
Every case study below is the result of RicoSystems actively rebuilding, optimizing, and managing paid campaigns for maximum performance and minimum cost. These are not just tracking results — they are optimization outcomes.
Direct screenshots from Google Ads and Analytics. No mockups, no stock images. These interfaces can't be faked.





Hospitality & Tourism
A boutique resort needed to forecast bookings with accuracy to support operational planning and revenue projections. Traditional marketing produced unpredictable results that made business planning difficult.
They required a system that could generate predictable customer acquisition costs across seasonal fluctuations.
Based on systematic testing and proprietary models, we projected:
B2B Technology
A SaaS startup needed to validate paid acquisition as a scalable growth channel. With limited budget, they required confidence that customer acquisition costs would support their unit economics.
They needed forecasted CAC before committing growth capital.
Based on cross-industry validation and business model analysis, we projected:
Learning phase (Days 1–14) shows lower performance that improves as algorithms optimize. Our models account for this maturation curve, providing accurate long-term forecasts from early-phase data.
Professional Services
A consulting firm needed qualified leads for high-value engagements. Traditional lead generation produced inconsistent results with unpredictable costs per qualified opportunity.
They required a system that could forecast lead acquisition costs to support sales pipeline planning.
Based on B2B service validation, we projected:
Retail
A high-end furniture retailer needed to scale sales by $200K/month. Traditional retail advertising produced unpredictable results that made inventory and operational planning difficult.
Required forecasted customer acquisition costs based on unit economics.
Based on cross-industry validation (hospitality, SaaS, services) and retail-specific modeling:
This validates our methodology's transferability to retail — the 4th distinct industry vertical. Demonstrates cross-industry applicability of our proprietary models. Full case study available Q2 2026.
(Without revealing our secret sauce)
We build statistical models based on empirical observation, not platform recommendations or industry "best practices." Our forecasts have confidence intervals backed by cross-industry validation.
We identify the highest-intensity customer needs in your market — not just product features. Creative assets address deep pain points, not surface-level messaging.
Every forecast is tested through controlled deployment. We measure actual vs. projected performance continuously, refining models based on real-world results.
Because our models produce predictable outcomes, we guarantee results within forecasted ranges. This isn't possible with guesswork — only with validated methodology.
The specific optimization techniques, platform mechanics, and strategic approaches that produce these results are proprietary intellectual property. That's why we can offer guarantees while competitors can't.
Different industries show different performance ranges — but all show predictable convergence within our forecasted parameters.
| Industry | CTR Range | CPC Range | Forecast Accuracy | Status |
|---|---|---|---|---|
| Hospitality | 3.5–5.0% | $0.08–0.12 | 96% | Validated |
| B2B SaaS | 1.5–2.5% | $0.12–0.18 | 90%+ | Validating |
| Professional Services | 1.8–2.2% | $0.18–0.24 | 94% | Validated |
| High-End Retail | 1.5–2.5% | $0.20–0.30 | TBD | Testing |
Every case study started with a forecast. Every client knew their expected results before committing budget.
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