THE ENGINE
LOGIC

The engine matches your ad spend to actual user behavior by linking campaign IDs across both platforms. Every click gets a cost. Every conversion gets a value. Spend meets outcome.

Delete one account and every row tied to those IDs is scrubbed from BigQuery. Delete your last account and the entire vault is destroyed. Your data, your call.

Your BigQuery dataset is yours alone — isolated, encrypted with AES-256, and tagged to your account. No other user can see it. Not even us, unless you explicitly grant support access.

Your email hash generates a dedicated BigQuery dataset. No shared tables, no cross-contamination. Your data lives in complete isolation from every other user.

OAuth 2.0 tokens are scoped to read-only permissions. The engine can see your advertising data to analyze it, but it cannot modify campaigns, adjust budgets, or spend your money.

Delete one account and every associated row is scrubbed. Delete your last account and the entire dataset is destroyed with all tokens revoked. Both operations are permanent and irreversible.

Instead of navigating 47+ campaign settings, you pick one business outcome (E-Commerce Sales, High-Intent Leads, Brand Awareness, App Installs). The engine auto-configures everything else.

For Search campaigns, the wizard asks for strategy — not keywords. Brand Defense (protect your name), Competitor Conquest (steal rival traffic), or Intent Capture (high-value service search). Each playbook has pre-built structures.

The wizard scans your existing Google Ads account for creatives — detecting logos, separating landscape from portrait images, and suggesting assets automatically based on the campaign type you've selected.

Campaign creation progress is cached in browser storage. Close the tab, lose internet, come back later — you pick up exactly where you left off. Cache expires after 7 days or on successful launch.

Verifies that ClickCatalyst holds valid OAuth 2.0 tokens with appropriate permissions for your Google Ads and GA4 accounts. Tokens are monitored for expiry and revocation.

Checks whether BigQuery is actively receiving fresh data from Google Ads and GA4 APIs. 'Initializing' means the engine is still running its first historical hydration — wait 3-5 minutes.

Tests whether Google Ads and GA4 are communicating via the GCLID (Google Click ID) parameter. Broken linkage means your bidding algorithm is making decisions blind.

Scans for recorded conversions in the last 30 days. Zero conversions means Smart Bidding has no learning signal — the AI needs conversion data to optimize effectively.

Automatically scans your GA4 property for trackable events like 'purchase', 'generate_lead', 'sign_up', and compares them to your existing Google Ads conversion actions.

Primary = 'Bid for this' (e.g., Purchase). Secondary = 'Track this, but don't spend money optimizing for it' (e.g., Page View). Only Primary conversions influence Smart Bidding decisions.

Found a GA4 event that isn't in Google Ads? Click 'Import to Ads' to create the conversion action via API in seconds. No need to open the Google Ads interface.

For revenue events (purchase, subscription), the architect uses the transaction value from GA4 automatically. For lead events without transaction values, you set a static value in Google Ads based on your lead-to-customer economics.

A single scan runs three checks simultaneously: your live site HTML is fetched and inspected for GA4 tags and click ID redirect survival, your Google Ads account is queried via API for auto-tagging status and GA4 link health, and your BigQuery data is analyzed for click-to-session loss, zero-value purchase events, and conversion import gaps. All three layers feed a single 0-100 health score.

Industry-standard click-to-session match rates run 85-95%. The monitor benchmarks your account against this range, tracks your 7-day trend, and distinguishes structural baseline loss — ad blockers, sub-second bounces — from fixable leaks caused by broken configuration. When your rate drops, you see whether it fell overnight (something broke) or gradually over weeks (page speed degrading).

The scanner fingerprints your tech stack on every scan. WordPress with WooCommerce gets different fix guidance than Shopify, which gets different guidance than Next.js or a React SPA. Instead of generic advice, you get the specific plugin name, config path, or code change that resolves the issue on your actual platform.

Auto-tagging disabled and GA4 not linked to Google Ads — two issues responsible for the majority of attribution failures — are fixed instantly via API. Click once, the connection is made or restored, no developer required. Every other detected issue gets a step-by-step pipeline verification checklist that walks you to the exact stage where the signal chain breaks.

One unified interface, five entity views: Campaigns, Ad Groups, Ads, Keywords, and Search Terms. Switch lenses to analyze your account at any level of granularity.

Every entity is plotted as a bubble on an X/Y axis (Spend vs. Revenue), with bubble size representing conversion volume. Stars (top-left) are high-revenue, low-spend winners. Bleeders (bottom-right) are high-spend, low-revenue drains.

Pearson's r correlation between all key metrics reveals hidden relationships. Does spending more actually increase conversions? Or does it just tank your ROAS? The matrix answers this with statistical precision.

See how your campaigns are distributed across any metric — ROAS, CTR, CPC. Most accounts follow a power law: a few campaigns carry all the profit while the majority underperform.

Act I: Status Check (10 seconds — am I on track?). Act II: Action Feed (30 seconds — what's burning?). Act III: Deep Dive Dispatch (60 seconds — where do I focus next?). One scroll, complete operational awareness.

The Action Feed doesn't just show data — it prioritizes actions using campaign intelligence signals: bleeding flags, pacing anomalies, quick-win opportunities, and temporal patterns. Each alert has a quantified financial impact.

Projects your month-end spend based on current daily run rate. Shows whether you're on pace, underspending (leaving opportunity), or overpacing (about to blow your budget by day 22). Adjustable daily budget slider lets you model scenarios.

Set your own monthly targets for Revenue, Spend, ROAS, and Conversions via the settings gear. The Performance Pulse gauges show real-time progress toward YOUR goals, not arbitrary benchmarks.

A composite score (0-100) grading your search targeting efficiency. Based on waste ratio (spend on zero-conversion terms), match type discipline (how much waste comes from Broad Match), and Quality Score distribution. Green (above 80) = tight ship. Red (below 50) = bleeding money.

Identifies search terms that meet your configurable criteria: significant clicks, meaningful spend, but zero conversions. These are your negative keyword candidates — budget you can recover immediately by blocking them.

Breaks down wasted spend by match type: Broad vs. Phrase vs. Exact. If 90% of waste is Broad Match, the data is telling you to tighten targeting with more Phrase/Exact keywords and additional negative keywords.

Every waste term has a 'Block' button that adds it as a negative keyword at campaign or account level. Bulk select multiple terms and block them all at once. Instant waste elimination.

Each ad component (headline, description, CTA, sitelink) is scored independently on CTR, CVR, and cost efficiency. This isolates whether the headline is brilliant but the CTA is killing conversions.

For Responsive Search Ads, the lab analyzes which headlines and descriptions Google serves most often, which combinations drive the best CTR, and where pinning could improve performance.

Cross-references creative performance by device type. An ad that dominates on desktop might underperform on mobile due to truncation, viewport differences, or user intent shifts.

Tracks CTR decay over time per ad. When an ad's CTR drops below 70% of its peak performance for 7+ consecutive days, it's flagged as fatigued with a recommendation to refresh.

The engine calculates 'Potential Lift' by analyzing Impression Share Lost to Budget. Formula: (Current Revenue / (1 - Lost IS Budget%)) - Current Revenue. This tells you exactly how much revenue you're leaving on the table.

Transfers are capped at 50% of the source campaign's daily spend to prevent shock to Smart Bidding's learning phase. You can make multiple 50% moves over several days if data supports it.

Campaigns are scored on a composite index of Current ROAS × Impression Share Lost (%), creating a priority ranking. High ROAS + high IS Lost = top reallocation target.

Before recommending a transfer, the system checks 7-day ROAS stability. High volatility (standard deviation >25%) triggers a warning flag to prevent scaling unstable campaigns.

Aggregates conversion rate by Hour of Day × Day of Week to identify 'Prime Time' (scale here) vs 'Kill Zones' (reduce bids or exclude).

Identifies specific device models or browser versions with high traffic but 0% conversion rate—often indicating UX bugs rather than audience issues.

Groups cities and regions by performance similarity, revealing locations that should perform alike based on demographics but don't—flagging potential landing page or offer mismatches.

Each context dimension is scored 0-100. Contexts scoring below 30 are flagged for exclusion or creative adjustment. Contexts above 80 are candidates for bid increases.

Uses a confidence interval approach to calculate statistical readiness. When Readiness hits 95%, you've gathered enough data to scale confidently. The engine tracks both conversion volume and spend efficiency simultaneously.

Campaigns progress through 4 stages: Exploration (buying data, 0-70% readiness), Calibration (data exists but volatile, 70-95%), Exploitation (statistically safe to scale, 95%+), Degradation (efficiency decay confirmed).

Campaigns in Exploration or with high volatility are 'shielded' from waste alerts. This prevents false panic and premature pausing. Shield drops when MVS is reached and volatility stabilizes.

Three modes adjust confidence thresholds: Conservative (95% confidence, Z=1.64), Balanced (90% confidence, Z=1.28), Aggressive (85% confidence, Z=1.04). Higher confidence = more data required before green light.

Runs Pearson's r between 'Driver' spend (Video/Display) and 'Harvester' conversions (Search) with rolling time lags (0-14 days). A peak correlation at lag=5 means Display ads take 5 days to drive Search activity.

Quantifies revenue 'assisted' by a campaign but not claimed by it under Last-Click attribution. Surfaces View-Through Conversions and cross-campaign paths from GA4.

Analyzes the lift in branded search volume driven by non-brand campaign activity. Tracks branded search impressions before and after Display/Video spend changes to isolate the awareness-to-intent pipeline.

Uses GA4 conversion path data to distribute credit across the customer journey. Shows which touchpoints consistently appear before conversions, even if they never get Last-Click credit.

Formula: LTV × (1 - Target Margin %). If your actual CAC exceeds this, the Breakeven Thermometer turns red. This is your mathematical ceiling—spend above it and you lose money on every customer.

Uses 'AOV × Avg Orders (90 days) × Estimated Lifetime in Quarters' as a real-time proxy for Lifetime Value when full cohort data is unavailable. Replace with actual retention curves as your data matures.

Tracks CAC and LTV by monthly acquisition cohort, revealing whether customer quality is improving or degrading over time. Month-over-month trends matter more than absolute numbers.

Shows how many months it takes for the average customer to generate enough gross profit to cover their acquisition cost. Shorter payback = faster reinvestment into growth.

Recency (days since last purchase), Frequency (total purchases), Monetary (total spend). Each dimension is scored 1-5, creating segments like 'Champions' (5-5-5) or 'At Risk' (2-3-4).

Maps acquisition channel (Search, Display, Shopping, Social) to long-term customer segment. Reveals which channels acquire 'Champions' vs 'One-and-Done' buyers.

Based on historical purchase intervals, estimates the probability that a customer has churned. If average purchase interval is 30 days and a customer hasn't bought in 90 days, churn probability is high.

Calculates the expected return from reactivation campaigns targeting 'At Risk' or 'Hibernating' segments. Compares cost of reactivation vs cost of acquiring a new customer.

Breaks search queries into unigrams (single words), bigrams (two-word pairs), and trigrams (three-word sequences). Aggregates performance metrics (spend, conversions, ROAS) across all queries containing each n-gram.

Groups n-grams by inferred intent based on keyword modifiers: 'buy/purchase/order' = Transactional, 'how/what/why' = Informational, 'best/top/review' = Comparison, 'near me/local' = Local. Performance varies dramatically by intent group.

Identifies n-grams that appear across multiple queries and consistently produce poor results (high spend, low/zero conversions). These are systemic waste patterns—adding one n-gram as a negative keyword can eliminate waste across dozens of queries at once.

Surfaces high-performing n-grams that could become exact match keywords or ad group themes. If the bigram 'enterprise solution' appears in 15 queries with 8x ROAS, it deserves its own dedicated ad group.

Quality Score has 3 components: Expected CTR (ad appeal), Ad Relevance (keyword-to-ad alignment), Landing Page Experience (post-click quality). Each is rated Above Average, Average, or Below Average. The Clinic isolates which component is the bottleneck.

Estimates the CPC premium you pay due to low QS. A keyword with QS 4 pays roughly 50-75% more per click than QS 8. The Clinic calculates your total 'QS Tax'—the annual cost of poor Quality Scores across all keywords.

Each QS component maps to specific fixes: Low Expected CTR → improve headlines in Creative Lab. Low Ad Relevance → tighten keyword-to-ad group alignment. Low LP Experience → fix page speed, mobile UX, content relevance.

Monitors Quality Score changes over time per keyword. Sudden QS drops indicate a problem (competitor improvement, LP issue, ad fatigue). Gradual improvement confirms optimization efforts are working.