Every new paid media engagement starts the same way: five days, same checklist, no assumptions. We have run this diagnostic on accounts spending $8,000 a month and accounts spending $400,000 a month. The issues are almost always the same. The scale of the damage varies.
This is not a theoretical framework. These are the eight specific metrics we pull first, what benchmarks we compare them against, and what we typically find. If you manage paid media for your business or for clients, you can run every single one of these checks yourself.
1. Impression Share Lost to Budget vs. Lost to Rank
Pull Impression Share, IS Lost to Budget, and IS Lost to Rank for every Search campaign. These two numbers tell you a completely different story about what is wrong.
- High IS lost to budget (>15%) means you are not spending enough in campaigns that are already winning on quality. The fix is budget reallocation — not always more total spend.
- High IS lost to rank (>25%) means your Quality Score or landing page experience is capping your ability to compete at any budget level. Throwing money at this makes it worse.
- High on both: you have a priority problem. Fix rank first, then allocate budget.
What we typically find: 60% of accounts have their largest budgets sitting in campaigns with significant rank losses. They are funding reach into placements they cannot actually win competitively. The high-performing campaigns are the ones with tight budget caps.
2. Search Term Report Match Type Leakage
Pull the search terms report for the last 90 days. Sort by spend, descending. Look at the top 100 rows. For each term, note whether it is an exact or phrase match to a keyword you explicitly added, or whether it is a broad match expansion.
The benchmark: in a well-managed account, 80%+ of spend should go to terms you deliberately targeted. In accounts that rely heavily on broad match or Performance Max without proper negative keyword lists, 30–45% of spend regularly goes to terms with marginal relevance — often competing directly with branded keywords, going to irrelevant industry segments, or hitting informational searches from people who will never buy.
3. Conversion Window vs. Actual Sales Cycle
Pull your account conversion settings and note the click-through conversion window for each conversion action. Then pull your CRM data: what is the median and 90th-percentile time from first paid ad click to closed revenue?
If those two numbers do not match, your bidding strategy is optimizing for the wrong signal. A B2B account with a 60-day average sales cycle running a 30-day conversion window is systematically telling Google that half of its eventual customers did not come from paid ads. Smart Bidding responds by cutting bids in campaigns that are actually working. You get lower CPCs but worse outcomes.
Conversely, a DTC account with a 2-day average purchase cycle running a 90-day window is attributing every organic return visit to the original paid click. The campaign looks like it is printing money. It is not.
4. Creative Frequency × CTR Decay
Pull the Ads report, filtered to the last 90 days, and sort by impressions. For each ad, look at CTR over time — specifically at what frequency the CTR starts declining compared to the CTR in the first two weeks of the ad running.
- B2C cold audiences: CTR decay typically starts between 3–5× frequency. Below this, most users have not seen the ad enough for it to register.
- B2B cold audiences: decay often starts earlier, around 1.5–2× frequency, because the audiences are smaller and more concentrated.
- Retargeting: much faster decay — expect significant CTR drop after 5–7 days for most e-commerce retargeting ads.
What we typically find: ads that have been running for 4–6 months with no creative rotation, CTR down 50–60% from launch, and CPM costs that have risen because the platform is detecting low engagement and deprioritizing the ad in the auction. The account manager knows the ads are "getting tired" but has not made new creative a priority because the account is "performing." The platform is extracting more money for worse results, gradually.
5. Campaign-Level ROAS Spread
Pull revenue (or conversion value) and spend by campaign. Calculate ROAS per campaign. Rank them. Then look at the budget distribution.
The typical finding: 1–2 campaigns are carrying the account with 3–5× ROAS. 3–5 campaigns are running below 1.5× ROAS, often below break-even. And the budget is distributed roughly evenly across all of them — because it was set up that way when the campaigns launched and nobody ever restructured it based on what the data showed.
The reason this persists: restructuring campaigns is uncomfortable. It means killing things. It means telling the stakeholder that the campaign they were excited about is underwater. Doing nothing feels safer than doing the math. The math does not care.
6. Quality Score Distribution
Pull keyword-level Quality Score for all active keywords. Filter to keywords with more than $200 in spend over 90 days. Build a distribution: how many keywords are at QS 8–10, 6–7, 4–5, 1–3?
Quality Score below 6 means you are paying a CPC premium — sometimes 30–60% above what a well-optimized keyword would cost for the same position. The root cause is almost always one of three things: ad relevance (the ad copy does not match the keyword intent), expected CTR (the ad is not compelling enough for this query), or landing page experience (Google thinks the page after the click does not deliver what the keyword promises).
What we find: most accounts have 15–25% of their active spend on keywords with QS below 6. That is not a rounding error. At a $50,000/month spend, that is $7,500–$12,500 per month in CPC premium being paid to Google for not having matched the ad experience to the keyword.
7. Attribution Model vs. Sales Cycle Length
In Google Ads, go to Tools → Attribution → Model Comparison. Compare last-click to data-driven attribution for your primary conversion action. Look at which campaigns gain credit under data-driven and which lose it.
Brand campaigns almost always look spectacular under last-click. A branded search happens at the end of the decision journey — the customer has already decided to buy. The brand keyword gets 100% of the credit for a conversion that was driven by an awareness campaign, a retargeting sequence, and three organic blog visits. Data-driven attribution redistributes that credit based on the actual path.
If you are bidding on brand keywords under a target ROAS goal and last-click is your attribution model, you are systematically overvaluing brand and undervaluing the upper-funnel spend that built the consideration. The result: you cut the thing that fills the funnel and optimize the thing that captures what was already in it. Funnel runs dry in 6–12 months. You wonder why prospecting is not working.
8. Budget Pacing by Hour
Pull the hourly impression data for your top two campaigns (or use the Google Ads Editor to see spend by hour). Look for campaign budget exhaustion — specifically, at what hour does the daily budget run out?
- Budget exhausting before noon: you are bidding aggressively in low-intent morning hours and missing peak-intent afternoon and evening searches.
- Budget exhausting around 3–4pm: common, and means you are absent for the highest-converting evening window in most B2C categories.
- Budget lasting all day but spend heavily weighted to 6–10am: check if you have ad scheduling or bid adjustments deprioritizing evenings by accident.
The fix depends on the root cause. Sometimes it is increasing budget in campaigns that earn it. Sometimes it is adding a bid reduction in early morning hours so the budget stretches to cover peak times. Sometimes it is switching to a Target CPA or Target ROAS strategy and letting Google self-pace — but only once conversion tracking is clean enough for Smart Bidding to optimize on real signals.
What the Checklist Tells You
In five years of running this diagnostic, we have never seen an account pass all eight checks cleanly. The average account fails four or five of them. That is not a criticism of the people managing the account — it is a reflection of how complicated these platforms are and how little time most in-house teams have to do the forensic work.
The value of the checklist is not the individual findings. It is that it tells you whether the engagement is primarily about fixing fundamentals — which is 70% of new accounts — or about scaling what is already working. Those are completely different strategies. Running the same playbook on both produces mediocre results on both.