1. Business Alignment
Before examining any campaign setting, we ask what business outcomes the account is supposed to serve. This sounds obvious, but many accounts are running campaigns that optimise towards actions — page views, form opens, add-to-carts — that have no proven connection to closed revenue. We check whether conversion actions have been mapped to actual business outcomes (qualified leads, sales, sign-ups that result in revenue) and whether the people managing the account agree on which KPIs matter before optimisation begins.
Business alignment also means checking whether campaign structures reflect business priorities. If a product line contributes 60 percent of revenue but receives 10 percent of ad budget, that is a structural problem no amount of bid-tweaking will fix. We ask the account owner to articulate their priorities and then check whether the account reflects them.
2. Account Architecture
Campaign structure, ad group granularity, naming conventions, and account segmentation. We map the campaign and ad group hierarchy and check whether it reflects meaningful business distinctions — product lines, geographic markets, intent levels, or audience segments — rather than whatever was easiest to set up at launch. Poor architecture makes every downstream problem harder to diagnose: when campaigns are overloaded or ad groups are too broad, it becomes impossible to tell whether a performance issue is caused by bidding, keywords, or creative.
We evaluate whether naming conventions are consistent enough that someone unfamiliar with the account can navigate it. We check whether campaigns are over-segmented (twenty campaigns with three keywords each) or under-segmented (one campaign with five hundred keywords and no thematic grouping). Both extremes waste management time and produce misleading performance data.
3. Campaign Type Coverage
We assess whether the right campaign types are active for the account's goals. Many accounts run only Search when they would benefit from Performance Max, Shopping, YouTube, Demand Gen, or Discovery campaigns. Others run Performance Max with no Shopping feed, which limits what the algorithm can work with. We check whether each campaign type that is running has sufficient conversion data to function, and whether there are campaign types that should be added based on the business model, product catalog, and available creative assets.
This dimension also covers whether campaign types are cannibalising each other. A common problem is running identical targeting across Search and Performance Max, where PMax captures branded queries that would have converted at lower cost through Search. We map the overlap and recommend consolidation or differentiation as appropriate.
4. Conversion Measurement
GA4 event configuration, Google Tag Manager container audit, enhanced conversions setup, import conversions, conversion action priorities, and cross-device tracking. We verify that the conversion actions listed in Google Ads actually correspond to business outcomes, that they are counted correctly, and that they are not being double-counted or under-counted across devices and browsers.
We check whether enhanced conversions are implemented correctly — not just enabled, but actually sending accurate first-party data that improves signal quality for smart bidding. We review the GTM container for redundant tags, deprecated tracking, and firing rules that may be counting micro-interactions as conversions. If conversion actions are imported from GA4, we check whether the GA4 events themselves are correctly configured and whether the import is using the right attribution model and conversion window.
5. Bidding and Budget
Smart bidding configuration, budget allocation across campaigns, portfolio bid strategies, bid strategy versus conversion data readiness, and spend pacing. We check whether each campaign's bid strategy is aligned with the conversion data available — a campaign with three conversions per month should not be running Target CPA, and a campaign with no conversion data should not be running Maximize Conversions with a target that the algorithm cannot reliably hit.
We map budget allocation against actual performance data. A common finding is that 70 percent of budget sits in campaigns with the worst conversion rates, while high-performing campaigns are budget-constrained. We also check whether portfolio bid strategies would help distribute budget more effectively, whether spend is pacing evenly or front-loading, and whether seasonal patterns are accounted for in budget settings.
6. Keyword and Query Strategy
Match type distribution, search intent mapping, keyword-to-ad-group alignment, keyword quality, and query-to-keyword coverage gaps. We review how keywords are distributed across ad groups and whether each ad group has a coherent theme. We check whether exact match, phrase match, and broad match are used intentionally — with clear reasoning for each — or whether the match type mix is the result of historical default settings.
We evaluate whether keywords are aligned with the search intent they are supposed to capture. A keyword like "cloud software" might mean the searcher is researching, comparing, or ready to buy — and the ad and landing page need to match whichever intent is dominant. We also check whether there are coverage gaps: queries that are driving impressions but for which no corresponding keyword exists, meaning the account is relying on broad-match auto-targeting to capture traffic it has not explicitly opted into.
7. Negative Keyword Hygiene
Negative list coverage, competitor brand negatives, job-search and research-term negatives, cross-campaign negatives, and negation audit cadence. Negative keywords are the primary mechanism for preventing wasted spend, yet many accounts have minimal negation in place. We check whether shared negative lists exist, whether they are applied to the right campaigns, and whether they contain the standard exclusions that most accounts need — job titles, research terms, free-tier queries, and competitor brand names that will not convert.
We also check whether cross-campaign negatives are in place to prevent internal competition. When branded Search campaigns and Performance Max both target the same queries, they compete against each other in auction, driving up CPC. We map the negation gaps and recommend a negation structure that eliminates overlap while preserving legitimate reach.
8. Ad Creative and Assets
RSA asset performance, ad extension coverage, ad relevance to landing page, creative freshness, and asset pinning decisions. We review whether Responsive Search Ads have enough asset variety to allow meaningful testing, whether headlines and descriptions are differentiated enough for the algorithm to optimise, and whether asset pinning is being used to control key messages or is accidentally constraining the ad by fixing weak assets in place.
We check whether ad extensions — sitelinks, callouts, structured snippets, price extensions, promotion extensions — are present, active, and relevant. We evaluate whether ad copy matches the search intent it is served against: an ad promising "enterprise pricing" appearing against a query for "small business tool" is a relevance mismatch that depresses Quality Score and increases CPC. We also check creative freshness: ads that have not been refreshed in six months are likely underperforming relative to updated competitor creative.
9. Landing Page Experience
Page load speed, mobile experience, message match between ad and landing page, and conversion path clarity. We check whether the page an ad click lands on matches the promise made in the ad — the headline, the offer, the value proposition. A mismatch between ad and landing page is one of the most common causes of low Quality Score and high bounce rates, and it is one of the easiest to fix.
We evaluate page load speed on mobile and desktop, check whether the conversion path is clear (where is the form, where is the buy button, is it above the fold), and whether there are friction points — excessive form fields, mandatory account creation, unclear next steps — that reduce conversion rates. We also check whether the landing page has tracking in place so that the conversion data flowing back to Google Ads is accurate.
10. Search Term Waste
The actual queries triggering ads, the cost of non-converting queries, wasted spend identification, and opportunity cost analysis. This dimension pulls search term data and separates queries into three categories: those that converted (and should be protected), those that are irrelevant and should be negated, and those that are ambiguous — they may convert with different landing pages, ad copy, or bid adjustments but are not converting now.
We calculate the actual cost of non-converting queries over the past 30, 60, and 90 days. This gives the account owner a dollar figure for waste — not an estimate, but a measurement based on what actually happened. We also assess opportunity cost: if budget were freed from non-converting queries, what could it fund instead? This reframes negative keywords from a cost-saving exercise into a reallocation opportunity.
11. Competitive Landscape
Auction insights data, impression share, outranking share, and competitor ad copy and positioning. We check who else is appearing in the same auctions, how often they outrank the account, and whether the competitive landscape has shifted since the last time the account was restructured. Auction insights reveal whether performance problems are caused by account-level issues or by competitors increasing spend, changing messaging, or entering the market.
We review impression share and outranking share trends to determine whether the account is losing ground or holding steady. We also check whether competitors are bidding on the account's brand terms, whether competitor ad copy has changed, and whether the account's ad positioning is aligned with its pricing and value proposition. A competitor offering lower prices with stronger ad copy will win impressions even with lower bids — and the fix is not higher bids but better differentiation.
12. Attribution and Reporting
Attribution model appropriateness, conversion window settings, reported metrics versus business metrics, and dashboard quality. We check whether the attribution model in use matches the business reality. A business with a 30-day sales cycle should not be using last-click attribution, because it credits the final touchpoint and ignores every interaction that built consideration. We evaluate whether data-driven attribution is available and appropriate, and whether conversion windows are set to match actual sales cycle length.
We also review what metrics are being reported versus what the business actually cares about. Many accounts track cost-per-click and click-through rate when the business needs cost-per-acquisition and return on ad spend. We check whether dashboards exist, whether they are updated, and whether they report metrics that lead to better decisions — not just metrics that look good in a report.
