300+ ready-to-use prompts for auditing and scaling your ad accounts with Claude

Performance Diagnosis & Monitoring

For my Google PMax campaigns, show me asset performance labels broken down by asset type — headlines, descriptions, images, videos — and flag every asset group where more than half the assets are rated Low or Pending.

Performance Diagnosis & Monitoring

Pull the last 30 days of Meta ad set performance and identify every ad set where frequency is above 3 AND CTR has declined week over week — these are the clearest signals of creative fatigue before ROAS tanks.

Performance Diagnosis & Monitoring

For all active Google Search campaigns, compare average CPC this week vs. the prior week — and for every campaign where CPC increased more than 15%, pull the impression share lost to rank to confirm if competitor pressure is the cause.

Performance Diagnosis & Monitoring

Pull Meta campaign performance broken down by day for the last 21 days — identify any specific days where CPM spiked more than 30% above the prior 7-day average, which would indicate an external auction event I should know about.

Performance Diagnosis & Monitoring

For every Meta ad set spending more than $100/day with a purchase conversion rate below 1%, pull the full funnel — impressions, clicks, landing page views, add-to-carts, initiated checkouts, purchases — and tell me the exact funnel stage where the drop-off is steepest.

Performance Diagnosis & Monitoring

Pull all active Google Shopping campaigns and for each one show me the ratio of spend on the top 20% of SKUs vs. the bottom 80% — if the bottom 80% is consuming more than 40% of budget with disproportionately low conversions, flag those SKUs for exclusion.

Performance Diagnosis & Monitoring

For my Meta campaigns using Campaign Budget Optimization, show me how budget is distributed across ad sets — and flag any CBO campaign where one ad set is receiving more than 70% of spend, as this may indicate the others are being starved.

Performance Diagnosis & Monitoring

Pull Google Ads Quality Score for all active keywords sorted ascending — and for every keyword with a Quality Score below 5, show me the expected CTR, ad relevance, and landing page experience scores separately so I know which component to fix first.

Performance Diagnosis & Monitoring

For all active Meta campaigns, pull the cost per initiated checkout alongside cost per purchase — and calculate the checkout-to-purchase drop-off rate per campaign to identify where payment friction or post-checkout issues are killing conversions.

Performance Diagnosis & Monitoring

For my Google Search campaigns, pull all broad match keywords alongside their triggered search terms from the last 30 days — highlight any keyword where more than 30% of spend came from search terms outside the core product or service category.

Performance Diagnosis & Monitoring

Pull Meta ad performance broken down by placement for the last 30 days — and show me which placements have a CPM more than 50% above the account average while generating below-average CTR, so I can evaluate excluding them.

Performance Diagnosis & Monitoring

For every Google campaign currently using Maximize Conversions or Maximize Conversion Value bidding, show me actual CPA vs. target CPA and actual ROAS vs. target ROAS — and flag any campaign where the gap has been widening over the last 3 weeks.

Performance Diagnosis & Monitoring

Pull all active Meta ad sets and show me the estimated audience size for each — flag every ad set with an audience below 500,000 as these are at risk of delivery issues, high frequency, and inflated CPMs due to limited scale.

Performance Diagnosis & Monitoring

For my Google Shopping campaigns, pull search terms triggering product ads and identify any terms that contain words like "free," "cheap," "DIY," "how to," or "used" — these are wrong-intent queries consuming budget that should be negated immediately.

Performance Diagnosis & Monitoring

For all active Meta campaigns, pull impressions, link clicks, landing page views, add-to-carts, initiated checkouts, and purchases in a single view — then calculate the drop-off percentage at each stage and identify which funnel transition has the largest loss across the account.

Performance Diagnosis & Monitoring

Pull Meta campaign data and calculate the ratio of add-to-cart to purchase for each campaign — then compare prospecting vs. retargeting campaigns separately, because a high ATC-to-purchase drop-off in retargeting specifically indicates a checkout friction problem rather than an audience or creative issue.

Performance Diagnosis & Monitoring

For every active Google Search campaign, pull average CPC alongside conversion rate — and calculate cost per conversion at the keyword level to identify the exact keywords where click costs are efficient but something on the post-click journey is breaking down.

Performance Diagnosis & Monitoring

Pull Meta ad set performance and calculate cost per landing page view vs. cost per purchase for each — a large multiplier between these two numbers indicates the landing page itself is the constraint, not the ad or the audience.

Performance Diagnosis & Monitoring

For my Google Shopping campaigns, pull click-through rate at the product level alongside conversion rate — and identify any products with above-average CTR but below-average conversion rate, which means the product listing is attracting clicks but the product page or price is failing to close.

Performance Diagnosis & Monitoring

Pull Meta campaign data and filter for campaigns where initiated checkout rate is above 5% but purchase rate is below 2% — this specific funnel signature points to payment friction, shipping cost reveal, or trust issues at the final purchase step rather than creative or audience problems.

Performance Diagnosis & Monitoring

For all active Google campaigns, pull the conversion lag report — show me the average number of days between first click and conversion — so I can verify that my attribution windows are wide enough to capture the full conversion cycle for my product category.

Performance Diagnosis & Monitoring

Pull Meta ad performance and calculate the ratio of video views to link clicks for each video ad — a high view-to-click ratio means the video is being watched but not prompting action, which is a CTA or offer clarity problem rather than a hook or attention problem.

Performance Diagnosis & Monitoring

For my Meta retargeting campaigns, pull the audience breakdown between website visitors, add-to-cart abandoners, and initiated checkout abandoners — and show me the CPA for each segment separately, because these three audiences have fundamentally different intent levels and should not be evaluated together.

Performance Diagnosis & Monitoring

Pull Google Ads conversion data and break it down by time of day and day of week — identify the hours and days with the highest conversion rate so I can evaluate whether dayparting adjustments or bid modifiers by time would improve overall campaign efficiency.

Performance Diagnosis & Monitoring

For all active Meta campaigns, pull the view content event rate as a percentage of landing page views — a low view content rate means users are bouncing before even seeing the product, which is a landing page load speed or relevance problem that no amount of ad optimization will fix.

Performance Diagnosis & Monitoring

Pull Meta campaign data and compare the add-to-cart rate for traffic coming from prospecting campaigns vs. retargeting campaigns — if prospecting is driving a high ATC rate, it validates the product-market fit and indicates the issue is in the lower funnel or the retargeting pool size.

Performance Diagnosis & Monitoring

For my Google PMax campaigns, pull conversion data broken down by device — and compare mobile conversion rate to desktop conversion rate — because PMax frequently over-indexes on mobile traffic and a weak mobile conversion rate is one of the primary drivers of PMax underperformance.

Performance Diagnosis & Monitoring

Pull Meta ad performance and calculate cost per add-to-cart vs. cost per purchase for each active campaign — then identify the campaigns with the largest gap between these two numbers, as this gap represents the checkout abandonment problem that retargeting or email flows need to solve.

Performance Diagnosis & Monitoring

For all active Google Search campaigns, pull the assisted conversion data alongside last-click conversions — and identify keywords that generate a high volume of assisted conversions but few last-click conversions, as these keywords are often under-valued by last-click attribution and at risk of being incorrectly paused.

Performance Diagnosis & Monitoring

Pull Meta campaign data and segment purchases by new customer vs. returning customer if pixel data supports it — a campaign with a high returning customer purchase rate may be reporting strong ROAS while actually cannibalizing organic repeat purchases rather than acquiring new customers.

Performance Diagnosis & Monitoring

For my Meta account, pull the landing page view rate as a percentage of link clicks for each campaign — and flag any campaign where this rate drops below 70%, because more than 30% of paid clicks not reaching the landing page represents a significant technical or redirect problem causing invisible budget waste.

Performance Diagnosis & Monitoring

Pull Google Shopping performance and calculate the impression-to-click rate at the product level — products with very low impression-to-click rates are either priced uncompetitively relative to what's visible in the Shopping carousel or have weak product titles and images that fail to earn clicks even when shown.

Performance Diagnosis & Monitoring

For my Meta account, pull initiated checkout as a percentage of add-to-cart for each campaign — this specific ratio isolates the shopping cart page as a conversion barrier, and a rate below 50% indicates that the cart page design, shipping cost, or account creation requirement is killing purchases.

Performance Diagnosis & Monitoring

Pull Meta performance data and show me the number of purchases attributed to view-through vs. click-through for each campaign — a campaign that attributes the majority of its conversions to view-through is likely inflating its reported ROAS significantly and may be receiving credit for conversions that would have happened organically.

Performance Diagnosis & Monitoring

Pull daily Meta account-level CPM for the last 60 days and plot the trend — then identify any sustained upward movement over a 14-day period that would indicate the account is entering a competitive pricing environment requiring creative refresh or audience expansion.

Performance Diagnosis & Monitoring

For all active Google Search campaigns, pull average CPC on a weekly basis for the last 8 weeks — and identify any campaign where CPC has been rising for 3 or more consecutive weeks, as sustained CPC growth without corresponding conversion rate improvement directly erodes profitability.

Performance Diagnosis & Monitoring

Pull Meta campaign performance week over week for the last 6 weeks and calculate the percentage change in ROAS, CPM, and CTR for each — then tell me which metric is moving in the wrong direction most consistently, as the most consistent negative trend reveals the primary account-level problem.

Performance Diagnosis & Monitoring

For all active Meta ad sets, pull frequency on a weekly basis for the last 30 days — and flag any ad set where frequency has grown by more than 1 point per week, as rapidly increasing frequency is the leading indicator of audience exhaustion 2-3 weeks before CTR and ROAS visibly deteriorate.

Performance Diagnosis & Monitoring

Pull Google Ads impression share on a weekly basis for my top 3 Search campaigns over the last 8 weeks — and flag any campaign where impression share has been declining while budget and bids remained unchanged, as organic IS decline indicates growing competitor activity.

Performance Diagnosis & Monitoring

For my Meta account, pull the new creative launch rate over the last 90 days — specifically the number of new ads started per month — because accounts that are not launching at least 4-6 new creative tests per month are structurally at risk of fatigue-driven performance decline.

Performance Diagnosis & Monitoring

Pull all active Google campaigns and show me the 7-day conversion volume vs. the 30-day weekly average for each — any campaign where last week's conversions are more than 20% below the 30-day weekly average is showing an early decline signal worth investigating before it becomes a larger problem.

Performance Diagnosis & Monitoring

For my Meta account, pull the account-level cost per purchase trend by month for the last 6 months — and identify whether CPA is trending upward, downward, or flat over this period, as the direction of this trend determines whether the account is improving in efficiency or degrading over time.

Performance Diagnosis & Monitoring

Pull Google Search term report volume trends by week for the last 8 weeks — and identify any high-converting search term category that is showing declining search volume, as organic demand contraction at the category level affects all campaigns and cannot be solved through bid or budget changes alone.

Performance Diagnosis & Monitoring

For my Meta account, pull the performance comparison between the last 7 days and the prior 7-day period at the campaign level — and for every campaign that declined in ROAS by more than 15%, pull CPM, CTR, and conversion rate changes in the same period to isolate which of the three drove the decline.

Performance Diagnosis & Monitoring

Pull total spend and total purchase revenue from both Meta Ads and Google Ads for the last 30 days — then calculate the blended ROAS across both platforms and compare it to each platform individually to understand whether combined attribution is masking underperformance on either channel.

Performance Diagnosis & Monitoring

For my Google branded Search campaigns and Meta retargeting campaigns running simultaneously, pull their combined spend and conversion volume — then evaluate whether both are necessary or whether one is cannibalizing the other's attribution for users who were already intent-driven enough to convert.

Performance Diagnosis & Monitoring

Pull Meta prospecting campaign performance alongside Google Search impression share data — if Meta prospecting ROAS is declining while Google branded Search volume is increasing, this could indicate Meta is successfully building brand awareness that Google is then capturing, making Meta's direct ROAS an underrepresentation of its true value.

Performance Diagnosis & Monitoring

Pull the top 10 converting keywords from Google Search and cross-reference them with the ad copy and landing page messaging being used in Meta campaigns — significant messaging inconsistency between what converts in Search and what's being tested in Meta is a missed optimization opportunity.

Performance Diagnosis & Monitoring

Pull total new customer acquisition cost from Meta prospecting campaigns and compare it to the new customer CPA from Google Shopping campaigns — understanding which platform acquires new customers more efficiently informs where to invest incremental acquisition budget.

Performance Diagnosis & Monitoring

For my account, pull the day-of-week conversion distribution from both Meta and Google Ads — and identify if the peak conversion days differ significantly between platforms, which would justify different budget pacing strategies for each rather than applying the same schedule to both.

Performance Diagnosis & Monitoring

Pull device-level performance from both Meta Ads and Google Ads — and compare mobile conversion rates across the two platforms — because a strong mobile conversion rate on Meta but weak on Google often points to a Google mobile landing page experience problem rather than a general mobile audience quality issue.

Performance Diagnosis & Monitoring

Pull the monthly spend trend for both Meta and Google over the last 6 months alongside combined revenue — and calculate whether the incremental ROAS of additional spend has been increasing or decreasing over time, which reveals whether the account is in a growth phase or approaching a saturation ceiling.

Performance Diagnosis & Monitoring

For my Google Search campaigns targeting brand keywords and my Meta retargeting campaigns running simultaneously, pull the audience overlap and calculate the combined frequency a single user might experience across both platforms — excessive combined frequency is an invisible cost driver that doesn't appear in either platform's reporting in isolation.

Performance Diagnosis & Monitoring

Pull the top 5 revenue-generating campaigns from Meta and the top 5 from Google for the last 30 days — then compare their CPA and ROAS side by side to identify which platform is delivering superior efficiency at scale, and what the budget reallocation opportunity looks like if the gap is material.

Performance Diagnosis & Monitoring

Pull the total number of active campaigns, ad sets, and ads in my Meta account — and flag if the ratio of ad sets to campaigns or ads to ad sets is disproportionately high, because account complexity beyond a manageable structure dilutes algorithmic learning signals and makes performance analysis harder.

Performance Diagnosis & Monitoring

For my Meta account, calculate the creative refresh rate over the last 90 days — the number of new ads launched divided by the number of active ad sets — because an account that is not consistently testing new creative angles is running on borrowed time, with fatigue as the inevitable outcome.

Performance Diagnosis & Monitoring

Pull all active Google and Meta campaigns and calculate the percentage of total budget going to brand campaigns vs. non-brand campaigns — and assess whether the brand vs. non-brand investment ratio is consistent with the growth stage of the business (heavy brand spend at early stage is often a misallocation).

Performance Diagnosis & Monitoring

For my Meta account, pull the account-level purchase conversion rate (purchases divided by link clicks) for each month over the last 6 months — and identify whether the trend is improving, stable, or declining, as this single metric is the clearest indicator of whether the overall account is getting more or less efficient over time.

Performance Diagnosis & Monitoring

Pull all active Google campaigns and calculate the account-wide impression share — then compare it to the impression share 90 days ago to determine whether the account is gaining or losing ground in the auction environment over time.

Performance Diagnosis & Monitoring

For my Meta account, pull the total spend on prospecting campaigns vs. retargeting campaigns as a percentage of total account spend — then evaluate whether this ratio is sustainable given the current size of my retargeting audience, because a high retargeting spend share on a small audience leads to diminishing returns and frequency inflation.

Performance Diagnosis & Monitoring

Pull all Google campaigns that were modified in the last 14 days — show me what changed (budget, bids, targeting, structure) — and flag any account that has had more than 5 structural changes in a 14-day period, as excessive changes prevent smart bidding from stabilizing and make it impossible to isolate cause and effect in performance data.

Performance Diagnosis & Monitoring

For my Meta account, pull the cost per purchase trend over the last 12 months broken down by quarter — and identify whether there are seasonal patterns in CPA that should inform budget planning and creative strategy for upcoming quarters.

Performance Diagnosis & Monitoring

Pull all Google and Meta campaigns that are currently active but have generated zero conversions in the last 14 days despite spending meaningful budget — list them with their total spend in that period so I can make informed decisions about whether to pause, restructure, or continue testing each one.

Performance Diagnosis & Monitoring

For my Meta account, pull the ratio of spend going to video creatives vs. static image creatives — and compare the CPA of each format — because an account that has not shifted toward video in an environment where Meta's algorithm increasingly favors it may be paying a structural CPM premium for refusing to adapt creative formats.

Performance Diagnosis & Monitoring

Pull Meta campaign data and compare the purchases reported in Meta Ads Manager to the purchases recorded in my Shopify or analytics platform for the same period — and calculate the discrepancy percentage, because a gap above 20% indicates a tracking implementation problem that is corrupting every optimization decision downstream.

Performance Diagnosis & Monitoring

For all active Google campaigns, pull conversion data from Google Ads alongside the same conversion events from Google Analytics — and identify any discrepancies that indicate tracking gaps or double-counting across platforms.

Performance Diagnosis & Monitoring

Pull all active Meta campaigns and show me the attribution window breakdown — purchases attributed to 1-day click, 7-day click, and 1-day view — separately for each campaign — so I can understand how much of reported ROAS is driven by direct click-through purchase vs. view-through attribution that may be crediting Meta for conversions driven by other channels.

Performance Diagnosis & Monitoring

For my Google Ads account, pull the conversion actions that are set as primary conversion goals and verify that each one fires only once per purchase — duplicate conversion firing is one of the most common measurement errors and can inflate reported conversion volume by 30-50% without any visible signal.

Performance Diagnosis & Monitoring

Pull all active Meta campaigns and calculate the ratio of reported Meta purchases to the total purchases recorded in my analytics platform over the same period — if the sum of Meta and Google reported conversions exceeds total actual purchases by more than 30%, cross-channel attribution overlap is inflating performance reporting across both platforms.

Performance Diagnosis & Monitoring

For my Google campaigns, pull click and conversion data alongside Google Analytics session data for the same period — and identify any campaigns where Google Ads reports significantly more conversions than Google Analytics records, which often indicates cross-device attribution in Google Ads capturing conversions that Analytics cannot track across devices.

Performance Diagnosis & Monitoring

Pull Meta view-through conversions as a percentage of total reported conversions for each active campaign — any campaign where view-through attribution accounts for more than 30% of total reported purchases is significantly over-stating its true performance, since view-through credit is assigned to users who saw but never clicked the ad.

Performance Diagnosis & Monitoring

For all active Google campaigns, pull the conversion delay report — and identify the percentage of conversions that are attributed within 1 day, within 7 days, and beyond 7 days — because if my conversion cycle extends beyond 7 days, my 7-day performance windows are systematically under-reporting true campaign impact.

Performance Diagnosis & Monitoring

Pull Meta campaign performance using different attribution windows — 1-day click only vs. 7-day click + 1-day view — and calculate how much ROAS changes between the two settings — the delta between these two windows reveals the extent to which current ROAS reporting is inflated by view-through and extended-window attribution.

Performance Diagnosis & Monitoring

For my Google Ads account, pull all conversion actions and identify which ones have smart bidding optimization enabled — then verify that only the primary purchase conversion action is informing smart bidding, because including micro-conversions in smart bidding signals leads the algorithm to optimize for engagement rather than revenue.

Budget, Bidding & Profitability

Pull all active Google Display Network campaigns and show me conversion rate and CPA compared to my Search campaigns — Display placements regularly consume budget at dramatically worse efficiency and often justify exclusion from performance campaigns.

Budget, Bidding & Profitability

For my Meta CBO campaigns, pull the spend distribution across ad sets — and for any CBO campaign where one ad set is capturing more than 75% of budget while others have comparable or better efficiency metrics, flag this as algorithmic over-concentration.

Budget, Bidding & Profitability

Pull all active Google keywords on broad match and show me the diversity of search terms they are triggering — specifically the percentage of triggered terms that are outside my core product category, as broad match without proper negatives is often the largest source of irrelevant spend.

Budget, Bidding & Profitability

For my Meta account, pull all active ads that have been running more than 60 days — and flag any where frequency has exceeded 6 in the last 14 days, because at that frequency level CPM begins rising as the algorithm exhausts the audience and starts re-serving to the same users.

Budget, Bidding & Profitability

Pull all Google Shopping product groups and identify SKUs that have received spend exceeding 2x my average order value with zero purchases — these are the clearest kill candidates and the first place to cut in any Shopping campaign audit.

Budget, Bidding & Profitability

For my Meta account, pull all ad sets where the targeting audience size is below 200,000 — these narrow audiences hit saturation quickly, drive up frequency and CPM, and often explain why scaling attempts on these ad sets consistently fail.

Budget, Bidding & Profitability

Pull all active Google campaigns and show me the actual daily spend vs. the daily budget cap for the last 14 days — any campaign consistently hitting its budget cap before end of day is leaving eligible conversions on the table and should be reviewed for budget increase or bid efficiency improvements.

Budget, Bidding & Profitability

For my Meta account, pull all active ads and calculate the percentage of total account spend that goes to the top 3 ads — if more than 60% of spend is concentrated in 3 creatives with no new tests running, the account is dangerously dependent on a small creative base that could fatigue at any time.

Budget, Bidding & Profitability

Pull all Google Search campaigns running on Search Partners in addition to Google Search — show me the conversion rate and CPA from Search Partners separately, as Search Partners frequently underperform and their traffic often warrants exclusion from performance-focused campaigns.

Budget, Bidding & Profitability

For my Meta account, pull all campaigns where the attribution window is set to 1-day click — and compare their purchase numbers to campaigns using 7-day click attribution to identify if my attribution settings are artificially suppressing or inflating reported ROAS.

Budget, Bidding & Profitability

Pull all active Google ad groups with only one keyword — single keyword ad groups created without deliberate strategy fragment Quality Score signals, create structural complexity, and often deliver worse performance than properly consolidated thematic ad groups.

Budget, Bidding & Profitability

For my Meta account, pull all ad sets running with both custom audiences and interest targeting layered together — layering these reduces audience size and can create overly narrow targeting that inflates CPMs without proportional conversion improvement.

Budget, Bidding & Profitability

Pull all Google campaigns using Maximize Clicks as their bidding strategy — this strategy optimizes for cheap clicks regardless of conversion intent, and any campaign on this strategy that has accumulated enough conversion data should be evaluated for migration to a conversion-based bid strategy.

Budget, Bidding & Profitability

For my Meta account, pull all active campaigns that have no UTM parameters on their destination URLs — these campaigns are generating spend and conversions that will appear as direct or unattributed traffic in analytics, making it impossible to accurately measure their true contribution to revenue.

Budget, Bidding & Profitability

For all active Google Search campaigns, pull impression share lost to rank alongside current average CPC — and for any campaign losing more than 30% of impression share to rank, calculate the estimated bid increase needed to recover that share based on current Quality Scores.

Budget, Bidding & Profitability

Pull all active Meta campaigns and identify which ones have a cost cap or bid cap that is causing the ad set to spend less than 70% of its daily budget — an aggressive cost cap that suppresses delivery below 70% is costing more in missed volume than it saves in efficiency.

Budget, Bidding & Profitability

For all active Google campaigns using Target ROAS bidding, pull the actual ROAS vs. target ROAS for the last 30 days — and calculate whether the target is set so conservatively that it is leaving significant impression share unrealized, or so aggressively that the campaign is chronically underdelivering.

Budget, Bidding & Profitability

Pull my Meta account's total spend distribution across all active campaigns — and calculate what percentage of total spend is going to prospecting vs. retargeting vs. retention campaigns — because a healthy account typically requires a specific investment ratio between acquisition and retention to sustain growth.

Budget, Bidding & Profitability

For all active Google campaigns, pull actual daily spend vs. daily budget for the last 14 days — and identify any campaign that hits its daily budget cap before 6pm consistently, as these campaigns are missing evening conversion opportunities that may have higher purchase intent in my category.

Budget, Bidding & Profitability

Pull all active Meta CBO campaigns and show me the minimum and maximum spend limits set per ad set within each — and flag any CBO campaign where minimum spend limits are set so high that they remove meaningful budget allocation flexibility from the algorithm.

Budget, Bidding & Profitability

For all active Google Shopping campaigns, pull budget utilization rate for the last 14 days — and flag any campaign consistently under 80% utilization despite having high-converting SKUs, as under-utilization in a performing Shopping campaign often indicates that the product feed quality is limiting auction eligibility rather than the budget itself.

Budget, Bidding & Profitability

Pull all active Meta ad sets and calculate the revenue generated per dollar of daily budget for each — then rank them and identify the top 20% by this efficiency metric, because these are the ad sets where reallocation from the bottom 20% would have the highest marginal impact on account revenue.

Budget, Bidding & Profitability

For all active Google campaigns using Maximize Conversions without a target CPA set, pull the actual CPA trend over the last 30 days — Maximize Conversions without a target will optimize for volume at any cost, and a rising CPA trend on this strategy means the algorithm is exhausting cheap conversion opportunities and moving into progressively more expensive inventory.

Budget, Bidding & Profitability

Pull all active Meta campaigns and identify any where the daily budget is below the amount needed to generate at least 50 conversion events per week at the current CPA — campaigns spending below this threshold do not provide enough data for the algorithm to optimize effectively.

Budget, Bidding & Profitability

Pull all active Meta campaigns and calculate the contribution margin ROAS for each — meaning ROAS adjusted for product margin rather than just revenue — so I can identify which campaigns are truly profitable after cost of goods rather than just generating top-line revenue.

Budget, Bidding & Profitability

For my Google Shopping campaigns, pull revenue and conversion volume broken down by product margin tier — and identify if the campaign is correctly concentrating spend on high-margin SKUs or inadvertently subsidizing spend on low-margin products with poor profitability.

Budget, Bidding & Profitability

Pull Meta campaign data and calculate new customer acquisition cost separately from returning customer CPA — because an account where the majority of purchases come from returning customers is not growing its customer base efficiently and is at risk of revenue plateau.

Budget, Bidding & Profitability

For all active Google campaigns, pull conversion value per click for each campaign — and compare it to the average CPC — to calculate the gross revenue multiple generated per dollar of click spend, then rank campaigns by this metric to identify the highest revenue-generating units per dollar invested.

Budget, Bidding & Profitability

Pull Meta ad performance and calculate average order value separately for prospecting-sourced purchases vs. retargeting-sourced purchases — because a significant AOV gap between the two indicates that the quality of customers acquired through cold prospecting differs from returning customers and should inform lifetime value projections.

Budget, Bidding & Profitability

For my Google Shopping campaigns, pull revenue by product category and compare it against the spend allocation by category — and identify categories where the revenue-to-spend ratio significantly exceeds others, as these are the categories where additional spend would generate the highest incremental return.

Budget, Bidding & Profitability

Pull all active Meta campaigns and calculate the 30-day revenue-to-budget ratio for each campaign — then identify the marginal return on the last 10% of spend in each campaign, because the highest-spending campaigns frequently show diminishing returns at the margin even when aggregate ROAS looks acceptable.

Budget, Bidding & Profitability

For all active Google campaigns, pull conversion value and cost data by day of week — and calculate ROAS by day to determine if there are specific days where incremental budget investment would generate disproportionately higher returns due to elevated purchase intent.

Budget, Bidding & Profitability

Pull Meta campaign data and segment it by the customer's estimated purchase order (first purchase vs. second+ purchase) if supported — because the true profitability of an acquisition campaign can only be assessed against the full predicted customer lifetime value, not just the first-order revenue reported in the campaign.

Budget, Bidding & Profitability

For my Google and Meta campaigns combined, calculate the total cost of acquiring 100 new customers in the last 30 days — then compare this against the estimated 90-day revenue from those customers — to determine whether current paid acquisition economics are profitable at a unit economics level or whether the business is losing money on each new customer acquired.

Budget, Bidding & Profitability

Pull all active Meta campaigns and identify any that have been in a "learning limited" delivery status — then diagnose the specific reason for each (audience too small, budget too low, too many ad sets, bid too restrictive) so I can address the root cause rather than guessing at structural fixes.

Budget, Bidding & Profitability

For all active Google campaigns, pull the change history for the last 30 days and identify any weeks where more than 3 significant changes were made simultaneously — then correlate those change-heavy weeks with performance fluctuations, to determine whether over-management is a contributing factor to performance instability.

Budget, Bidding & Profitability

Pull all Meta campaigns currently using manual bidding (cost caps or bid caps) — and compare their delivery rate, CPM, and CPA to equivalent campaigns using automatic bidding — to determine whether manual bid controls are improving efficiency or suppressing delivery without a corresponding CPA benefit.

Budget, Bidding & Profitability

For my Google Ads account, pull the automated recommendations that have been applied in the last 90 days — and for each one calculate the performance impact in the 14 days after application vs. the 14 days before, to determine whether Google's automated recommendations are net positive or net negative for my account.

Budget, Bidding & Profitability

Pull all active Meta ad sets and show me which ones have automatic placements enabled vs. manual placement selection — then compare CPM and CPA between the two approaches, because the conventional wisdom that automatic placements always win is not universally true and is worth validating with your own account data.

Budget, Bidding & Profitability

For all active Google campaigns, pull the bid strategy performance report and show me the percentage of time each smart bidding campaign is in the target range, above target, and below target — campaigns spending significant time above CPA target or below ROAS target are not being correctly managed by their current bidding strategy.

Budget, Bidding & Profitability

Pull all Meta campaigns created in the last 90 days and calculate the average time between campaign creation and first purchase — then identify any structural patterns (campaign objective, audience type, creative format) that are associated with faster or slower time-to-first-conversion.

Budget, Bidding & Profitability

For my Google Ads account, pull all shared negative keyword lists and identify which campaigns they are applied to — then flag any campaign that is not covered by any negative keyword list, as these campaigns are operating without negative keyword protection and are likely generating wasted spend on irrelevant queries.

Budget, Bidding & Profitability

Pull all active Meta campaigns and calculate the average number of days between creative refreshes for ad sets that are currently active — then compare this cadence to the frequency growth rate across those ad sets, to determine whether the creative refresh rate is keeping pace with audience fatigue or falling behind.

Budget, Bidding & Profitability

For all active Google campaigns, pull the budget utilization rate alongside the conversion volume for the last 14 days — and identify any campaign where budget utilization is above 95% but conversion volume is flat, which indicates the campaign is spending its full budget but running into conversion ceiling issues that a budget increase alone will not solve.

Creative & Testing

Pull Meta ad performance broken down by creative format and by placement — and show me which format performs best on Feed vs. Reels vs. Stories, because the winning format on one placement may be the worst on another.

Creative & Testing

For all active Meta ads with spend above a meaningful threshold, calculate the ratio of outbound clicks to link clicks — a large gap here indicates users are clicking but not reaching the landing page, which is a tracking or redirect issue.

Creative & Testing

Pull all Meta ads currently active and show me which ones have not had a creative refresh (new ad launched in the same ad set) in the last 30 days — these are the highest fatigue risk in the account regardless of current metrics.

Creative & Testing

For my Google PMax campaigns, pull all asset groups and show me the count of Best, Good, Low, and Pending assets per group — and flag any asset group where there are zero Best-rated assets, as these need immediate creative investment.

Creative & Testing

Pull the top 10 Meta ads by purchase volume and the bottom 10 by purchase volume (with equal or higher spend) — then compare their CPM, CTR, and hook rate side by side to identify what structural differences separate winners from underperformers.

Creative & Testing

For every active Meta carousel ad, pull CTR alongside cost per purchase — and compare it to the single image and video ads in the same ad set to determine whether the carousel format is justified by its conversion performance.

Creative & Testing

Pull Meta video ad performance and calculate the percentage of users who reach the 25%, 50%, 75%, and 100% completion marks — then identify which video has the steepest drop-off and at which point in the video the audience disengages.

Creative & Testing

For all active Google Search ads, pull CTR by ad position — and identify any RSA where CTR drops significantly when shown in position 2 or lower, which means the ad copy is not strong enough to compete without the top position advantage.

Creative & Testing

Pull all Meta ads that launched in the last 14 days and have more than 1,000 impressions — sort by CTR descending and identify the early signals of which new creatives are gaining traction before they've consumed significant budget.

Creative & Testing

For my Meta account, pull the average CPM for video ads vs. image ads over the last 30 days — and calculate which format gives me more impressions per dollar spent, because creative format affects auction costs beyond just CTR.

Creative & Testing

Pull all active Meta ads and identify which ones have the highest ratio of post engagement (likes, comments, shares) relative to reach — high organic engagement on a paid ad is a strong signal of creative resonance worth scaling.

Creative & Testing

For every Meta ad set with more than 5 active ads running simultaneously, pull individual ad spend distribution — and flag any ad set where one ad is receiving more than 60% of spend and all others are being starved, which creates false performance signals.

Creative & Testing

Pull the Google RSA pinning configuration across all active Search campaigns — and flag any RSA where more than 2 headlines are pinned to fixed positions, as heavy pinning reduces the machine learning optimization surface and typically lowers Quality Score.

Creative & Testing

For all Meta ads that were paused in the last 30 days, pull their final 7-day performance before pausing — and identify if any were paused while still showing positive ROAS, which would indicate premature pausing of a recovering creative.

Creative & Testing

Pull all active Meta ads and group them by the primary offer type — percentage discount, dollar amount off, free shipping, bundle, free trial, or no explicit offer — then calculate average CPA for each offer type to identify which promotional mechanic drives the most cost-efficient conversions in my account.

Creative & Testing

For all Meta video ads active in the last 60 days, pull hook rate, hold rate, and CTR together — then map each ad into one of four diagnostic quadrants: strong hook + strong hold, strong hook + weak hold, weak hook + any hold, strong hold + weak CTA — and identify which quadrant has the most ads so I know where to focus creative fixes.

Creative & Testing

Pull all Meta ads that use a question in the primary text or headline — and compare their average CTR and CPA to ads that use a declarative statement — to determine whether question-based copy outperforms statement-based copy in my specific category and audience.

Creative & Testing

For all active Meta campaigns, pull the performance of ads featuring a real person (UGC, influencer, spokesperson) vs. ads that are product-only or graphic-design led — and compare their CPM, CTR, and cost per purchase to determine which creative direction earns more trust and conversion in my category.

Creative & Testing

Pull all Meta ads created in the last 90 days and categorize them by the primary pain point or aspiration they address — then rank each category by average ROAS to identify which emotional angle resonates most strongly with my audience at a conversion level, not just an engagement level.

Creative & Testing

For all active Google RSA ads, pull the top 3 headline combinations being served most frequently by Google's algorithm — these are the combinations Google has found to drive the highest CTR — and compare them to the combinations I intended to lead with to identify if my intended messaging is being surfaced or suppressed.

Creative & Testing

Pull all Meta ads and group them by copy length — short (under 50 words), medium (50-150 words), and long (150+ words) — then calculate the average CTR and CPA for each group to determine whether my audience responds better to concise or detailed ad copy.

Creative & Testing

For all active Meta campaigns, pull the performance of ads that lead with a specific product benefit vs. ads that lead with a social proof element (reviews, user counts, press mentions) — and compare CPA to determine which trust signal is more effective at driving purchase decisions in my category.

Creative & Testing

Pull all Meta ads that include a specific urgency element — limited time, low stock, sale ending — and compare their CTR and purchase conversion rate to ads without urgency messaging — to determine whether scarcity and urgency tactics are lifting conversions or being tuned out by my audience.

Creative & Testing

For all active Google RSA campaigns, pull the asset performance labels for headlines containing a price or promotion vs. headlines that are benefit-focused — and compare their performance ratings to determine whether Google's algorithm favors promotional or benefit messaging for my campaign type.

Creative & Testing

Pull all Meta A/B tests run in the last 90 days and show me the sample size, test duration, and confidence level reached for each — and flag any test that was ended before reaching statistical significance, because decisions made on inconclusive tests create false learnings that compound into poor strategy.

Creative & Testing

For all active Meta campaigns where multiple creatives are running in the same ad set, pull the spend distribution across creatives — and flag any ad set where the spend is not distributed evenly enough to generate comparable data across all variants within 14 days, because unequal spend distribution prevents valid creative comparison.

Creative & Testing

Pull all Google Ads experiments currently running or completed in the last 90 days — show me what was being tested, what the result was, and whether the winning variant was applied — because an account that runs experiments but doesn't implement winning variants is generating data it's not using.

Creative & Testing

For my Meta account, pull all ad sets that have more than 4 active ads running simultaneously — and flag any where the creative variants being tested are too similar to each other (same format, same offer, same hook) — because testing slight variations of a losing concept is a waste of testing budget that should be redirected to testing fundamentally different angles.

Creative & Testing

Pull all Google RSA experiments and show me whether the current headline and description combinations being tested differ meaningfully in their value proposition — RSA tests that only vary punctuation or word order are not generating strategically useful learnings.

Creative & Testing

For my Meta account, pull the win rate of creative tests over the last 90 days — meaning the percentage of tests where the challenger beat the control — and if the win rate is below 20%, this signals that the testing hypotheses are not differentiated enough and the creative strategy needs a more radical exploration of new angles.

Creative & Testing

Pull all active Meta campaigns and identify any that have had budget, creative, and audience changes made in the same 7-day window — simultaneous multi-variable changes make it impossible to isolate what caused any subsequent performance change and invalidate all learnings from that period.

Creative & Testing

For my Google campaigns, pull the performance of the control variant vs. experimental variant in any active experiments — and calculate the projected annual revenue impact of implementing the winning variant at full scale, to prioritize which experiment results deserve immediate rollout.

Creative & Testing

Pull all Meta ad sets where the creative testing has been running for more than 21 days without a clear winner emerging — and flag any where the metrics between variants are within 10% of each other, because these tests have reached practical equivalence and resources should be redirected to testing more differentiated concepts.

Creative & Testing

For my Meta account, calculate how much budget has been allocated to creative testing vs. scaled winning campaigns in the last 30 days — and evaluate whether the testing-to-scaling ratio is appropriate, because an account spending less than 10% of budget on active creative tests is at high risk of fatigue-driven performance deterioration without a creative pipeline to replace declining ads.

Audience & Targeting

Pull Meta performance broken down by new audience vs. existing customers vs. engaged audience using the user segment breakdown — and show me the CPA and ROAS for each segment type, because blended campaign metrics that mix all three hide the true cost of new customer acquisition.

Audience & Targeting

For all active Google campaigns, pull performance by location at the city or region level — and identify the top 10 geographic markets by conversion volume and the bottom 10 by CPA — then compare whether current location bid adjustments reflect these performance differences.

Audience & Targeting

Pull Meta ad set performance for all lookalike audiences and break them down by lookalike percentage (1%, 2%, 5%, 10%) — then compare CPA and purchase volume for each tier to identify the optimal lookalike size that balances audience quality with scale for my specific product category.

Audience & Targeting

For all active Meta campaigns, pull the performance breakdown between new visitors and returning visitors if supported by pixel data — and calculate what percentage of reported purchases are coming from returning customers, because a campaign with 70%+ returning customer purchases is retaining rather than acquiring and should not be evaluated as a prospecting campaign.

Audience & Targeting

Pull all active Meta lookalike audiences and show me the seed audience used for each — then flag any lookalike built from a seed audience of fewer than 1,000 people, because small seed audiences produce lower-quality lookalikes that do not accurately represent the true characteristics of your best customers.

Audience & Targeting

For my Meta account, pull the performance of interest-based targeting vs. broad audience targeting vs. lookalike audiences across all prospecting campaigns — and compare CPA and purchase volume for each approach at equivalent spend levels, because the targeting philosophy with the best CPA at scale should inform where future prospecting budget is concentrated.

Audience & Targeting

Pull all active Meta ad sets and identify any that are targeting audiences with a high overlap with my existing customer base — running prospecting creative to audiences that are predominantly existing customers wastes acquisition budget on people who may already be in the purchase funnel or who have already converted.

Audience & Targeting

For all active Google Search campaigns, pull the audience observation data — and identify any audience segment (in-market, affinity, or remarketing) that has a significantly lower CPA than the baseline, which would justify creating a separate campaign targeting this segment with a dedicated budget.

Audience & Targeting

Pull all Meta custom audiences currently in use and check their recency — specifically flag any website visitor audience built on a 180-day window that is being used for retargeting, because 180-day website visitors include a large proportion of low-intent browsers whose inclusion dilutes the retargeting pool and inflates CPA.

Audience & Targeting

For my Meta account, pull the size of each active custom audience and evaluate whether the retargeting pool is large enough to sustain current retargeting spend — a retargeting budget that exceeds what the audience size can support at healthy frequency (below 3) is driving diminishing returns and inflating CPMs.

Audience & Targeting

Pull all active Meta lookalike campaigns and show me the performance breakdown by lookalike percentage tier — 1%, 2%, 3-5%, 6-10% — and identify whether the performance degradation from 1% to 10% is gradual or sharp, because this tells me how aggressively I can expand lookalike audiences without losing conversion quality.

Audience & Targeting

For my Google campaigns, pull the in-market audience performance data from audience observations — and identify any in-market segment where CPAs are more than 20% below the campaign average, then evaluate whether creating a separate campaign targeting only this in-market audience with a higher budget would be justified.

Audience & Targeting

Pull all active Meta campaigns and calculate the unique reach generated per dollar of spend — then compare this across campaigns to identify which campaigns are efficiently introducing the brand to new users vs. which ones are repeatedly reaching the same users at a higher cost per unique impression.

Audience & Targeting

For my Meta account, pull the performance of video view custom audiences (people who watched 50%+ of specific videos) when used as retargeting audiences — and compare their CPA to website visitor retargeting audiences, because video view audiences built from high-performing content often represent a warm, high-intent segment that is underutilized in most retargeting strategies.

Competitive Intelligence & Account Setup

Pull all active Meta carousel ads from [competitor brand name] and show me what products or features they highlight in the first card — the first card of a carousel gets the most impressions and reveals what they believe is their strongest hook.

Competitive Intelligence & Account Setup

Find Meta ads from [competitor brand name] where the headline contains a number, a question, or a direct pain point — I want to see which copywriting formula they deploy most frequently in their highest-volume ads.

Competitive Intelligence & Account Setup

Pull all Meta ads from [competitor brand name] and identify which ones are directing traffic to a homepage vs. a dedicated landing page vs. a product page — this reveals their conversion strategy and level of funnel sophistication.

Competitive Intelligence & Account Setup

Show me the Meta ads from [competitor brand name] that have been running the longest alongside their creative format — ads that survive 90+ days without being pulled are generating strong enough ROI to keep running and deserve close creative study.

Competitive Intelligence & Account Setup

Pull active Meta ads from the top 3 competitors in my category and compare the primary CTA used across all of them — Shop Now, Learn More, Get Offer, Sign Up — to identify which CTAs dominate in this market.

Competitive Intelligence & Account Setup

Find all Meta ads from [competitor brand name] that mention a specific price point, discount percentage, or dollar-off offer — this reveals their promotional cadence and pricing strategy being pushed through paid social.

Competitive Intelligence & Account Setup

Pull Meta ads from [competitor brand name] and identify any that are clearly targeting a specific pain point vs. an aspirational outcome — tell me which angle they use more, as this reveals their understanding of their customer's primary motivation.

Competitive Intelligence & Account Setup

Show me all Meta video ads from [competitor brand name] — and for each one tell me whether it appears to be UGC-style, studio-produced, or influencer-led, so I can understand the creative production direction they're investing in.

Competitive Intelligence & Account Setup

Pull all Meta ads from [competitor brand name] launched in the last 30 days and compare them to ads they were running 90 days ago — tell me what has changed in their messaging, format, or offer strategy so I can infer what's working for them now.

Competitive Intelligence & Account Setup

Find Meta ads from competitors in [my product category] that are running on Reels placement — show me the visual style and length to understand how the category is adapting creative for short-form vertical video.

Competitive Intelligence & Account Setup

Pull all active Meta ads from [competitor brand name] and identify which ones use before/after, comparison, or transformation-style creative — and how long those specific ads have been running, indicating whether that angle converts in this market.

Competitive Intelligence & Account Setup

Show me the Meta ads from [competitor brand name] that have the most social engagement visible — comments, reactions, shares — as high engagement ads reveal messaging that resonates beyond just paid distribution.

Competitive Intelligence & Account Setup

Pull all Meta ads from [competitor brand name] and identify if they are running separate creatives for different audience segments — for example, ads targeting first-time buyers vs. returning customers, which reveals audience segmentation sophistication.

Competitive Intelligence & Account Setup

Find Meta ads in [my product category] from any brand that have been running continuously for more than 90 days — these are the category's proven long-form winners and represent the gold standard for evergreen creative strategy in this market.

Competitive Intelligence & Account Setup

Pull all active Meta campaigns and show me which ones are using Campaign Budget Optimization vs. ad set level budgets — then flag any inconsistency where CBO is being used with only one ad set, which removes the algorithmic budget allocation benefit entirely.

Competitive Intelligence & Account Setup

For all active Google Search campaigns, pull the list of negative keyword lists applied to each campaign — and flag any campaign with no negative keyword lists attached, as running Search without negatives is one of the most common and costly structural oversights in any account.

Competitive Intelligence & Account Setup

Pull all active Meta ad sets and show me the optimization event each is using — flag any ad set optimizing for a high-funnel event like link clicks or landing page views if the campaign objective is purchases, as mismatched optimization events frequently explain why an ad set drives traffic but no conversions.

Competitive Intelligence & Account Setup

For my Google campaigns, pull all conversion actions that are currently being tracked and show me which ones are set as primary vs. secondary — and flag any campaign where a micro-conversion (like page view or add-to-cart) is set as a primary conversion action, as this gives the bidding algorithm the wrong signal to optimize against.

Competitive Intelligence & Account Setup

Pull all active Meta campaigns and identify any that have no UTM parameters on destination URLs — without UTMs these campaigns create unattributed traffic in Google Analytics and make cross-platform revenue reconciliation impossible.

Competitive Intelligence & Account Setup

For all active Google ad groups, pull the number of active RSAs per ad group — and flag any ad group with fewer than one active RSA, as Google now requires RSAs and ad groups without them are structurally incomplete and will underperform in auction.

Competitive Intelligence & Account Setup

Pull all active Meta ad sets and show me the attribution window setting for each — flag any ad set where the attribution window differs from the account default, as inconsistent attribution windows across ad sets make ROAS comparisons between them meaningless.

Competitive Intelligence & Account Setup

For my Google PMax campaigns, pull the number of asset groups per campaign and the number of assets per group — and flag any asset group that has fewer than 3 images, 1 video, 5 headlines, or 5 descriptions, as under-populated asset groups severely limit Google's ability to optimize creative combinations.

Competitive Intelligence & Account Setup

Pull all active Meta campaigns and show me which pixel events are firing for each campaign's conversion tracking — and flag any campaign where the pixel event being tracked does not match the campaign's stated objective.

Competitive Intelligence & Account Setup

For all active Google Shopping campaigns, pull the campaign priority setting (Low, Medium, High) for each — and flag any account where multiple Shopping campaigns targeting the same products have the same priority level, as this creates undefined auction behavior where the wrong campaign may win for a given query.

Competitive Intelligence & Account Setup

Pull all active Meta ad sets and show me the audience type for each — custom audience, lookalike, interest, or broad — then flag any prospecting campaign that is running exclusively on custom audiences, as this means it is only reaching people already familiar with the brand rather than acquiring new customers.

Competitive Intelligence & Account Setup

For all active Google campaigns, pull the bid strategy in use for each and compare it against the conversion volume generated in the last 30 days — and flag any campaign using a smart bidding strategy (Target CPA, Target ROAS) with fewer than 30 conversions in the last 30 days, as these campaigns lack sufficient data for smart bidding to function correctly.

Competitive Intelligence & Account Setup

Pull all active Meta campaigns and identify any where the campaign objective is set to Traffic or Awareness but the ad set has purchase pixel events configured — the mismatch between campaign objective and optimization event creates a structural conflict that limits the algorithm's ability to find buyers.

Competitive Intelligence & Account Setup

For all Google Search campaigns, pull the keyword match type distribution — and calculate the percentage of spend going to broad match vs. phrase match vs. exact match keywords — a high broad match spend percentage without a comprehensive negative keyword strategy is a structural risk that should be flagged.

Competitive Intelligence & Account Setup

Pull all active Meta ad sets and show me which ones have ad scheduling (dayparting) enabled — then flag any ad set where dayparting is restricting delivery during hours that historically have high purchase conversion rates for my account.

Competitive Intelligence & Account Setup

For all active Google campaigns, pull automated rules that are currently active — and identify any rules that automatically pause campaigns, reduce budgets, or adjust bids based on single-day performance metrics, as rules based on daily data without statistical thresholds frequently make incorrect changes.

Competitive Intelligence & Account Setup

Pull all active Meta campaigns and identify any that are targeting the same custom audience across both prospecting and retargeting campaigns simultaneously — audience overlap between campaign types leads to internal competition, inflated CPMs, and attribution confusion.

Competitive Intelligence & Account Setup

For my Google Ads account, pull all shared budgets and identify which campaigns are sharing a budget — and flag any shared budget where one campaign is consistently consuming the majority of the shared allocation, starving other campaigns of delivery.

Competitive Intelligence & Account Setup

Pull all active Meta ad sets and show me the minimum ROAS or cost cap bid strategy settings for each — and flag any ad set where an overly aggressive cost cap is causing delivery to fall below 50% of the daily budget, as under-delivery at scale is worse than slightly higher CPAs with full delivery.

Competitive Intelligence & Account Setup

For all active Google campaigns, pull the landing page URLs in use and identify any that return a slow mobile load time indicator — landing page speed is a Quality Score component, and slow mobile pages simultaneously hurt ad rank, increase CPC, and reduce post-click conversion rates.

Competitive Intelligence & Account Setup

For my Google Search campaigns, pull the auction insights report and identify competitors whose impression share has increased by more than 10 percentage points in the last 30 days — new or growing competitors in auction are the most likely explanation for rising CPCs and declining impression share that cannot be explained by account-internal factors.

Competitive Intelligence & Account Setup

Pull my Google Search campaign CPCs week over week for the last 8 weeks — and cross-reference any CPC spikes with publicly known competitor activity, promotional calendars, or seasonal demand increases to determine whether CPC inflation is structural (competitor entry) or cyclical (seasonal demand).

Competitive Intelligence & Account Setup

For my top Google branded keywords, pull impression share and average position — and identify any degradation in position or share that indicates a competitor is actively bidding on my brand terms, which would justify a defensive branded keyword bidding strategy or bid increase.

Competitive Intelligence & Account Setup

Pull Meta CPM trends for the last 60 days at the account level — and identify any sustained CPM increase that exceeds seasonal norms, as platform-wide CPM inflation in my category indicates increased advertiser competition that requires either higher creative quality to maintain CTR or audience expansion to find cheaper inventory.

Competitive Intelligence & Account Setup

For my Meta prospecting campaigns, pull CPM broken down by audience type — broad, interest, and lookalike — and compare the CPM trend for each over the last 30 days — differentiating audience-level CPM inflation from platform-wide CPM inflation helps identify whether the cost pressure is addressable through targeting changes.

Competitive Intelligence & Account Setup

Pull all Meta ads currently running in my product category from the Ads Library for the top 3 competitors — and identify how many ads each is running simultaneously, because a sudden increase in a competitor's active ad count often signals an upcoming promotional push that will increase auction competition.

Competitive Intelligence & Account Setup

For my Google Shopping campaigns, pull the benchmark CTR data available in the Google Ads interface — and compare my actual product listing CTR against the benchmark for each product category to identify where product title, image, or price competitiveness improvements would have the highest impact.

Competitive Intelligence & Account Setup

Pull my Google Search impression share alongside the auction insights top-of-page rate for my top competitors — and identify any competitor consistently winning the top-of-page position at a higher rate than me, as this indicates they have achieved a combination of higher bids and Quality Scores that requires a multi-lever response.

Competitive Intelligence & Account Setup

For my Meta account, pull the CPM trend for the 30 days before and 30 days after my last major creative refresh — and quantify how much the creative refresh reduced CPM, because fresh creative that generates higher CTR lowers CPM through improved relevance signals and this impact can be measured and used to justify future creative investment.

Competitive Intelligence & Account Setup

Pull Google Shopping auction insights for my top 3 product categories — and identify which competitors have the highest overlap rate with my products in Shopping auctions — then compare their average position against mine to understand the competitive gap in Shopping placement quality.

Competitive Intelligence & Account Setup

Pull the search term report for all active Google Search campaigns and segment terms into four buckets — brand, competitor, generic category, and informational — then calculate the CPA and spend share for each bucket to understand the true composition of my search traffic and where conversion efficiency actually lives.

Competitive Intelligence & Account Setup

For all active Google campaigns, pull the Quality Score components — expected CTR, ad relevance, and landing page experience — at the keyword level — then identify the specific component that is most frequently rated Below Average, as this single component is responsible for the majority of Quality Score drag across the account.

Competitive Intelligence & Account Setup

Pull Google Shopping search terms and identify the top 20 terms by spend — then manually review whether each term's intent aligns with purchase readiness or whether it reflects research, comparison, or informational intent — and calculate how much budget is being consumed by non-purchase-intent queries.

Competitive Intelligence & Account Setup

For all active Google PMax campaigns, pull conversion volume by asset group — and compare the conversion rate per impression for each asset group to identify which asset groups are punching above their weight and which are diluting overall campaign performance.

Competitive Intelligence & Account Setup

Pull all active Google Search campaigns and identify ad groups where the keyword theme and the RSA copy are misaligned — specifically ad groups where the primary keyword is not present in any of the RSA headlines, as this misalignment directly suppresses ad relevance scores and Quality Score.

Competitive Intelligence & Account Setup

For my Google Shopping campaigns, pull product-level ROAS and compare it against my target ROAS — then classify every product into three categories: PROMOTE (above target with 5+ conversions), MONITOR (insufficient data), and KILL (spend above 2x AOV with zero conversions) — so I have a clear action list.

Competitive Intelligence & Account Setup

Pull Google Ads auction insights for my top 3 Search campaigns — and show me which competitors have the highest impression share overlap with my campaigns, how their impression share has changed month over month, and where I am winning vs. losing in head-to-head auction competition.

Competitive Intelligence & Account Setup

For all active Google campaigns, pull the conversion rate broken down by hour of day for the last 30 days — and identify the 6-hour window with the lowest conversion rate, then evaluate whether bid modifiers or dayparting adjustments during that window could improve overall campaign efficiency.

Competitive Intelligence & Account Setup

Pull Google keyword performance and identify any keywords where average position is consistently above 1.5 but conversion rate is below the campaign average — high position at above-average CPC with below-average conversion rate means I am paying a premium for visibility on keywords that do not convert efficiently at that position.

Competitive Intelligence & Account Setup

For all active Google PMax campaigns, pull the search category report if available — and identify the search term themes driving the most impressions and the most conversions separately — because PMax frequently generates high impression volume in categories that are brand-adjacent but not purchase-intent, and this report reveals whether the algorithm is optimizing for volume or value.

Scaling & Strategic Planning

Pull all active Google Shopping campaigns and identify SKUs that have generated ROAS above target with more than 5 purchases in the last 30 days — these are the PROMOTE candidates that should be isolated into their own product group or campaign to give them protected budget.

Scaling & Strategic Planning

For my Meta account, pull all active lookalike audiences and compare their CPA and ROAS to interest-based audiences in the same campaigns — and tell me which lookalike percentage is outperforming so I know where to direct prospecting budget expansion.

Scaling & Strategic Planning

Pull Google campaign performance for the last 30 days and identify any campaign where the 7-day ROAS is higher than the 30-day ROAS — an improving trend over the most recent week is a forward-looking signal that conditions are right to increase investment now.

Scaling & Strategic Planning

For my Meta account, pull all active video ads with a hook rate above 40% AND a cost per purchase below my account average — this intersection of strong creative engagement and efficient conversion is the exact profile of a creative worth duplicating and scaling.

Scaling & Strategic Planning

Pull all active Google campaigns where Target ROAS is set and the actual ROAS has exceeded the target for 10 or more of the last 14 days — consistent over-delivery against target means the target may be set too conservatively and can be raised to unlock more volume.

Scaling & Strategic Planning

For my Meta account, pull all active ad sets and calculate their revenue contribution as a percentage of total account revenue — only ad sets contributing more than 10% of account revenue with above-target ROAS should be considered for meaningful budget scaling, to avoid over-indexing on small pockets of performance.

Scaling & Strategic Planning

Pull Google PMax campaign performance and compare it to Search campaigns running in the same account over the last 30 days — if PMax is generating a higher conversion volume at a comparable CPA, evaluate where Search budget could be consolidated into PMax to capture broader demand.

Scaling & Strategic Planning

For my Meta account, pull the performance of all active broad audience ad sets (no interest or lookalike layers) and compare to interest-targeted ad sets — if broad is outperforming on CPA, this is a structural signal to simplify targeting and consolidate budget into fewer, wider ad sets.

Scaling & Strategic Planning

Pull all active Google Search campaigns and identify the top 5 keywords by conversion volume that are currently in exact match — then check if equivalent phrase or broad match variants of those keywords exist and whether they are capturing additional relevant volume at a comparable CPA.

Scaling & Strategic Planning

For my Meta account, pull all active campaigns and identify the one ad set per campaign that has the highest purchase volume AND the lowest CPA simultaneously — this is the account's most efficient unit and should be the first candidate for a 15-20% budget increase before testing anything else.

Scaling & Strategic Planning

Pull Google Shopping campaign data and identify the top 10 SKUs by revenue contribution over the last 30 days — then check their current bidding priority and whether they have protected budget or are competing with low-ROAS products in the same campaign for the same budget.

Scaling & Strategic Planning

For my Meta account, pull all active creatives launched in the last 21 days that already have 3 or more purchases — early conversion signals within 3 weeks indicate a strong creative that has found product-market fit quickly and should receive accelerated budget before the novelty window closes.

Scaling & Strategic Planning

Pull all active Google ad groups with a Quality Score above 8 alongside their impression share — high Quality Score means Google favors these ads in auction, and any with low impression share are being held back by bid or budget constraints rather than relevance issues.

Scaling & Strategic Planning

For my Meta account, pull all active ad sets and rank them by the ratio of purchase conversion value to spend — then identify the top quartile by this efficiency metric and calculate the total combined daily budget they are currently receiving vs. the total account budget, to determine the headroom available to over-index on proven efficiency.

Scaling & Strategic Planning

Pull my Meta account's historical performance data for the same time period last year — compare CPM, CTR, conversion rate, and ROAS — to identify whether I am entering a seasonally strong or weak period and calibrate expectations accordingly.

Scaling & Strategic Planning

For all active Google campaigns, pull impression share data for the last 4 weeks alongside daily budget utilization — and calculate how much additional budget would be needed to capture 80% impression share on my highest-converting campaigns during peak demand periods.

Scaling & Strategic Planning

Pull Meta campaign performance for the last 30 days and identify which campaigns have maintained the most stable CPA week over week — campaigns with stable CPAs are algorithmically mature and can absorb budget increases with the lowest risk of performance disruption during scaling.

Scaling & Strategic Planning

For my Google Shopping campaigns, pull conversion volume by product category for each of the last 4 weeks — and identify any category showing week-over-week acceleration in demand, which may indicate an emerging trend or seasonal signal worth capturing with additional investment.

Scaling & Strategic Planning

Pull all active Meta campaigns and calculate the minimum budget needed to exit the learning phase based on current CPA — any campaign running below this threshold is permanently in a sub-optimal learning state and will not reach peak efficiency regardless of how long it runs.

Scaling & Strategic Planning

For all active Google Search campaigns, pull the impression volume trend for my top 10 keywords over the last 8 weeks — and identify any keywords where search volume is growing, as organic demand growth represents an opportunity to capture more conversions by increasing bids or budgets on proven converters.

Scaling & Strategic Planning

Pull Meta ad performance and identify which creative formats, offer types, and audience configurations performed best in the same calendar period last year — then use this as a brief for new creative production ahead of the upcoming equivalent period.

Scaling & Strategic Planning

For my Google Ads account, pull the historical spend and conversion volume during key seasonal periods (Q4, Valentine's Day, back-to-school, etc.) — then compare against current campaign readiness to identify structural, creative, or budget gaps that need to be addressed before the next peak period arrives.

Scaling & Strategic Planning

Pull all active Meta campaigns and calculate the current weekly creative refresh rate — then compare this to what the refresh rate needs to be based on current frequency growth to stay ahead of fatigue without burning out the creative production pipeline.

Scaling & Strategic Planning

For my Google PMax campaigns, pull the seasonal adjustment settings currently in use — and identify any upcoming high-traffic period where a seasonal bid adjustment should be applied proactively rather than reactively after performance has already suffered from demand spikes.

Scaling & Strategic Planning

Pull the performance of all Meta campaigns that ran during my last major promotional period and calculate the incremental ROAS — meaning the revenue generated above baseline organic revenue — to understand the true profit contribution of paid spend during promotions rather than just the headline ROAS number.

Scaling & Strategic Planning

For all Google campaigns that were paused or ended in the last 90 days, pull their final 30-day performance metrics — and identify if any were paused while still generating above-target ROAS, which would indicate premature campaign termination driven by non-performance factors.

Scaling & Strategic Planning

Pull Meta ad performance for all ads that were created as A/B tests in the last 60 days — and for each test identify whether there was a statistically meaningful difference in the primary metric, or whether the test was ended before enough data was collected to draw valid conclusions.

Scaling & Strategic Planning

For my Google Search campaigns, pull the list of negative keywords added in the last 90 days — and cross-reference them against current converting search terms to identify if any negatives were added too broadly and are now blocking converting traffic.

Scaling & Strategic Planning

Pull all Meta campaigns that ran for more than 60 days and ended in the last quarter — show me their cumulative ROAS, total revenue generated, and total spend — then rank them to identify which campaign types and structures delivered the best long-term efficiency at scale.

Scaling & Strategic Planning

For my Google Ads account, pull the quality score trend for my top 20 keywords over the last 3 months — and identify any keywords where Quality Score declined despite no changes to the ad or landing page, which may indicate competitor improvements to their relevance signals.

Scaling & Strategic Planning

Pull all Meta creative tests conducted in the last 90 days and show me the winning variant alongside the losing variant for each — then identify patterns across winning variants to build a data-driven hypothesis about what creative elements are consistently outperforming in my category.

Scaling & Strategic Planning

For all active Google campaigns, pull the conversion data and calculate the percentage of total conversions that occurred within 1 day of the click vs. within 7 days vs. within 30 days — then evaluate whether my attribution window settings capture enough of the conversion cycle or are under-reporting true campaign performance.

Scaling & Strategic Planning

Pull Meta campaign performance broken down by the first month of each campaign's life vs. subsequent months — and identify whether campaigns tend to start strong and decay, start weak and improve, or maintain consistent performance — as this pattern should inform how long to give new campaigns before making optimization decisions.

Scaling & Strategic Planning

For all Google Shopping campaigns, pull the SKU-level performance for the last 90 days and identify the top 10 SKUs by total revenue contribution — then verify whether these SKUs have appropriate bidding priority and dedicated budget, or whether they are competing for budget with low-performing products in the same campaign structure.

Scaling & Strategic Planning

Pull my Meta account's blended CPA for new customer acquisition for each of the last 6 months — and calculate the trend to determine whether new customer acquisition is getting more expensive over time, which informs whether the current growth strategy is sustainable or approaching a ceiling.

Scaling & Strategic Planning

For my Google Ads account, pull total revenue attributed to each campaign type — Search, Shopping, PMax, Display — over the last 12 months — and calculate the year-over-year growth rate for each to identify which campaign type is accelerating, plateauing, or declining as a revenue contributor.

Scaling & Strategic Planning

Pull the Meta account's historical ROAS by quarter for the last 4 quarters — and identify whether seasonal patterns are consistent year over year, to determine whether current ROAS performance is on track relative to historical seasonality or is diverging from the expected pattern.

Scaling & Strategic Planning

For my Google and Meta accounts combined, pull total monthly spend and total monthly revenue for the last 12 months — then calculate the marginal ROAS of incremental spend at each spend level to identify the point of diminishing returns and the optimal total paid media investment level for current account conditions.

Scaling & Strategic Planning

Pull all active Meta and Google campaigns and calculate the new customer acquisition cost for each — then model the payback period based on estimated average order value and repurchase rate to determine which campaigns are generating customers that pay back within an acceptable timeframe and which are acquiring customers at a cost that the business cannot sustain.

Scaling & Strategic Planning

For my Meta account, pull the performance data for campaigns that were running 12 months ago and are still running today — and calculate how their ROAS and CPA have evolved over 12 months to determine whether long-running campaigns naturally improve through algorithmic optimization or inevitably decay through creative fatigue.

Scaling & Strategic Planning

Pull all Google and Meta campaigns and calculate the total platform spend as a percentage of total business revenue for each of the last 6 months — then evaluate whether the paid media investment ratio is expanding (indicating platform dependency) or contracting (indicating improving organic revenue leverage).

Scaling & Strategic Planning

For my Meta account, pull the performance breakdown of all campaigns targeting cold audiences vs. warm audiences over the last 90 days — and calculate the split of total revenue between new customer acquisition and existing customer reactivation, to determine whether the business is growing its customer base or primarily monetizing its existing one through paid media.

Scaling & Strategic Planning

Pull all active Google and Meta campaigns and identify the total number of creative assets — images, videos, headlines, descriptions — currently in rotation — then calculate whether the creative library is large enough and diverse enough to sustain 6 months of testing at the current refresh cadence without repeating concepts that have already been exhausted.

Scaling & Strategic Planning

For my Google and Meta accounts combined, pull the cost per new customer acquired in the last 30 days and compare it against the estimated 12-month customer lifetime value — this single ratio, the LTV-to-CAC multiple, determines whether current paid media investment is creating or destroying business value and should be the ultimate lens through which every campaign decision is evaluated.