Google Ads Lead Generation Case Study in Pakistan

The Client: B2B IT Services Company in Karachi

A Karachi-based IT services company providing enterprise software solutions, cloud migration, and managed IT services to businesses across Pakistan. Their primary lead generation channel was Google Ads, where they were spending PKR 2.4M monthly across search and display campaigns targeting businesses in Lahore, Karachi, and Islamabad.

The company had been running Google Ads for two years. Volume wasn’t the problem — they were generating 200+ form submissions per month. The problem was quality. Only 12% of those leads converted to sales conversations, and just 6% eventually became paying clients. The sales team was drowning in low-intent inquiries that wasted time and compressed their capacity to pursue genuine opportunities.

The Problem: Volume Without Quality

Three interconnected issues were undermining their Google Ads performance:

1. Broken conversion tracking. The Google Ads account was tracking every form submission as a conversion — including spam, incomplete submissions, and job seekers filling out the contact form. Of the 200+ monthly “conversions,” roughly 40% were spam, 25% were unqualified (students, job seekers, or businesses outside their target profile), and only 35% were genuine business leads. The account was optimizing toward the wrong signal.

2. Campaign structure built on assumptions, not data. All campaigns used broad match keywords with no negative keyword lists. The same budget served both high-intent commercial queries (“enterprise cloud migration Pakistan”) and informational queries (“what is cloud computing”). Campaign targeting included all of Pakistan with no geographic bid adjustments, despite 73% of their clients being in Lahore and Karachi.

3. Single landing page for all campaigns. Every ad — across 8 campaigns targeting different services — directed to the same generic contact page. There was no message match between ad copy and landing page, no service-specific value proposition, and no lead qualification built into the form.

Phase 1: Conversion Tracking Audit and Cleanup (Weeks 1–2)

The first priority was fixing the data. Optimizing campaigns on corrupted conversion data would amplify waste rather than reduce it.

Spam filtering. We implemented honeypot fields and server-side validation on all forms. Honeypot fields are hidden form fields invisible to human users but visible to bots — when filled, the submission is flagged as spam. Within the first week, spam form submissions dropped from 80+ per month to under 5.

Conversion action redefinition. Instead of tracking raw form submissions, we created three conversion actions:

• Micro-conversion: Form submission (used for reporting only, not bidding)
• Primary conversion: Qualified form submission — form passes validation checks including company name, business email (no gmail/yahoo), and service interest matching campaign targeting
• Secondary conversion: Phone call lasting over 90 seconds (tracked through call extensions)

The primary conversion became the sole bid strategy signal. This single change meant Google’s algorithm started optimizing for qualified leads rather than raw form fills.

Impact of tracking cleanup:

• True monthly lead count dropped from 200 to 120 (honest data)
• But qualified lead count rose from 42 to 71 (the real metric)
• Bid strategy immediately shifted budget toward higher-quality traffic sources

Phase 2: Campaign Restructuring and Negative Keyword Build (Weeks 3–5)

With accurate conversion data flowing, we restructured the account to match actual buyer intent patterns.

Keyword strategy overhaul. We analyzed 18 months of search term reports and categorized every query by intent:

Intent Level	Example Queries	Previous Treatment	New Treatment
High (commercial)	“cloud migration service Pakistan”, “managed IT support Lahore”	Broad match, same campaign as informational	Exact + phrase match, dedicated campaign with higher bids
Mid (problem-aware)	“best cloud solutions for SME Pakistan”, “IT infrastructure upgrade cost”	Mixed with high-intent	Dedicated campaign with educational ad copy and content offer
Low (informational)	“what is cloud computing”, “IT services meaning”	Same budget as commercial queries	Excluded via negative keywords

Negative keyword build. We compiled a negative keyword list of 847 terms from search term reports — job-related queries, location-irrelevant queries, competitor brand terms with no conversion history, and informational queries from students. This alone reduced wasted spend by PKR 380K per month.

Geographic bid adjustments. Analysis showed that leads from Lahore and Karachi converted at 2.3x the rate of leads from other cities. We applied +40% bid adjustments for Lahore and Karachi, -30% for cities with historically low conversion rates, and excluded locations with zero conversions.

Ad group structure. Instead of 8 campaigns with 3–5 ad groups each, we rebuilt around service-specific themes with tight keyword-ad-copy-landing-page alignment. Each ad group contained 5–8 closely related keywords served by 3 responsive search ads with matching messaging.

Phase 3: Landing Page Optimization and Lead Scoring (Weeks 4–7)

Campaign restructuring sent better traffic, but the landing pages needed to convert that traffic at a higher rate.

Service-specific landing pages. We built dedicated landing pages for each of their four core services — cloud migration, managed IT, cybersecurity, and enterprise software. Each page included:

• Service-specific headline matching the ad copy
• Three-benefit structure tied to the keyword intent
• Client logos from relevant industries
• A qualification-focused contact form (company size, current infrastructure, timeline)
• Trust signals: certifications, case study links, team credentials

Lead scoring integration. We connected the form submissions to their CRM (HubSpot) with a simple lead scoring model:

• Company size over 50 employees: +20 points
• Business email domain (not gmail/yahoo): +15 points
• Specific service interest matching campaign targeting: +10 points
• Requested timeline under 6 months: +10 points
• Budget range indicated: +10 points

Leads scoring above 50 points were routed directly to the senior sales team. Leads between 25–50 went to the inside sales team for qualification calls. Leads below 25 received automated nurture emails.

Landing page results:

• Overall form conversion rate improved from 3.2% to 5.8%
• Service-specific pages converted at 6.4% versus 3.2% on the generic page
• Lead scoring reduced unqualified leads reaching sales team by 63%

Phase 4: Bid Strategy Optimization and Scaling (Weeks 6–12)

With clean data, structured campaigns, and converting landing pages, the final phase focused on scaling what worked.

Automated bidding migration. We migrated from manual CPC to Target CPA bidding using the qualified lead conversion action. The algorithm had clean data, sufficient volume (70+ qualified conversions/month), and clear signal direction. CPA stabilized within 3 weeks.

Audience layering. We added remarketing lists for search ads (RLSA) to re-engage site visitors who hadn’t converted. Combined with customer match lists from their CRM, we created bid-only audiences that increased conversion rates by 18% for returning searchers.

Budget reallocation. With clear performance data across campaigns, we shifted budget from underperforming service lines to the top two performers. Cloud migration and managed IT services received 65% of total budget (up from 40%) because they delivered 78% of qualified leads.

Final Results at 90 Days

Metric	Before	After	Change
Cost per qualified lead	PKR 4,200	PKR 2,898	-31%
Monthly qualified leads	85	142	+67%
Lead-to-sale conversion rate	12%	21%	+75%
Sales time on unqualified leads	60%	22%	-63%
Monthly ad spend	PKR 2.4M	PKR 2.4M	Reallocated
Spam form submissions	80+/month	<5/month	-94%
Negative keyword count	0	847	New

What Made This Work

Three factors drove the results:

1. Conversion data integrity. The single biggest impact came from fixing conversion tracking. When the bidding algorithm started optimizing for qualified leads instead of spam form fills, performance improved immediately. Every Pakistani business running Google Ads should audit their conversion tracking before changing anything else.

2. Intent-aligned campaign structure. Treating all search queries equally — from “what is cloud computing” to “cloud migration service Pakistan” — guaranteed waste. Restructuring around intent levels and dedicating budget to high-commercial-intent queries was the second-largest improvement.

3. Sales-marketing feedback loop. The lead scoring model wasn’t static. We reviewed lead quality with the sales team every two weeks and adjusted scoring criteria based on which leads actually closed. This feedback loop ensured that marketing optimization stayed aligned with sales reality.

What Teams Can Apply

For Pakistani service businesses struggling with lead quality in Google Ads:

1. Audit your conversion tracking first. If you’re counting spam as conversions, you’re training the algorithm to find more spam.
2. Build negative keyword lists from search term reports — not from generic templates. Your business has specific irrelevant queries that no template will catch.
3. Create landing pages that match ad intent. A generic contact page converts at half the rate of a service-specific page with qualification built in.
4. Connect marketing data to sales outcomes. Without a feedback loop between sales results and campaign optimization, you’re guessing at what “quality” means.

WeProms Digital has applied this lead generation framework across IT services, healthcare providers, educational institutions, real estate developers, and professional services firms throughout Pakistan. The specifics change with each vertical, but the system remains the same: clean data, intent-aligned structure, converting pages, and a sales feedback loop.