Srednoff Yandex Direct Search Query Waste Miner

Yandex Direct Search Query Waste Miner

Review Yandex Direct search queries for irrelevant intent without blindly excluding profitable demand.

A high-spend, zero-goal query list confuses irrelevant intent with delayed sales, missing tracking, assortment gaps, and valuable queries that share a broad negative token.

Who This Is For

A paid-search specialist responsible for reducing irrelevant Yandex traffic without excluding profitable long-tail searches or damaging brand and assisted demand.

What You Get

• A standalone, domain-specific agent workflow with decision rules and stop conditions.
• Evidence requirements, structured report guidance and missing-data handling.
• Synthetic worked examples and supporting reference material.
• Optional offline evidence-precheck source where the licensed delivery allows execution; not an automatic artifact scanner.

Expected Deliverables

• coverage_bridge: query spend, campaign Search subtotal, reported gaps, tolerance, and reconciliation status
• intent_table: original query, normalized cluster, scope IDs, intent, relevance evidence, and outcome maturity
• protected_inventory: paid-sale/qualified-lead examples, owner keep terms, and strategic exceptions
• negative_candidates: phrase/operator syntax, narrow scope, rationale, confidence, evidence refs, and approval status
• collision_matrix: candidate, protected query, collision reason, matcher limitation, and required review
• exposure_estimate: distinct historical rows, comparable spend, overlap removed, and useful-traffic risk
• keep_and_investigate: explicit retained queries, missing evidence, owner question, and next diagnostic
• canary_packet: exact diff, control segment, observation window, rollback phrases, and separate write approval

How The Workflow Proceeds

1. Validate scope and report completeness; independently aggregate query spend and bridge it to the comparable Search subtotal. Retain unreported or filtered spend as a named coverage gap, not invented query rows.
2. Preserve original query text and create a separate analysis form for whitespace/case grouping. Retain morphology, operators, word order, and source row IDs; clustering is not a platform matching simulator.
3. Join queries to campaign/ad-group scope and available paid outcomes, recording duplicate and unmatched keys. Label zero observed outcomes separately from unavailable or immature outcomes.
4. Classify each query as offered commercial intent, unsupported product/service/geography, informational, employment, competitor, or ambiguous. Explain why that category is or is not relevant to this owner's business.

The remaining workflow covers validation, failure recovery and an evidence-linked handoff.

Bring These Inputs

• SEARCH_QUERY_PERFORMANCE_REPORT export with campaign/ad-group IDs, query text, targeting context, dates, spend, clicks, goal definition, and row coverage metadata.
• Campaign/account control totals and reporting basis: timezone, currency, VAT, attribution, date basis, and conversion maturity evidence.
• Current negatives at each available scope, active keywords/autotargeting context, and their export timestamps.
• Owner-confirmed assortment, services, geography, exclusions, and business economics; do not infer inventory solely from missing web pages.
• Protected queries and patterns from paid orders, qualified leads, strategic brand terms, and the owner's explicit keep list.

Scope And Compatibility

This is a workflow toolkit used by your connected AI agent, not a hosted service, installed API client, autonomous executor or security/compliance certification. You supply the model, authorized tools and project evidence. It does not provide access to advertising, analytics, billing or production accounts. No business outcome or token saving is guaranteed.

Agensi delivery is agent-only. Do not save or export the proprietary source. If local helper execution is unavailable, the agent follows the review workflow and marks the helper NOT_EXECUTED; no local-script execution is promised for this channel.

Validation Transparency

Packaged offline supplied-evidence checks: PASS. Retained synthetic text-response cases: 10/10; scope PASS_SCOPED. These tests are not real-client task trials or marketplace-runtime certification. A documentation-only license clarification followed the exact-text run; this is recorded separately.

Commercial Use And Support

Use project-specific outputs in personal, business and client work under the included Commercial License 1.0. Historical MIT and third-party rights remain unchanged; mandatory marketplace terms prevail. No proprietary source redistribution is included. Publisher: Ivan Srednoff / Srednoff.Agency.

FAQ

Will it make changes automatically? No. Analysis does not authorize spending, publication, deployment or account mutations.

Are examples client results? No. Examples are explicitly synthetic DEMO DATA.

Does it require Srednoff OS? No. The workflow is standalone; external tools and subscriptions are not included.

Is the launch price a proven optimum? No. It is a scoped introductory price informed by comparable offers, not sales forecasts or fake discounts.