AI Search term analysis
Analyzes real user search queries that trigger ads, helping identify irrelevant traffic, improve targeting, and uncover high-intent keyword opportunities.
Search terms
Search terms are the actual phrases users type before clicking an ad. In Google Ads, they help identify irrelevant traffic, wasted budget, and opportunities for better targeting.
Main pain
Managing search terms manually is repetitive, time-consuming, and error-prone. Irrelevant terms can continue spending budget before being detected and excluded.
Challenge
The challenge was to reduce the manual effort required to review large volumes of search terms. This required integrating AI models to assist in processing and structuring data at scale.
Solution
The system combines a structured setup flow with AI-assisted analysis to simplify ongoing optimization. Users define rules and provide examples, while AI models process large volumes of search terms, identify patterns, and generate suggestions for exclusions. This approach reduces manual effort while keeping the user in control of final decisions, enabling efficient and scalable search term management.
Setup flow
Connect account → define campaign rules → add examples → review suggestions → activate analysis
Ongoing flow
Monitor search terms → review analytics → adjust exclusions → reanalyze when needed
Evaluate the impact of irrelevant search terms by identifying problematic queries, affected impressions, and estimated wasted spend.
Monitor account connection status and track analysis execution across multiple accounts to ensure clear visibility into system activity and overall performance.
System in Action
Business context provides the foundation for search term analysis, combining company details and competitor information to shape how the system interprets queries and generates rules. The context can be established manually or inferred with AI, enabling more accurate and relevant analysis.
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Business Context Definition
Services and products define the business scope the system uses to interpret search terms and evaluate relevance. This structured context helps align analysis with what the business actually offers.
Service descriptions like “SEO” and “Website building” act as semantic anchors, allowing the system to interpret search intent in context. This enables more accurate detection of irrelevant queries and improves the precision of generated exclusion rules.
Rules define the system's decision logic for evaluating search terms and determining whether to keep or exclude them. This structured layer ensures that analysis follows clear, consistent criteria aligned with the business context.
Rules such as "Do not exclude competitor brand names" or "Exclude informational queries like 'what is' or ‘how to'" guide the system's interpretation of search intent. Based on these conditions, the system evaluates each query, preserves high-intent terms, filters out irrelevant traffic, and generates exclusion decisions that align with defined business goals.
Extra examples provide granular input that helps the system refine how individual search terms are evaluated across different scenarios. This layer improves consistency by handling edge cases and nuanced situations that are not fully covered by general rules.
Examples such as "Chair," "Room," or "SEO," marked for inclusion or exclusion, act as direct signals for how specific terms should be treated. The system uses these inputs to generalize patterns across similar queries, improving consistency in exclusion decisions and reducing errors in ambiguous cases.
The left table
evaluates individual search terms that triggered ads, analyzing their performance metrics and determining whether they should be included or excluded. Each row represents a real query, allowing precise, case-by-case decision-making.
The right table
breaks all search terms into individual words (N-grams) and aggregates them across the dataset to reveal recurring patterns. This allows the system to identify high-frequency words associated with low-quality or high-cost traffic and apply consistent decisions across similar queries.
How the system works
The analysis layer combines search term evaluation with pattern detection to support data-driven decision-making at scale. It processes large volumes of queries, measures their performance impact across impressions and cost, and provides structured insights for identifying irrelevant traffic, uncovering hidden patterns, and supporting more efficient optimization decisions.
TOGETHER
these two layers connect granular analysis with pattern-level insights. Individual decisions are supported by aggregated data, enabling the system to move from manual review to scalable, consistent optimization.
Adaptive Split View
The interface allows dynamic resizing of the two tables, enabling users to focus on either detailed search term analysis or aggregated N-gram insights without losing context. This flexible layout supports efficient comparison and adapts to different analysis scenarios.
Smart filtering combines conditions such as impressions, cost, category, and status to surface high-impact segments, enabling deeper pattern analysis and more precise identification of inefficient traffic.
Inclusion and exclusion define whether individual search terms are considered relevant or filtered out, shaping the overall quality of the dataset.
The negative planner defines which terms are grouped for structured exclusion, enabling consistent management of unwanted traffic patterns.
Negative Keyword Planner
The Negative Planner bridges the gap between analysis and execution by transforming insights into a structured, deployment-ready set of negative keywords for Google Ads.
Applied when a term consistently drives irrelevant traffic across multiple queries and contexts.
Exact match. Used when a highly specific query is irrelevant, while related variations may still perform well.
Phrase match. Used when a specific phrase signals unwanted intent, while broader queries may remain relevant.
AI Suggestions & Performance Insights
AI-generated suggestions provide continuous visibility into performance impact across connected accounts. This layer identifies wasted spend and surfaces optimization opportunities based on real-time analysis of search term data.
Is AI in the system fully autonomous?
AI supports large-scale analysis and pattern detection, but decisions remain transparent and controllable. The system is designed as a tool that augments decision-making, not replaces it.
Can AI make mistakes in the system?
Like any data-driven system, AI can misinterpret ambiguous queries or edge cases, especially when context is incomplete, signals are conflicting, or intent is not clearly defined across similar search terms.
How are AI mistakes prevented?
Structured inputs such as business context, services, rules, and examples guide the system's behavior, while transparent logic and manual controls ensure that every decision can be reviewed, adjusted, and refined.
How does the system ensure consistency across large datasets?
By combining rule-based logic with pattern-level analysis (N-grams), the system applies consistent decisions across similar queries rather than evaluating each term in isolation.
How much budget can this system save?
Savings depend on traffic quality and campaign setup, but in high-volume accounts, even a 5–15% reduction in inefficient spend can result in substantial impact. For a $1M annual budget, this may represent tens or even hundreds of thousands of dollars in recoverable value over time.