Platform AI keeps telling you to spend more. You need priorities you can trust.
Every platform's recommendation engine is designed with one goal: increase your spend on that platform. 'Expand your audience.' 'Try automated bidding.' 'Add more placements.' Alpomi's Playbook is different: it prioritises from reconciled performance and your goals. When it proposes pausing a campaign, your team reviews it. When it proposes shifting budget, you approve before anything changes in the ad platforms.
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Platform AI recommendations are optimised to increase platform spend — not your profit
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Without a data analyst, optimisation decisions are made weekly at best, or not at all
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Campaign performance changes are often missed for days before someone notices
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Budget reallocation between channels requires manually checking all platforms and doing the maths
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Alpomi analyses performance across connected platforms and turns patterns into Playbook priorities and proposed tasks. You see which campaigns need review, which creatives are fatiguing, and where a budget shift could help—without pulling exports yourself. Nothing applies until your team accepts it on the Action Board.
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Review campaigns manually each week. Notice one ad set is underperforming but not sure if it's temporary. Leave it running for another week. It's now wasted 2 weeks of budget.
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Playbook flags the ad set on day 3 with a proposed task to pause or refresh creative. You approve on the Action Board, then change the campaign in-platform. Budget saved.
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Budget decision: 50/50 split between Google and Meta because that's what you've always done.
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Playbook proposes shifting budget from Meta to Google — Meta ROAS has dropped, Google looks stronger on reconciled data. You approve the task, then change budgets in-platform.
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Faster optimisation cycles
Issues flagged in days, not weeks — so budget isn't wasted on underperforming campaigns
Priorities aligned to reconciled ROAS, not platform spend
Playbook items grounded in your performance—not the platform's revenue target
No data analyst required
Specific, actionable suggestions — not raw data to interpret yourself
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