34 posts
Google Ads Experiments (formerly Campaign Drafts and Experiments) lets you test a change on a portion of your budget before applying it to the whole campaign. For B2B SaaS founders, it's the right way to test bidding strategy changes, landing page variations, and keyword match type changes without risking your full spend.
If you're running Google Ads for B2B software and your conversion rate is mysteriously low despite decent CTR, job seekers are probably a significant part of your audience. Here's why it happens, how to diagnose it, and how to fix it in under 15 minutes.
Google Ads has predefined audience segments you can exclude from seeing your ads — including job seekers, students, and people searching for salaries. For B2B SaaS founders, failing to exclude these segments means a significant portion of your budget goes to people who will never buy. Here's exactly which audiences to exclude and where to find the settings.
Negative keywords tell Google which searches should never trigger your ads. For B2B SaaS, a solid negative keyword list blocks job seekers, free-tool hunters, students, and informational researchers from consuming your budget. Here's the exact list to start with.
Google Ads demographic targeting lets you adjust bids or exclude audiences by age, gender, parental status, and household income. For B2B SaaS founders, household income exclusions and age range optimisation are the two highest-leverage demographic adjustments — both are underused and both improve conversion rate.
Without conversion tracking, Google Ads optimises for clicks — not for the actions that matter. For B2B SaaS, properly configured conversion tracking tells Google which clicks became demo requests, trial signups, or form submissions — so the algorithm can find more people like that. Here's the exact setup process.
Orion is Ektie's AI Sales Director. He reviews Morgan's outreach every cycle, writes coaching notes, and escalates to the human when something needs a real decision. Here's what that actually looks like.
Morgan is Ektie's AI SDR. He runs on a 60-minute heartbeat cycle — 24 times a day, every day. Here's exactly what happens inside one full day: how many prospects he finds, what he researches, what he writes, and where the work goes.
AI sales agents that improve over time do so through structured memory — not model retraining. Here's how coaching, signals, and patterns accumulate into a persistent knowledge layer that makes every cycle better than the last.
A heartbeat agent is an AI agent that runs on a fixed recurring cycle — waking at set intervals, reloading its context, executing work, writing memory, and sleeping until the next cycle.