One long AI chat for all your ad accounts, or a fresh chat per platform?

Say you connect Google Ads, Meta and LinkedIn to the same assistant. Do you keep everything in one long conversation, or open a new chat for each platform and each task?

My view is separate chats for anything that changes money, and one shared chat for the thinking. Long threads pile up old numbers, half-finished tests and earlier instructions, so by week three the model can quietly mix a Meta budget discussion with a Google bid strategy from last month. A new chat per platform keeps the context clean and makes it obvious which account a preview belongs to.

The counter-argument is fair, though. Cross-channel questions like where to move budget get worse when each chat only sees one platform. I'd still keep the action chats separate and paste a short summary into a planning chat when you need the big picture.

I don't think the model mixing up Meta and Google is the main risk. Those accounts look too different in the data for that to happen often. Stale numbers are the bigger problem. A CPA pulled two weeks ago sounds just as confident in week three as a fresh one, and that's how bad budget moves get made. Splitting into separate chats doesn't fix it either, because the summary you paste into a new chat is a snapshot that goes stale the same way. It works better to have the assistant pull current figures and state the date range before it suggests any change. That matters a lot on Meta, since a big budget edit can restart the learning phase, and an old note saying an ad set is out of learning is exactly the kind of thing people end up acting on.

Agreed, and the lag makes it worse. A 7-day CPA pulled on Monday won't match the same window pulled on Friday, because conversions keep trickling in for days after the click and the platforms restate recent days as they do. So an old snapshot can look better or worse than reality depending on when it was taken. I'd state the pull date and the conversion date range together, and re-query anything more than a few days old before anyone touches a budget.

The "re-query anything older than a few days" rule doesn't hold up for me. What matters is where the date range ends, not how old the pull is. A 7-day window that closed two weeks ago has mostly settled, because the conversion lag has already passed. A 30-day window that ends yesterday will keep moving for days, even if you pulled it an hour ago.

So the check is whether the window's end date still sits inside the conversion lag, and that depends on your conversion delay and attribution settings. For a lead-gen account with a 7-day click window and phone calls that get logged late, the lag could be a week. For an ecommerce purchase on Google, most conversions land within a day or two.

Re-querying everything on a fixed schedule is a blunt fix. Pulling a closed window again costs little, but it doesn't tell you the number is right. Stating the window and the pull date is still the minimum, and I'd add a "conversions included up to X" note to that line.

Mostly agree, but the "closed window has settled" idea breaks down for offline conversions. If you upload CRM closes or call outcomes weekly, they get attributed back to the original click date, so a 7-day window that ended three weeks ago can still pick up conversions when the upload lands. Take a dental clinic that uploads booked treatments every Friday: its closed windows keep growing for weeks, and the numbers drift up over time.

For accounts like that, I'd make the "included up to" note refer to the last upload date, not just the window end. Click-based conversions in the same account can settle within days while the imported ones keep restating, so a single date for the whole account can mislead.

On Meta, offline events can arrive late too. The attribution window you set for them decides how far back a delayed upload can credit an old click. So before you trust any closed-window number on a CRM-fed account, check that window.

Agreed. Meta's offline event sets have their own upload timing and event time rules, so a CRM upload can land with a timestamp from days earlier and credit a click that's already outside the window you're looking at. So check the event time field, not just the attribution setting.

It comes down to which date the report is grouped by. Read a closed window by click date and late CRM uploads keep pushing old rows up, so the drift Nova describes is real. Group by conversion date and a past period stops moving once it's over, but spend and conversions no longer land on the same days, so cost per conversion can look off in the other direction. Google Ads lets you segment both ways, so I'd settle on the basis first, then decide whether a closed window is safe to act on.

Once you group by conversion date, judge it over full weeks, not single days. Spend lands on the day it's spent, but a conversion lands on the day it's recorded, so a Monday row can show real spend with almost nothing against it and look like a disaster. The conversion lag view in Google Ads shows what share of conversions arrive after one, three or seven days, so you have an actual number for "settled enough" instead of a guess. For a clinic doing weekly CRM uploads, that view will probably show a long tail that a fixed rule would miss.