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Case Study: How an Ecommerce Brand Used Tabular Models to Reduce Wasted Ad Spend

sseo brain

2026-02-06

10 min read

How an ecommerce brand converted SKU, search and campaign data into tabular models to cut wasted ad spend and align paid & organic.

Hook: Why your ad budget is leaking — and how tables stop it

If you run an ecommerce brand, you know the pressure: limited budgets, rising CPCs, and a messy mix of campaigns, SKUs and search queries that make it impossible to see what’s truly incremental. The result is wasted ad spend — bids on keywords your organic pages already own, duplicated budget across similar SKUs, and reactive bid changes that never fix root causes.

In 2026 the unblock is not more dashboards — it’s converting disparate commerce, search and campaign data into tabular models that reveal where bids overlap, where paid cannibalizes organic, and where budget reallocations actually move the needle. This case-style walkthrough shows how one ecommerce brand did exactly that and cut wasted spend while improving paid/organic alignment.

Executive summary (what we did and what changed)

Brand: anonymized mid-market ecommerce retailer (home & lifestyle). Timeframe: Q3–Q4 2025. Goal: reduce wasted ad spend and align paid and organic strategy across 5,000 SKUs and 12 markets.

Approach: ingest SKU catalog, search query data, SERP organic metrics, and campaign logs into a unified tabular model. Use canonical SKU-to-query joins, create derived features (SKU-level paid/organic overlap, incremental conversion rates), and apply rules + human review to update bidding and budget allocation.

Outcome (90 days): ~27% reduction in wasted ad spend on identified keywords, 18% increase in incremental conversions from paid channels, and clearer rules for when to bid, harvest, or suppress. These improvements enabled the brand to reallocate a portion of paid budget to high-margin SKUs and seasonal promotions.

Why tabular models — the 2026 context

In 2026, the data and AI landscape favors structured, canonical datasets. Tabular Foundation Models (TFMs) are now mainstream in enterprise analytics: they can reason over tables and surface cross-table relationships that were previously hidden in silos. Brands that convert raw logs and text into clean, consistent tables unlock deterministic insights and AI-assisted pattern detection.

Two platform-level changes made this workflow practical: Google’s rollout of total campaign budgets for Search and Shopping (Jan 2026) allows campaigns to be managed by target budget windows instead of fragile daily budgets; and mature TFMs help analysts de-duplicate SKUs, map queries to product hierarchies, and rank incremental value by SKU/query pair.

Case background: the problem in detail

This retailer had three concrete problems:

High CPC growth and inconsistent ROAS across product categories.
Poor visibility into which paid keywords were actually incremental versus those compensated by organic rankings.
Campaign fragmentation — multiple overlapping ad groups and duplicated SKUs across Shopping and Search campaigns.

The superficial fixes (manual bid cuts, negative keyword lists) reduced cost temporarily but did not scale. We needed a reproducible analytical foundation to decide, at scale, when to bid, when to suppress, and where to shift budget.

Data sources — the inputs to the tabular model

To build a trustworthy tabular model we consolidated these sources into a data warehouse (BigQuery in this engagement):

SKU catalog (product_id, sku, title, category, margin). Updated nightly from the PIM.
Search console logs and organic SERP metrics (queries, avg position, impressions, CTR, page landing URL).
Paid campaigns (ad group, keyword, match type, clicks, cost, conversions, conv_value). 5-minute granularity for the period.
Site analytics (sessions, transactions, revenue by landing URL and product_id). Modeled as last-click and also instrumented for experiment-level incremental measurement.
Inventory and fulfillment constraints (stock level, lead time) to avoid bidding on out-of-stock SKUs.

Transformation strategy — canonicalization and modeling

The core idea: build a canonical SKU table and canonical query table, then a SKU-query pivot table that links paid keywords and organic queries to product outcomes. Key steps:

1) Canonical SKU mapping

Problems solved: duplicate SKUs, variant titles, mismatched SKUs between ad feeds and PIM.

Normalize titles and attributes (lowercase, strip punctuation, standardize color/size tokens).
Use fuzzy match (Jaro-Winkler) and TF-IDF similarity to map feed items to PIM sku.
Create sku_master(sku_id, canonical_title, category, margin_band, lifecycle_status).

2) Canonical query mapping

Problems solved: query variants, misspellings, and brand vs non-brand classification.

Normalize queries (stemming, stopwords removal while preserving intent tokens).
Entity extraction for product attributes (color, size, model).
Assign query_intent: {brand, category, product, transactional, informational}.

3) Join paid and organic signals to SKU — the pivot table

Create a table with one row per sku_id x canonical_query x day. Key fields:

sku_id, canonical_query, date
paid_clicks, paid_cost, paid_conversions
organic_impressions, organic_clicks, landing_url, avg_position
sku_margin, inventory_status
derived: paid_share_of_clicks, organic_share_of_clicks, paid_vs_organic_overlap_score

SQL sketch — building the pivot (simplified)

-- pseudo-SQL
  CREATE TABLE sku_query_daily AS
  SELECT
    s.sku_id,
    q.canonical_query,
    p.date,
    SUM(p.clicks) AS paid_clicks,
    SUM(p.cost) AS paid_cost,
    SUM(p.conversions) AS paid_conversions,
    SUM(o.impressions) AS organic_impressions,
    SUM(o.clicks) AS organic_clicks,
    s.margin
  FROM paid_feed p
  JOIN query_master q ON p.query_norm = q.query_norm
  JOIN sku_master s ON p.product_feed_sku = s.sku_feed_id
  LEFT JOIN organic_console o ON q.query_norm = o.query_norm AND o.landing_url = s.landing_url
  GROUP BY s.sku_id, q.canonical_query, p.date;

Analytical features we derived

The power comes from derived metrics that answer the high-impact questions:

Paid/Organic Overlap Score = fraction of total clicks for query-day where organic landing page belonged to the SKU’s product page. High score (>0.6) signals organic ownership.
Incremental Conversion Rate (ICR) = conversions from paid when organic position < X — estimated via holdout experiments and incremental lift models.
Cost Per Incremental Conversion (CPIC) = paid_cost / incremental_conversions. This becomes the decision trigger for bids.
Inventory-weighted ROAS = conv_value * (inventory_availability_factor) / paid_cost.

How the tabular model revealed bidding inefficiencies

Once the model was built, three patterns surfaced rapidly:

High paid spend on brand queries where the product landing page ranked #1 organically — low incremental conversions, high CPIC.
Duplicated ad groups where the same SKU existed in multiple campaigns with overlapping keywords (bids competing against themselves).
High-cost non-brand category keywords that drove conversions primarily to low-margin SKUs — positive conversions but negative margin after ad costs.

Each pattern suggested a different operational fix: reduce bids for brand queries where organic was dominant; consolidate duplicated ad groups; or reallocate spend from low-margin category keywords to higher-margin, incremental opportunities.

Decision logic: when to bid, harvest, or suppress

We codified a simple decision tree, operationalized in dbt and fed into campaign rules:

If Paid/Organic Overlap > 0.6 and CPIC > target CPIC → reduce bids by X% or switch to ad scheduling/limited SERP presence.
If Paid/Organic Overlap < 0.3 and ICR > threshold → maintain or increase bids.
If SKU margin < margin_threshold and CPIC > margin-adjusted CPIC → suppress keyword or apply negative targeting.
If SKU out of stock or lead_time > promo_window → pause bids.

Operationalizing changes — tools and execution

We implemented the model-driven rules in three ways:

Automated bid suggestions in Google Ads via scripts connected to BigQuery. The script proposed bid adjusts; analysts reviewed before applying.
Consolidated product groups in the Merchant Feed and Shopping campaigns. This removed internal competition and allowed Google’s auction to work efficiently.
Applied negative keyword lists and implemented brand-exclusion rules for specific SKUs where organic ownership was clear.

We also leveraged Google’s new total campaign budgets for two short promotion windows. That freed the team from daily budget micro-management and let the campaign algorithms pace spend across the window while we focused on SKU-level reallocation.

Example: one decision that saved 14% of monthly spend

Query: "brand-name + product-line" — historically high CPC and top converting. The tabular model showed:

Organic landing page for the product ranked #1 for 72% of query-days.
Paid conversions for that query had an ICR of only 8% (low incremental lift).
CPIC exceeded acceptable threshold by 45%.

Action: reduce max CPC for that query by 50% and add to a “brand-smart” campaign with limited impression share. Result: paid spend for the query dropped 63% while overall conversions fell ~6% — most of which were replaced by organic clicks. Net monthly ad spend fell by 14%; organic traffic and revenue remained stable.

Measurement and validation

We used two complementary measurement approaches:

Short-term A/B holdouts on high CPC queries. Randomized query-level holdouts confirmed low incremental lift where the model predicted it.
Time-series pre/post analysis with the tabular model to measure changes in CPIC, incremental conversions, and margin-adjusted ROAS. We adjusted estimates to control for seasonality and promotions.

Findings were strong: modeled reductions in wasted spend were validated by holdouts and held across markets after normalizing for promo events.

Results (quantified)

Within 90 days across prioritized categories:

27% reduction in non-incremental paid spend (savings reallocated to high-margin queries).
18% increase in incremental conversions from paid channels (by removing low-lift spend and focusing on incremental keywords).
Average campaign-level ROAS improved by 12% after margin adjustments.
Operational time to generate actionable bid recommendations fell from 3 days to under 6 hours thanks to automated tabular pipelines.

Governance: building a repeatable process

To prevent drift we codified the process:

Daily ETL pipelines to refresh canonical tables.
Weekly automated reports that flag SKU-query pairs with high overlap and borderline CPIC.
Monthly campaign review to convert model insights into campaign architecture updates (consolidations, new negative lists).
Quarterly holdouts to re-validate incremental lift assumptions as rankings and creatives change.

2026 trends and why this matters now

Several forces in 2026 make this approach essential:

Tabular Foundation Models have matured — they can recommend joins and surface subtle SKU-query relationships across millions of rows.
Advertisers can set total campaign budgets for Search/Shopping, so money can be deployed more strategically over promo windows rather than micro-managed daily.
AI-driven creatives and targeting mean the marginal value of an impression is more dependent on the SKU-level context — not just keyword-level intent.
Privacy-driven signal gaps make structured, first-party tables your most reliable asset for measurement and attribution.

Lessons learned — practical takeaways

Canonicalize early: a clean SKU and query canonical layer is the single biggest time-saver.
Measure incrementality: don’t assume conversions = incremental conversions. Use randomized holdouts where possible.
Automate guardrails: codify CPIC and overlap thresholds to generate consistent, auditable recommendations.
Integrate business signals: inventory, margin, and lead time must be part of the bid decision to avoid profitless conversions.
Close the loop: feed campaign outcomes back into the tabular model to improve future predictions and TFM prompts.

Quick start checklist (what to do in your first 30 days)

Inventory a data list: feed file, paid logs, Search Console, analytics, inventory.
Create canonical SKU and query tables (dbt recommended).
Build the sku_query pivot and compute Paid/Organic Overlap and CPIC.
Run a prioritized list of top-spend keywords through the decision tree (bid/reduce/pause) and implement conservative changes.
Design at least one randomized holdout test to validate low-lift predictions.

Next steps & future-proofing

After stabilizing the model, the brand implemented a TFM-driven assistant that:

Automatically proposed SKU grouping changes to the Merchant Feed.
Suggested creative variants for queries with falling ICR using near-real-time creative performance signals.
Recommended budget reallocation across total campaign budgets to maximize incremental conversions during promotional windows.

These automation layers reduced manual work and allowed analysts to focus on strategic exceptions and creative testing.

“Structured tables, not dashboards, delivered our ROI. Once we could see SKU x Query outcomes daily, decision-making stopped being guesswork.” — Head of Growth, anonymized retailer

Final thoughts — a practical prediction for 2026

Tabular approaches will be the standard operating model for ecommerce marketing in 2026. Brands that invest in canonical tables, integrate business signals, and apply simple incrementality rules will win back wasted ad spend and free budget for higher-impact activities: creative, product assortment, and market expansion.

Call to action

If your campaigns still feel like a game of whack-a-mole, start with a small canonical table: top 500 SKUs and top 1,000 paid queries. Model the Paid/Organic Overlap and CPIC, run one holdout and make conservative bid changes. If you want a ready-to-run dbt model and SQL skeleton used in this case, contact our team or download the free template to begin reducing wasted ad spend this quarter.

seo brain

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