35% Reduction in Cost Per Lead (CPL) in Google Ads via Funnel Optimization

1. Project Overview

Niche: Universal Commercial Sector (Services / B2B / Real Estate).

Pre-launch Status: At the start of the project, the client already had active advertising campaigns. However, the cost per lead (CPL) was steadily increasing. Business margins were shrinking, and acquiring new customers was on the verge of becoming unprofitable.

2. Identified Issues and Growth Opportunities

During a deep audit of the Google Ads account, our team discovered several critical factors leading to budget leakage:

1. Non-Target Traffic: A significant portion of the budget was spent on informational queries and ineffective placements within the Display Network (GDN).

2. Outdated Ads: Creatives had a low Click-Through Rate (CTR), which led to an increase in the cost of every click.

3. Lack of Segmentation: Ads were shown to all users without considering their socio-demographic characteristics or device preferences.

3. Goals and Objectives

Our primary objectives for this campaign were:

CPL Reduction: Decreasing the cost per target lead by 35% without losing lead volume.

Traffic Quality Improvement: Increasing the conversion rate (CR) of advertising campaigns.

Automation: Successfully transitioning to smart bidding strategies to stabilize performance.

4. Implementation: Step-by-Step

Step 1: Comprehensive Audit and Account «Hygiene»

We began by thoroughly cleaning the incoming traffic:

• Analyzed search term reports and expanded the negative keyword list by 40%, eliminating non-target impressions.
• Audited GDN and YouTube placements, blocking sites with «junk» traffic and low-converting mobile apps.
• Removed ineffective keywords with low quality scores.

Step 2: Creative Optimization and A/B Testing

To improve ad relevance, we updated the text and visual components:

• Implemented Responsive Search Ads (RSA), testing over 15 headline variations.
• Added all available ad extensions (sitelinks, callouts, structured snippets, lead forms), which increased the visual real estate of the ads and boosted CTR.
• Formulated stronger Unique Selling Points (USPs) focused on solving the specific pain points of the target audience.

Step 3: Precise Audience Tuning

We analyzed historical data in GA4 and adjusted bidding settings:

• Demographics: Increased bids for the most high-converting age groups and excluded segments that failed to generate leads.
• Devices: Reallocated budget toward devices (Mobile/Desktop) that showed a better cost-per-conversion metric.
• Geography: Optimized impressions by region based on purchasing power.

Step 4: Transition to Smart Bidding

Once the campaigns accumulated sufficient conversion data (at least 30-50 per month), we transitioned them to machine learning algorithms:

• Utilized the «Maximize Conversions» strategy with a Target CPA (tCPA).
• This allowed the system to determine the conversion probability for every single auction in real-time and set the optimal bid.

5. Final Results

After 4 months of systematic work, we achieved significant improvements in key metrics:

6. Verification Tools

To confirm the results, we utilized the following tools:

Google Ads: A dashboard screenshot featuring a chart where the «Cost» curve trends downward while the «Conversions» curve trends upward.

GA4 (Google Analytics): A summary table of traffic sources (Session source/medium) confirming the increased efficiency of the CPC channel.