Overview

Zigwash is a laundry and dry-cleaning business operating across Noida and Greater Noida, offering services such as laundry pickup, dry cleaning, shoe cleaning, and franchise opportunities. Before starting Meta Ads campaigns, the brand relied heavily on Google for lead generation and had very limited consistency from paid social channels. Their advertising system lacked a structured testing framework, optimized creatives, and proper audience segmentation, which made it difficult to scale lead generation effectively. To solve this, the primary objective was to build a scalable Meta Ads system focused on generating both franchise inquiries and service-based leads for laundry and dry-cleaning services.

The Challenge

When the project started in February, the business faced several growth limitations:

• Most leads were coming only through Google
• Meta Ads were not generating consistent leads
• No clear creative testing structure existed
• Audience targeting was not optimized
• Different service categories were not segmented properly
• CPL consistency was missing

The goal was simple:

• Create a repeatable Meta Ads framework
• Generate quality leads consistently
• Reduce dependency on a single acquisition channel
• Scale both service and franchise inquiries

Strategy Implemented

1. Multi-Service Lead Generation Framework

The campaigns were divided based on services and objectives:

Service Categories Included

• Dry Cleaning
• Shoe Cleaning
• Laundry Services
• Franchise Opportunities

Instead of running generalized campaigns, separate testing structures were created for each category.

Video Tutorial : How to Run Meta Ads for Laundry & Dry Cleaning Business (Real Case Study) 

Creative Testing Strategy

To improve campaign performance, multiple graphical creatives were designed and tested across different audience segments. The strategy focused heavily on creative experimentation, offer testing, and service-specific messaging to identify what resonated most with the target audience. Different hooks, pricing angles, and promotional offers were tested to understand which creatives generated the highest engagement and lowest cost per lead.

The campaigns included separate creatives for services like dry cleaning, laundry pickup, and shoe cleaning, along with direct-response offer creatives focused on affordability and convenience. One of the highest-performing creatives featured the offer “Any Pair Shoe Cleaning at ₹399.” This pricing-focused approach significantly outperformed several generic branding creatives and became one of the strongest lead-generating assets during the campaign.

Audience & Location Testing

The campaign structure was designed around detailed location experimentation.

Different ad sets were created using:

• Pincode targeting
• Noida targeting
• Greater Noida targeting
• Broad local targeting

This allowed the team to identify:

• Which areas generated lower CPLs
• Which audience segments converted better
• Which locations produced higher-quality leads

The testing phase helped optimize the account based on actual conversion data rather than assumptions.

Campaign Execution 

Phase 1 — February Testing Phase

The first month focused heavily on:

• Creative testing
• Audience segmentation
• Location optimization
• Offer positioning

The objective during this phase was not aggressive scaling, but identifying winning combinations.

February Campaign Results

• Total Ad Spend: ₹20,000
• Leads Generated: 366
• Campaign Duration: February to mid-March
• Consistent campaign activity between February 8 and March 14

During this period, the team continuously analyzed:

• CPM
• Impressions
• CPL
• Creative performance
• Audience response rates

Scaling Phase

Once winning creatives and audience combinations were identified, campaigns were resumed and scaled further in April.

The focus during scaling was:

• Maintaining CPL consistency
• Increasing lead volume
• Improving campaign efficiency
• Scaling only proven creatives

Instead of making large budget jumps, campaigns were optimized gradually after identifying stable-performing ad sets.

Key Optimizations Applied

Structured Testing Framework

The account followed a systematic testing model:

• Test multiple creatives individually
• Identify winning creatives
• Scale successful ad sets only
• Eliminate underperforming combinations quickly

This prevented budget wastage and improved campaign efficiency.

Geo-Based Optimization

Through location-level testing, the campaigns discovered that:

• Certain pincodes performed significantly better
• Hyperlocal targeting reduced CPL
• Broad targeting alone was less effective compared to segmented local targeting

This became a major contributor to stable lead flow.

Content-Led Performance

The biggest learning from the campaign was that:

• Offer-driven creatives outperformed generic branding
• Clear pricing communication improved CTR
• Service-focused visuals increased lead quality

Instead of polished corporate creatives, practical and direct communication generated stronger results.

Results Achieved

Lead Generation Growth

• 366 leads generated in the initial testing phase
• Consistent lead flow established through Meta Ads
• Reduced dependency on Google-only acquisition

Performance Improvements

• Stable CPL maintained during scaling
• Multiple winning creatives identified
• Local audience segmentation improved efficiency

Business Impact

The campaigns successfully built a repeatable Meta Ads lead generation framework for:

• Franchise expansion
• Laundry service acquisition
• Shoe cleaning promotions
• Hyperlocal service marketing

What Made This Campaign Work

The success of this campaign was driven by a structured testing and optimization approach rather than relying on a single winning ad. Instead of using one creative or one audience segment, the campaigns continuously experimented with different hooks, offers, visual formats, service categories, and targeting combinations to identify what generated the best response. This consistent testing framework helped uncover high-performing creatives and audience segments while eliminating underperforming variations early in the process.

Another major factor behind the campaign’s performance was hyperlocal targeting. Different audience clusters across Noida and Greater Noida were tested separately, including pin-code-based targeting and location-specific ad sets. This allowed the campaigns to identify the most profitable regions and optimize budget allocation accordingly.

Offer-based creatives also played a critical role in improving lead acquisition efficiency. Direct-response offers, especially pricing-focused creatives such as shoe cleaning promotions, generated stronger engagement and better conversion rates compared to generic branding campaigns. These creatives gave users a clear reason to take action immediately, which helped reduce the overall cost per lead.

To maintain performance consistency, campaign scaling was handled in a controlled manner. Budgets were increased only after identifying stable-performing creatives and audience sets. This gradual scaling process helped maintain CPL stability while improving overall lead volume without negatively affecting campaign efficiency.

Conclusion

The ZigWash campaign demonstrates how a structured Meta Ads testing framework can help a local service business generate scalable leads consistently. By combining creative testing, hyperlocal targeting, offer-driven messaging, and structured scaling, the business successfully transformed Meta Ads into a predictable lead generation channel for both franchise growth and service acquisition.

The campaign proved that consistent optimization, data-driven decision-making, and localized audience targeting can significantly improve lead quality while maintaining stable acquisition costs. Instead of depending entirely on Google for inquiries, ZigWash was able to build an additional performance-driven lead source through Meta Ads that supported both customer acquisition and business expansion.

If you’re looking to generate consistent leads for your business through Meta Ads using proven testing and scaling frameworks, book a strategy call with our team to discuss how we can build a customized lead generation system for your brand.