Client Success Story

We Transformed PriceLabs from 0.7 to 2.5 ROAS

How we rebuilt a chaotic $90K/month Google Ads account into a profitable $180K/month performance engine — achieving 350% ROAS improvement in just 9 months

2.5× ROAS Achieved
2× Budget Scaled
45% Cost Reduction
75% CTR Increase
56% Fewer Campaigns

PriceLabs × GrowthSpree — At a Glance

Client PriceLabs — dynamic pricing & revenue management software for short-term rentals
Problem A $90K/month Google Ads account stuck at 0.7 ROAS with conversion data inflated 5×
Result 2.5 ROAS (a 350% gain) on a $180K/month budget, at 45% lower cost per signup
Timeframe 9 months (Month 0 to Month 9)

The Challenge: A $100K/Month Account in Chaos

Despite spending nearly $100,000 monthly on Google Ads, PriceLabs' account was broken. ROAS was stuck at 0.7 (losing money on every dollar spent), and the account structure made optimization impossible.

What We Found: 7 Critical Issues

✕ 90+ campaigns running simultaneously with no strategic allocation
✕ 600+ competing bidding strategies fighting each other
✕ Conversion data inflated 5× — all optimization signals were wrong
✕ Quality Score of 5 causing higher CPCs and worse ad positions
✕ 700+ ad groups preventing machine learning optimization
✕ ROAS of 0.7 — losing 30 cents on every dollar spent
Baseline snapshot — Month 0 (before GrowthSpree)
Monthly ad spend ~$90,000/month on Google Ads
Cost per signup (CAC proxy) ~$100 per signup, with no reliable value attribution
Conversion / ROAS ROAS of 0.7 — every $1 of spend returned $0.70 in revenue
Tracking issues Conversion data inflated ~5× (≈500% variance); no offline conversion import from the CRM and no GCLID-level attribution, so bidding optimized on false signals

The 9-Month Transformation

We didn't just optimize campaigns. We rebuilt the entire account from the ground up with a systematic, data-driven approach.

ROAS & Spend Evolution: Month 0 to Month 9

ROAS and monthly spend by month (chart data in text)
MonthROASMonthly spend
Month 00.7$90K
Month 10.7$100K
Month 20.75$120K
Month 30.8$130K
Month 40.9$140K
Month 51.1$150K
Month 61.3$160K
Month 71.8$165K
Month 82.2$175K
Month 92.5$180K

Our 3-Phase Approach

The rebuild rested on four levers — campaign structure, bidding, landing pages, and conversion tracking. Here is what changed in each.

Campaigns

Consolidated 90+ fragmented campaigns down to 40, retired wasteful single-keyword ad groups, and built thematic groups with enough signal density for the algorithm to learn.

Bidding

Replaced 600+ competing bid strategies with value-based bidding tied to customer LTV, so spend chased revenue quality rather than raw conversion volume.

Landing pages

Optimized 25+ landing pages for message match and conversion, lifting landing-page conversion rate by about 30% and supporting a higher Quality Score.

Tracking

Rebuilt the data foundation with offline conversion import from the CRM and GCLID-level attribution, cutting conversion-data variance from ~500% to ~20%.

1
Stabilization & Structural Reform (Months 1-2)

The Problem: With 90+ campaigns and 600+ competing bidding strategies, the account was too fragmented for algorithms to learn. We needed to restore structural integrity before pursuing growth.

What We Did: Consolidated campaigns from 90+ to 60, eliminated wasteful SKAG structures, improved Quality Scores through ad relevance, and created thematic ad groups with sufficient signal density for machine learning.

5 → 8 Quality Score
$140K Monthly Spend
Stable Account Foundation
2
Data Intelligence & Value Optimization (Months 3-6)

The Breakthrough: We discovered conversion data was inflated by over 500%. Every optimization decision had been based on false signals. We rebuilt the data foundation completely.

What We Did: Implemented offline conversion tracking from CRM, established GCLID-level attribution, assigned differential values based on customer LTV, shifted to value-based bidding, and optimized 25+ landing pages for conversion.

~20% Data Variance (from 500%)
1.3 ROAS Achieved
+30% Landing Page CVR
3
Scalable Profitability (Months 7-9)

The Scale: With foundations strong and data accurate, we proved the account could maintain efficiency while doubling spend. ROAS improved even as we scaled — the hallmark of a truly optimized system.

What We Did: Completed campaign consolidation to 40 campaigns, deployed AI Max for incremental discovery, maintained efficiency despite 2-3× CPC increases, and achieved profitable scale at 2× the original budget.

2.5 ROAS Achieved
$180K Monthly Spend
-45% Cost per Signup

The Final Results: From Chaos to Profitability

350% ROAS Improvement
(0.7 → 2.5)
100% Budget Scaled
($90K → $180K)
45% Lower Cost per
Customer Signup
75% CTR Increase
(4% → 7%+)
60% Quality Score
Improvement (5 → 8)
56% Fewer Campaigns
(90 → 40)

The Business Impact

Beyond platform metrics, here is what the rebuild meant for revenue and acquisition economics after nine months.

Ad-attributed revenue ~$450K/month in ad-attributed revenue at Month 9 (2.5 ROAS × $180K spend), up from ~$63K/month at Month 0 (0.7 ROAS × $90K spend).
Revenue growth ~7× more ad-attributed revenue per month, from roughly $63K to $450K, while the account moved from loss-making to profitable.
CAC / cost per signup 45% lower cost per signup, from roughly $100 to $55, with value-based bidding weighting spend toward higher-LTV customers.
Profitable scale 2× budget deployed profitably — spend doubled from $90K to $180K/month while ROAS still rose from 0.7 to 2.5, the mark of a genuinely optimized account.

Before vs After: The Complete Picture

Key Metrics Transformation

Before vs after by metric (chart data in text)
MetricBefore (Month 0)After (Month 9)
ROAS0.72.5
Monthly spend$90K$180K
Cost per signup$100$55
Click-through rate4%7%+
Quality Score58
Active campaigns9040

Frequently Asked Questions

GrowthSpree helped PriceLabs scale its Google Ads budget by 2× while improving ROAS by 350% (from 0.7 to 2.5). The engagement also reduced customer acquisition costs by 45%, increased CTR by 75%, and consolidated active campaigns by 56% for better algorithmic learning.
The account was in chaos with over 90 campaigns, 600+ competing bidding strategies, and a low Quality Score of 5. Additionally, conversion data was inflated by 5×, meaning the ad platform's machine learning was optimizing based on entirely incorrect conversion signals.
GrowthSpree rebuilt the data foundation from scratch by setting up offline conversion tracking from the CRM, establishing GCLID-level attribution, assigning differential values based on customer LTV, and shifting the account to value-based bidding.
Phase 1 focused on stabilization and structural reform (consolidating campaigns and boosting Quality Scores). Phase 2 established data intelligence and value-based bidding. Phase 3 focused on growth, scaling budget profitably to $180K/month once the engine was verified.
PriceLabs is a leading provider of dynamic pricing and revenue management software for vacation rentals, short-term rentals, and hotels, founded in 2014.
It took nine months. ROAS held near 0.7 through the stabilization phase (Months 1-2), climbed to about 1.3 as data intelligence and value-based bidding took hold (Months 3-6), and reached 2.5 during profitable scaling (Months 7-9).
Cost per signup fell from roughly $100 to $55 by fixing conversion tracking, importing offline conversions from the CRM, and shifting to value-based bidding weighted by customer LTV. Spend then concentrated on higher-value customers instead of inflated or low-quality conversions.

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