedIn Ads Full-Funnel Structure for ABM

By Yashasvi Saxena

LinkedIn ABM reporting goes wrong in a way that is hard to see because the reports look fine. For a long stretch of our program, I was reporting on ads and calling it ABM reporting. Impressions, CTR, cost per click, best-performing creative – all accurate and well presented. Then someone asked how many of our... Continue reading An AI Workflow for LinkedIn ABM Reporting: Four Loops, One Source of Truth

By Yashasvi Saxena

LinkedIn ABM nurture campaigns fail for a reason not discussed enough: LinkedIn’s retargeting audiences are built around individual members, and your ABM funnel is built around accounts. The reason the account-level view matters so much is that the average B2B buying committee is 11 people, so the person who watched your video in week one... Continue reading LinkedIn ABM Nurture Campaigns: Sequencing the Funnel

By Yashasvi Saxena

Most guides on how to optimize LinkedIn ads with ChatGPT skip the one thing that matters. ChatGPT cannot see your ad account. By default, it has no idea what you spent, which ads are dying, or which companies clicked. So it guesses, and the advice sounds smart while being worth nothing. I learned this the... Continue reading How to Optimize LinkedIn Ads with ChatGPT

By Yashasvi Saxena

AI ABM segmentation is marketed as an algorithm that finds hidden clusters in your account list, and that promise is where most programs waste their first quarter. In reality, the segments that predict a meeting are not always the ones you can draw from a firmographic spreadsheet. Moreover, oversegmentation is a problem too. So the... Continue reading AI ABM Segmentation: Clustering Accounts at Scale

By Yashasvi Saxena

LinkedIn ads ABM scaling usually starts with someone adding 500 accounts to the target list and hoping. It does not work like that, and the benchmark data says so. In the ZenABM 2026 report covering 211 B2B companies, moving up a spend tier was not associated with better efficiency. If anything, it trended slightly negative.... Continue reading Scaling LinkedIn Ads Across More Target Accounts

By Yashasvi Saxena

LinkedIn ABM ROI calculation goes wrong for one reason more than any other. People divide this quarter’s revenue by this quarter’s spend. That is arithmetic, but it is not measurement. Revenue closing today came from spending six to eighteen months ago. So the sum makes growing programs look bad, and shrinking programs look good. Well,... Continue reading LinkedIn ABM ROI Calculation: The Definitive Guide

By Yashasvi Saxena

A LinkedIn ads data export for AI analysis is easy to get and easy to get wrong. You download the CSV, drop it into ChatGPT or Claude, ask what is working, and get a confident answer back. The answer is often wrong, and not because the AI is bad at math. It is wrong because... Continue reading LinkedIn Ads Data Export for AI Analysis: What Works and What Breaks

By Yashasvi Saxena

LinkedIn ads conversion campaigns for ABM get chosen by default because “conversions” are what everyone wants. Then the campaign underdelivers, the cost per result stays stubbornly high, and nobody can say why. The reason is usually arithmetic rather than creative. A conversion campaign asks LinkedIn’s algorithm to learn who converts, and learning needs conversion volume.... Continue reading LinkedIn Ads Conversion Campaigns for ABM: When to Use Them and When Not

By Yashasvi Saxena

LinkedIn ads influenced pipeline is the most useful number in ABM reporting and the easiest one to inflate. That is not a contradiction. It is the whole problem. The metric has no built-in floor. Nothing in the definition says how big a touch has to be before it counts. So one impression can qualify a... Continue reading How to Measure LinkedIn Ads Influenced Pipeline in ABM

By Yashasvi Saxena

AI can now optimize almost every part of an ABM campaign on LinkedIn: who you target, how you bid and budget, your creative, your wasted spend, your ad frequency, the follow-up after the click, and how you measure it all against pipeline. Most of this runs at two levels. LinkedIn’s own AI (Predictive Audiences, automated... Continue reading AI for ABM Campaign Optimization on LinkedIn

By Yashasvi Saxena

AI intent data sounds like it should solve account prioritization for you. Feed a model every signal you have, get back a ranked list, and work the top of it. That is the pitch, and it fails in a specific way that I want to name early. AI does not fix bad intent data. It... Continue reading Using AI with Intent Data for ABM Account Prioritization