Inside the Shopping Waste Engine
Every metric in your Shopping Waste audit is sourced from documented BigQuery queries. This page explains product tier classification, feed health scoring, Google Shopping benchmark comparison, category ROAS verdicts, and the priority exclusion ranking system.
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Data Sources
read-only OAuth · no campaign changes everAll data is read via Google Ads API and GA4. Derived intelligence tables are pre-computed in our pipeline before your audit runs.
Google Ads API — Shopping Performance
google_ads_shopping_performance
Product-level cost, clicks, units_sold, gross_profit, brand, category, benchmark data
Google Ads API — Shopping Benchmarks
google_ads_shopping_benchmarks
Google's benchmark CPC and CTR for industry-level comparison
GA4 — Ecommerce Products
ga4_ecommerce_products
Product views, cart adds, purchases for cross-platform zombie validation
Product Performance Tier Distribution
How your catalog splits between winners, zombies, and unknowns
Every product with clicks > 0 from google_ads_shopping_performance classified into four tiers: Converting (conversions > 0), Zombie (20+ clicks, zero sales, above threshold), Untested (< 5 clicks), and Low Activity (5–19 clicks, zero sales). The ratio determines whether you have a pruning problem or a testing problem.
Why this matters: If Zombie > Converting, the algorithm is spending on proven failures more than proven successes. If Untested is the largest tier, the algorithm hasn't explored your catalog — budget is trapped in a handful of products while most never get tested.
In plain English: We sort every product in your Shopping feed into four groups based on performance. The proportions tell the story: lots of zombies = you need to prune aggressively. Lots of untested products = budget is stuck on a few items and most products never get a chance.
Revenue Concentration Risk
Is your Shopping revenue dependent on a handful of SKUs?
Top 10 products ranked by conversions_value, with each product's share of total Shopping revenue. Extreme concentration means a single stock-out, price change, or new competitor can collapse overall performance. Healthy accounts distribute revenue across 20+ products.
Why this matters: A founder who sees '3 products generate 70% of revenue' immediately understands the fragility risk. This isn't about waste — it's about resilience. One supply chain disruption to a top-3 product and the entire Shopping channel drops.
In plain English: We rank your products by revenue and show what percentage each one contributes to total Shopping revenue. If your top 3 products generate most of the revenue, your Shopping channel is fragile — one stock-out or price change crashes everything.
Feed Health Analysis
Are missing product attributes crippling the algorithm?
Aggregation across google_ads_shopping_performance checking product_category_level1 and product_brand for NULLs or empty strings. Products without these attributes receive lower placement quality from Google's Shopping algorithm — they can't be matched to relevant queries or audiences.
Why this matters: Feed quality is the most underrated Shopping lever. A product with missing category can't appear in category-filtered Shopping searches. A product with missing brand loses to branded competitors in every auction. Fixing the feed is a Merchant Center task — free and high-impact.
In plain English: We check how many of your products are missing key data like brand name or product category. Products without this info are invisible to Google's matching system — they either get no impressions or get shown to the wrong people. The fix is in your product feed, not your Google Ads settings.
Google Shopping Benchmark Comparison
Your CPC and CTR vs industry averages from Google's API
From google_ads_shopping_benchmarks: benchmark_average_max_cpc and benchmark_ctr provided by Google's Shopping auction data. Your actual metrics are compared against these benchmarks to contextualize performance. A CPC double the benchmark suggests overbidding; a CTR below benchmark suggests creative or pricing issues.
Why this matters: Without benchmarks, you can't tell if a 2% CTR is good or bad. If the benchmark is 1.5%, you're outperforming. If it's 3%, you're underperforming. The benchmark gives context that raw metrics lack — turning numbers into decisions.
In plain English: Google provides average CPC and click-through rate for Shopping across your industry. We compare your numbers against these benchmarks. If you're paying double the average per click, your bids are too aggressive. If your click rate is below average, your product images or prices need work.
Category ROAS Comparison
Which product categories earn their budget vs which drain it
Per product_category_level1: ROAS compared against account average with a BOOST/MAINTAIN/CUT verdict. Categories 30%+ above average should receive more budget. Categories 30%+ below should be reduced. Uses weighted average ROAS (roas × conversions) to prevent low-volume categories from distorting the benchmark.
Why this matters: Category-level decisions are strategic, not tactical. Moving budget from a CUT category to a BOOST category is a zero-cost efficiency gain — same total spend, better overall ROAS. The weighted average ensures the benchmark isn't distorted by a category with 2 conversions and 10x ROAS.
In plain English: We calculate ROAS for each product category and compare against your account average. Categories above average get a BOOST verdict (give them more budget). Categories below average get CUT (reduce budget). The math is weighted so a tiny category with one lucky sale doesn't skew the benchmark.
Priority-Ranked Exclusion List
The exact SKUs to remove from Shopping campaigns
All zombie products with product_item_id, title, category, wasted cost, click count, and priority tier (HIGH/MEDIUM/LOW based on cost relative to the dynamic threshold). HIGH priority products have spent more than 4× the threshold — far beyond any reasonable testing period.
Why this matters: This is the operational output. Product IDs can be directly pasted into Shopping campaign listing group exclusions or PMax asset group listing groups. Working top-down through the HIGH tier first maximizes budget recovery per minute of effort.
In plain English: We list every zombie product with its ID, name, wasted cost, and urgency level. HIGH priority products have spent 4× more than they should have with zero sales — there's no ambiguity. Copy the product IDs into your campaign's exclusion settings.
Shopping Recovery Summary
The business case in one row
Single-row synthesis: total Shopping spend, zombie waste, waste percentage, account ROAS, and projected recovery value (zombie waste × account ROAS = additional revenue if budget redirected to converting products). The severity verdict classifies accounts as CRITICAL (>40%), HIGH (>20%), or MODERATE.
Why this matters: This is the stakeholder slide. One row, five numbers, one verdict. A founder doesn't need to understand zombie thresholds or feed health — they need to know: 'We waste $X, which could generate $Y if redirected, and the severity is CRITICAL.' This row delivers that.
In plain English: We boil everything down to one line: how much you spend, how much is wasted on zombie products, what percentage that is, and how much additional revenue you'd generate if that wasted budget went to products that actually sell. One number, one decision.
Proprietary notice: The methodology, scoring models, waste classification logic, and recovery projections are proprietary to ClickCatalyst Digital and provided for informational purposes only. Results are subject to standard market volatility. Recovery projections are estimates, not guarantees. Contact us within 14 days if any metric appears inaccurate.
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