Client Results

Results we can put numbers on.

Every engagement started with an audit. Ended with a number we can defend. The client owns every system we built — no dependency on our access.

Client identities are anonymized. Results are actual.

5.4×
ROAS peak
paid media engagement
71%
Cost-per-SQL reduction
B2B funnel rebuild
3.1×
Mobile CVR lift
Core Web Vitals overhaul
$1.2M
Pipeline visibility
attribution engineering
DTC E-CommercePaid Media90-day engagement

$85k/mo spend · Google + Meta · 2.1× ROAS · last-click attribution

Six months of increased spend. ROAS went nowhere. Turns out 38% of the budget was funding channels that never drove a single purchase.

The Problem

The marketing director had been telling the board ROAS was "stable." What she didn't know: 38% of monthly spend was cycling through social touchpoints that never influenced a purchase — they just happened to appear before a conversion that was going to happen anyway. Attribution was last-click only, so the channel that got the final click took all the credit. The channels doing the actual work got defunded. She'd been optimising the wrong thing for six months.

What We Built

We rebuilt attribution before touching a single campaign. A data-driven multi-touch model exposed exactly where spend was leaking. Within 14 days, ML bidding was live across Google Shopping and Meta — reallocating budget toward high-intent signals in real time. A structured creative testing cadence identified winning formats in the first two weeks. No budget increase. Just the same money going to the right places.

“"Stable ROAS" meant we weren't looking hard enough. The data was there the whole time. We just weren't reading it right.”

— DTC E-Commerce Client

5.4×
ROAS at 90 days
up from 2.1× at engagement start
38%
Wasted spend recovered
redirected to top-performing segments
14 days
To first optimization
ML bidding live within two weeks
+61%
Revenue per session
driven by creative and audience changes
B2B SaaSLead Funnels60-day engagement

$40k/mo on paid search · 40 demos/wk · 4% close rate · one-field form

40 demos a week. 4% close rate. The problem wasn't the pitch — it was who they were pitching to.

The Problem

Their sales team was running 40 demos a week and closing barely 4% of them. Not because the product was wrong — it wasn't. Because the "Book a Demo" button on the website booked a demo with literally anyone who clicked it: students, competitors, people who confused it with another product, companies with no budget. The AE team was too professional to say it out loud, but they were burning most of their week on calls that were never going to close. One of them finally did say it. That's how this started.

What We Built

We replaced the single-field form with a 5-step conversational qualifier. AI branching logic scored each prospect on company size, use case, and budget fit before a calendar slot was ever offered. High-intent leads went straight to an AE. Everyone else entered a targeted nurture sequence. CRM automation was rebuilt so every handoff arrived with full qualification context — no more AEs doing discovery on calls that should have been emails.

“The AEs came back after week one and said the calendar felt completely different. Same number of demos booked. Just people we actually wanted to talk to.”

— B2B SaaS Client

71%
Cost-per-SQL reduction
qualified demo cost 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
Home Services FranchiseAI Automation4-month engagement

14 locations · $120k/mo · spreadsheet bidding · 30+ hrs/wk manual work

Every Friday at 4pm, the marketing manager opened 14 spreadsheets and manually adjusted bids. She'd been doing it for two years.

The Problem

One Google Ads account per location. Budget decisions made weekly, by hand, from last week's data. By the time a change went live the weekend demand surge was already over. Campaign structures had drifted across locations — some running smart bidding, some manual, some both. The marketing manager was spending 30+ hours a week on bid work that kept the engine running but left no time to actually grow it. She knew it was a problem. She just couldn't see a way out while doing it.

What We Built

We consolidated all 14 accounts into a single analytics layer and built ML allocation scripts that redistribute budget across locations in real time — weighing demand signals, local weather patterns, and each location's historical close rates. Routine bid adjustments became fully automated. The team now reviews one performance summary per day instead of logging into 14 separate dashboards. The 30 hours freed up went to strategy work that was previously impossible to schedule.

“I was spending every Friday doing something a machine should do. I kept telling myself the hands-on approach was the value-add. It wasn't.”

— Home Services Franchise Client

3.8×
Blended local ROAS
across all 14 locations
52%
Management overhead cut
from 30+ hrs/wk to under 15
14→1
Reporting consolidation
one dashboard replacing 14 account logins
+29%
Lead volume
same total budget, smarter allocation
D2C E-CommerceWeb Performance12-week build

$85k/mo paid media · 68% mobile sessions · 6.8s load time · 71% bounce rate

They'd fired two agencies for underperforming campaigns. Nobody had checked that the site took 6.8 seconds to load on a phone.

The Problem

Mobile was 68% of their traffic and 31% of their revenue. The gap had been growing for two years. Two agencies had been blamed for it, briefed on it, and replaced over it. Before touching a single campaign, we ran a real-device load test on a 4G connection. The homepage took 6.8 seconds. Product pages took longer. 71% of mobile visitors were leaving before they saw a product. $85k a month in ads was pointing traffic at a site that was bouncing most of it before anyone could buy anything.

What We Built

A full Core Web Vitals audit traced the problem to an unoptimised image pipeline, render-blocking third-party scripts, and zero edge caching. We rebuilt image delivery with next-gen formats and lazy loading, moved product pages to server-side rendering, and deployed Cloudflare Workers for edge caching. LCP dropped from 6.8 seconds to 1.4 seconds on a real 4G device. INP dropped from 420ms to 62ms. Not a single ad was changed during the entire 12-week build.

“We'd spent two years blaming campaigns for mobile numbers. The problem was the website. We fired two agencies for something that was never their fault.”

— D2C E-Commerce Client

3.1×
Mobile conversion rate
on identical traffic and spend
1.4s
LCP on 4G
down from 6.8s before engagement
96
Lighthouse score
avg across all product page templates
-83%
Bounce rate
on mobile product pages post-launch
B2B SaaSAttribution & Dashboards3-week build

4 ad channels · Google Sheet reporting · 8 hrs/wk to update · budget on gut feel

They'd been cutting LinkedIn's budget for a year because last-click made it look bad. It was actually their best-performing channel.

The Problem

Four ad channels. A Google Sheet that took 8 hours to update every Friday — when there was time. Budget decisions were made on last-click data and gut feel. The team had a nagging sense something was off with the numbers but couldn't prove it, so nothing changed. LinkedIn looked bad on every report, so its budget kept getting trimmed. What they didn't know: LinkedIn was responsible for a disproportionate share of the deals that actually closed. Last-click just couldn't see it because LinkedIn touches happened early in the journey, long before the final conversion.

What We Built

We built a Clickhouse + dbt pipeline pulling data from every ad platform, Salesforce, and Stripe into a single source of truth. A custom dashboard showed MQL, SQL, and ARR attribution by channel, cohort, and campaign — updating every 15 minutes. In the first week of live data, LinkedIn showed the highest pipeline-to-ARR conversion rate of any channel at nearly half the cost-per-opportunity. The team had been defunding their best performer for 12 months.

“First week of real attribution data was a genuinely uncomfortable conversation. We'd been punishing LinkedIn for a year for a problem that was our measurement, not their performance.”

— B2B SaaS Client

4.1×
LinkedIn ROAS revealed
previously underfunded based on last-click
8 hrs
Weekly reporting eliminated
live dashboard replaced the spreadsheet
$1.2M
Misattributed pipeline
identified in first 30 days of live data
3 wks
Build to deployment
from data spec to live production dashboard

Client identities are anonymized at their request. All results represent actual engagements and reflect each client's specific starting conditions, market, and budget. Individual results will vary.

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