225% Increase in Conversion Rate For Swiss Furniture Startup

The Challenge

Pabio, a Swiss startup offering high-quality sustainable furniture on a subscription model, needed help acquiring new customers and improving their customer acquisition cost (CAC) to lifetime value (LTV) ratio.

The company faced a unique market opportunity: Switzerland's cities are full of workers on short-term contracts, and Swiss apartments generally come unfurnished. However, this also presented significant challenges:

• Creating awareness for an entirely new category – furniture rental subscriptions – in a market where customers were unfamiliar with the concept
• Competing with established budget furniture retailers in an already crowded marketplace
• Identifying and reaching customers who would be open to renting furniture rather than buying
• Inefficient campaign structure limiting machine learning capabilities
• Lack of conversion data granularity to optimize for true customer value

The Strategy

My approach focused on leveraging machine learning and AI to identify and attract customers most likely to embrace the subscription model, while implementing strategic optimizations to improve campaign efficiency.

Core Strategic Pillars

The strategy centered on three key areas:

• Audience Intelligence: Use Google's machine learning capabilities to identify behavioral and intent signals that indicate openness to furniture rental over traditional purchasing
• Messaging Optimization: Develop compelling value propositions around sustainability and cost benefits, specifically tailored for fixed-term workers in the DACH region.
• Campaign Architecture Overhaul: Consolidate and restructure campaigns to maximize machine learning effectiveness and reduce inefficiencies from oversegmentation

Implementation & Key Optimizations

1. Campaign Consolidation

The existing account structure was severely limiting performance. Pabio was running 120 active Google Ads campaigns, creating excessive fragmentation that prevented machine learning algorithms from gathering sufficient data in each campaign.

Solution: I consolidated the campaigns from 120 down to 16, organizing them around core themes rather than overly specific segments. This consolidation allowed each campaign to:

• Gather more conversion data for faster learning
• Reach critical mass for automated bidding strategies to perform effectively
• Reduce management overhead and improve efficiency
• Enable better budget allocation across high-performing themes

2. Enhanced Conversion Tracking

To optimize for quality over quantity, I worked with Pabio's team to upgrade their conversion tracking by adapting the website form and onboarding process:

"Just Looking" Option

Isolated the "just looking" option in the onboarding form, allowing the bid strategies to distinguish between users who were ready to subscribe versus those who were merely browsing. This enabled Smart Bidding to prioritize users with genuine subscription intent.

Monthly Budget Question

Introduced a "monthly budget" question to the user onboarding form. By combining this data with average conversion rates, we could optimize campaigns for estimated customer lifetime value rather than just lead volume. This allowed the algorithm to favor higher-value customers and improve CAC:LTV ratios.

3. Broad Match Keyword Strategy

Shifted to 90% broad match keywords for furniture-related search terms, moving away from the restrictive exact and phrase match strategy that had not been cost-effective.

This approach worked because:

• Strong conversion tracking data gave Google's algorithms the signals needed to identify relevant searches
• Audience behavioral signals helped identify users likely to be open to subscription models
• Broad match captured long-tail generic furniture terms that exact match was missing
• The system could learn from user behavior patterns to find qualified prospects beyond obvious keyword targets

Results

The strategic restructuring and optimization delivered substantial improvements across all key metrics:

• 76% reduction in cost per lead – Campaign consolidation and improved targeting dramatically improved efficiency
• 50% increase in new subscription revenue – Better qualified leads translated directly to business growth
• 30% increase in qualified leads from generic furniture keywords – Broad match strategy successfully captured previously untapped demand
• 225% increase in conversion rate – Enhanced tracking and audience targeting brought significantly more qualified traffic

Campaign Efficiency Improvements

Beyond the headline metrics, the consolidation strategy delivered operational benefits:

• Faster optimization cycles with concentrated data in fewer campaigns
• Improved Quality Scores across keyword portfolios
• Better budget allocation with clearer performance signals
• Reduced management time, allowing focus on strategic testing

Key Takeaways

This project provided valuable insights into optimizing campaigns for emerging subscription-based business models:

• Consolidation improves efficiency and scalability: Oversegmentation limits machine learning opportunities. Fewer, well-structured campaigns with sufficient data outperform many fragmented campaigns every time.
• Broad match + strong conversion data is powerful: When you have quality conversion tracking, broad match keywords combined with Google's audience signals can identify customers willing to adopt new purchasing behaviors (like subscriptions) for high-volume generic terms.
• "Prequalification" drives performance without complex integrations: Simple website form adjustments can have a direct impact on campaign efficiency without requiring complex offline conversion data integrations or CRM connections.
• Trust the machine learning: With proper data foundation and conversion tracking, automated bidding strategies can identify patterns and opportunities that manual optimization misses.
• Quality signals matter more than quantity: Tracking the right conversions (qualified leads vs. all leads) enables the algorithm to optimize for business value, not just volume.

Client Testimonial