B2B Demand Generation vs Lead Generation: Why Cheap MQLs Starve Your Sales Team
Why low-cost MQLs rarely become closed revenue, and how to structure B2B Google Ads campaigns around buyer demand rather than form fills.
For a long time, product data was a maintenance job. Somebody set up the feed once, it worked, and nobody looked at it again for years.
You could get away with that because advertising was manual. You chose what to show up for. The product data only described what you sold.
Today the system uses your product data to decide whether you appear at all, and to whom. That moved the feed from maintenance into strategy, but at most companies the ownership didn't move with it.
Once the system handles bidding, targeting and placement, there isn't much left that can meaningfully change the outcome. What remains are the inputs, and product data is the most concrete of them.
If the title is accurate, the category right, the stock live and the attributes filled in, the system can find the real buyer. If any of that is missing, it guesses, and it guesses with your money.
Title structure. Brand, product name, then the most important attribute people filter by. Skip the keyword stuffing and write what a buyer would actually type.
Live stock. Paying to advertise an item you can't ship isn't an optimisation question. It's wasted money, and it costs you the customer's trust as well.
Exclusions. Not every product belongs in your ads. Filtering out items with thin margins or lots of returns is often worth more than any bid adjustment.
Passing margin through. If the system only sees revenue, it optimises for revenue. Show it the margin and it can optimise for profit.
Don't think of the feed as a file. Think of it as your instructions to the ad system. No system, however good, can make up for inaccurate instructions.
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More posts
Why low-cost MQLs rarely become closed revenue, and how to structure B2B Google Ads campaigns around buyer demand rather than form fills.
How to design high-trust B2B landing pages that qualify serious decision-makers and filter out non-buyers without destroying your conversion rate.
How venture-backed and bootstrapped B2B SaaS companies scale paid search pipeline profitably while keeping customer payback periods under twelve months.
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