Pull performance data across these 5 accounts for the last 4 months (accounts names/ids). Then tell me the account with the biggest performance drop, the one with the strongest performance, and any patterns you notice across accounts that I should pay attention to.

Summarize overall account performance for the last 30 days by identifying the top positive movers and the biggest declines across campaigns, ad groups, or keywords. Highlight the three strongest improvements based on metrics such as conversions, CPA, or ROAS, and the three biggest drops, such as conversion declines, CPA spikes, or CTR drops.

For each insight, show the current value versus the prior 30-day period, the percentage change, and a short explanation of why this movement matters. Based on these changes, recommend three priority checks I should review first, such as budget constraints, disapproved ads, landing page issues, or impression share losses, and provide one clear next action for each.

Scan all active ads for (account name/ID) over the last 30 days and list ads with the lowest CTR, lowest impressions, and highest relative drop in CTR versus the prior 30 days. For each ad show ad title, campaign, ad group, CTR, impressions, clicks, and change vs prior period. Provide 1–2 quick tests for each (headline tweak, CTA change, landing check) and a short A/B test suggestion (control vs test headline or description).

Analyze the highest-engagement ads in the account based on CTR, conversion rate, and impression-weighted performance over the last 30 days. Identify common patterns across top-performing creatives, such as phrasing style, emotional triggers, benefit framing, urgency cues, or keyword usage. Using these patterns, generate three stronger headline suggestions for the user’s top ads. For each suggested headline, include a short explanation of why it should perform better and propose a simple A/B test structure, specifying the control (current headline) and the test (new headline), along with the primary metric to monitor.

Analyze all search terms from (account name/ID) from the last 30 days and identify those that received impressions but resulted in zero clicks or zero conversions. Prioritize terms with the highest impression volume and spend, as these represent wasted visibility or budget.

For each term, show impressions, clicks, conversions, cost, and the campaign and ad group it belongs to. Based on relevance patterns, intent mismatches, or semantic drift, recommend whether each term should be added as a negative keyword and at what match type. Include a short rationale for each recommendation.

How much budget can I safely shift to top performers in the last 30 days for (account name/ID)? Identify high-performing campaigns with strong ROAS or conversion rates and unused budget capacity. Focus on which conditions qualify a campaign as a good candidate to receive more budget, such as high efficiency, low current budget utilization, impression share below 85%, and stable performance trends.

Also identify potential source campaigns that are overspending or showing poor returns (e.g., CPA significantly above average, pacing too aggressively). Emphasize the signals and thresholds that define safe reallocation in a summary so I can review which campaigns meet the criteria and why.