Search Query Mining and Negative Keyword Optimization in Pakistan
Every Google Ads account pays for searches it never meant to buy. Broad match reaches semantically related queries, exact match matches close variants, and Performance Max spends across inventory where you never chose a keyword at all. The search terms report is the only place that waste becomes visible, and even there it shows a filtered sample of what you actually paid for. Search query mining is the discipline of extracting that evidence, classifying every reported term against what your business actually sells, and converting the findings into layered negative keyword architecture that stops the same waste from returning.
WeProms Digital runs query mining and negative keyword engagements for advertisers across Lahore, Karachi, Islamabad, and the wider country. The work is narrow and surgical. We find where your ad budget is buying job seekers, freebie hunters, DIY researchers, and wrong-intent clicks, and we build the exclusion system that redirects that budget to searches with genuine commercial intent.
Why Wasted Query Spend Keeps Growing
Google’s matching system has spent years moving from words to meaning. Broad match now uses your landing page, ad assets, and historical signals to find searches it predicts may convert, including searches that contain none of your keyword words. AI Overviews and AI Mode push this further, because a conversational prompt can be reinterpreted before an ad auction happens, and the query shown in your report is not always the full story of what triggered the impression. Meanwhile Smart Bidding happily funds whatever the matcher serves it, because the matcher’s job is reach and the bid algorithm’s job is to spend toward its target.
The second problem is visibility. The search terms report omits queries that fall under privacy or volume thresholds, so the cost of reported terms is usually well below the cost of the campaigns that produced them. That gap is unclassified spend, and in an automated, semantic-matching account it grows silently unless someone reconciles the two numbers regularly. Most advertisers never do, because the report looks complete when you open it.
Pakistani accounts carry extra weight here. Traffic is overwhelmingly mobile, which means accidental taps and low-quality app placements alongside genuine intent. Queries arrive in a mix of Urdu and English, and employment-related searching is heavy, so an untended account collecting “jobs” and “salary” variants can bleed a meaningful share of a modest PKR budget before anyone opens the terms report.
How We Mine the Search Terms Report
Book a free strategy call - we'll audit your current setup and identify the highest-impact fixes.
The engagement starts with extraction, not opinion. We export search terms across every campaign, pull search category and asset group insights from Performance Max, and bring in placement and device reports where query-level data runs dry. Normalizing this into one view matters, because waste patterns that look small inside a single campaign are often obvious the moment the account is examined as a whole.
Classification comes next. Every reported term is sorted into intent families: commercial searches worth paying for, informational research, employment intent, support and login intent, freebie and DIY intent, competitor queries leaking into the wrong campaign, and queries for products or areas you simply do not serve. This is where local knowledge earns its keep. A price-check or installment query might be waste for one retailer and the best traffic another retailer has, so classification is done against your margins, your cash-on-delivery reality, and your actual catalogue, not a generic blocklist.
Then we reconcile. Comparing reported term spend against actual campaign spend per campaign quantifies the unclassified ratio, which tells us how much matching behaviour we cannot see directly and how conservative or aggressive the exclusion design needs to be. The output of this stage is a waste map: which intent families are consuming budget, in which campaigns, at roughly what cost, and what that implies for fixes that negatives alone cannot handle, such as landing page eligibility and campaign isolation.
Negative Keyword Architecture That Scales
Most accounts handle negatives reactively. Someone spots a bad term, adds it to one campaign, and the same term keeps spending in five others. We replace that habit with layered architecture built from shared lists, each owned by an intent family. A universal list carries the irrelevance that applies to every campaign, things like job, career, salary, free, and definition intent. A business-model list carries what is wrong for you specifically, such as wholesale, repair, rental, or second-hand if those are not what you sell. A brand-routing list keeps competitor and brand queries out of generic campaigns so they can be handled deliberately. A geo and catalogue list blocks locations, models, and service tiers you do not offer, and a dated test list stages risky additions so every change is reviewable and reversible.
Placement follows the platform’s actual mechanics. Shared lists are attached across campaigns so every new campaign inherits the full exclusion set on day one. Account-level negatives carry the universal rules into Search, Shopping, and Performance Max inventory. Performance Max also gets brand exclusions, which do a job negatives cannot, and campaign-level negatives where the account supports them. Demand Gen is treated honestly: it spends on audience and placement signals, so we configure audience and placement exclusions there rather than pretending keyword negatives control it.
Guardrails matter as much as coverage. Exact match matches close variants now, so a negative added on raw wording can block a valuable variant. We check whether a candidate term feeds converting traffic or a brand-defense route before excluding it, and we document every list so the reasoning survives staff changes on your side.
What Changes After the Engagement
The immediate change is where budget goes. Spend that was funding irrelevant intent families is released and redirects to the queries that were already working, without increasing your total budget. The query mix in your reports becomes readable again, because commercial intent is no longer buried under noise.
The structural change lasts longer. Shared lists mean the account’s exclusion logic lives in one maintained place instead of scattered across campaigns, so scaling spend, launching seasonal campaigns, or onboarding a new manager no longer rebuilds the same leaks. And the mining routine — weekly review of visible terms and search categories, monthly reconciliation of the unclassified ratio — becomes a standing operating habit, run by your team with our documentation or by us on retainer.
Who This Service Is For
How we helped a Pakistani business achieve measurable results.
This service fits advertisers with meaningful Google Ads spend and visible waste in their search terms report. That includes ecommerce brands running Shopping and Performance Max together, service businesses in Lahore and Karachi whose lead forms fill with job applicants, and accounts inherited from a previous agency where negatives were never structured at all. It is also the right engagement before scaling spend, because every rupee of budget added to an account with leaky exclusions multiplies the leak along with the growth.
