Client Results

Results we can put numbers on.

Every engagement below started with an audit, ended with measurable revenue impact, and left the client owning every system we built.

E-Commerce · Paid Media

DTC brand scales from 2.1× to 5.4× ROAS in 90 days

Situation

A direct-to-consumer apparel brand was running Google Shopping and Meta campaigns in-house. ROAS had plateaued at 2.1× for six months despite increasing budget. Attribution was last-click only, and the team had no visibility into which channels were actually driving purchases.

What we did

We replaced last-click attribution with a data-driven multi-touch model, revealing that 38% of budget was funding touchpoints with near-zero incremental contribution. ML bidding scripts were deployed across both platforms, reallocating spend toward high-intent signals in real time. A new creative testing cadence surfaced top performers within two weeks.

5.4×
ROAS at 90 days
up from 2.1× at engagement start
38%
Wasted spend recovered
redirected to top-performing segments
2 wks
Time to first optimization
ML bidding live within 14 days
+61%
Revenue per session
driven by creative and audience changes
B2B SaaS · Lead Funnels

SaaS company cuts cost-per-SQL by 71% with AI funnel

Situation

A B2B SaaS company was generating high demo-request volume from paid search but closing less than 4% of leads to opportunity. Sales was drowning in unqualified demos. The existing funnel was a single-field form with no qualification layer — anyone could book a call.

What we did

We replaced the generic demo form with a 5-step conversational qualification funnel. AI-driven branching logic segmented prospects by company size, use case, and budget fit — routing high-intent leads directly to sales and lower-intent leads into a nurture sequence. CRM automation was rebuilt to pass full context with every handoff.

71%
Cost-per-SQL reduction
qualified demo cost down vs. prior funnel
4.1×
Pipeline growth
qualified pipeline in 60 days
31%
Demo-to-opportunity rate
up from 4% pre-engagement
100%
CRM context coverage
every lead arrives with full qualification data
Multi-Location Services · AI Automation

Service franchise reduces ad overhead by 52% with ML allocation

Situation

A multi-location home services franchise was managing 14 separate Google Ads accounts manually — one per location. Campaign structures were inconsistent, budget decisions were made weekly in spreadsheets, and the marketing manager was spending 30+ hours per week on routine optimizations.

What we did

We consolidated reporting into a single analytics layer and built ML budget allocation scripts that redistributed spend across locations based on real-time demand signals, weather patterns, and historical close rates. Routine bid adjustments and budget shifts were fully automated. The team now reviews weekly summaries instead of managing daily.

3.8×
Blended local ROAS
across all 14 locations
52%
Management time saved
from 30+ hrs/wk to under 15
14→1
Reporting consolidation
single dashboard replacing 14 accounts
+29%
Lead volume
same total budget, smarter allocation

Client identities are anonymized. Results represent actual engagements and are specific to each client's starting conditions, budget, and market. Individual results will vary.

Want results like these?

Book a free strategy call. We'll audit your current setup and show you exactly where the leverage is — no pitch, just a roadmap.

Book a Free Audit