E-commerce Brand Saves 15 Hours/Week with AI Automation

Challenge

A fast-growing European fashion e-commerce brand was drowning in data.

The situation:

• €180,000/month ad spend across Google and Meta
• 3-person marketing team
• 67 active campaigns
• Managing: product feeds, creative testing, audience targeting, bidding, reporting
• Everyone working 60+ hour weeks
• Still couldn't keep up

The breaking point:

Their Head of Performance Marketing was spending:

• 12 hours/week building reports for management
• 8 hours/week analyzing campaign performance
• 6 hours/week on bid adjustments
• 4 hours/week on audience optimization
• 10+ hours/week firefighting issues

40+ hours on optimization. Barely any time for strategy or creative.

The team was burned out. Performance was plateauing.

They needed to either:

1. Hire 2-3 more people (€200K+/year cost)
2. Cut ad spend (not an option—busy season coming)
3. Find a better way

Solution

They implemented Cogny in November 2024.

Setup:

• Connected Google Ads (15 campaigns, 400+ products)
• Connected Meta Ads (52 ad sets, 200+ creatives)
• Connected GA4 via BigQuery
• Total setup time: 20 minutes

First Week:

AI generated 47 growth tickets covering:

• Budget reallocation opportunities
• Underperforming products to pause
• Audience overlap issues
• Creative fatigue alerts
• Bid optimization recommendations

The team spent 6 hours that week reviewing and implementing top tickets.

Weeks 2-4:

They established a routine:

• Monday morning: Review weekend performance + new tickets (30 min)
• Wednesday: Implement high-priority tickets (1-2 hours)
• Friday: Strategic review + planning (1 hour)

Total optimization time: ~4 hours/week

Down from 40 hours.

The Time Breakdown

Before Cogny (Weekly Hours)

Reporting: 12 hours

• Pull data from Google Ads, Meta, Shopify, GA4
• Build Excel reports
• Calculate custom metrics (ROAS, CAC, LTV)
• Create PowerPoint for management
• Explain numbers in meetings

Performance Analysis: 8 hours

• Check every campaign daily
• Look for anomalies
• Try to spot patterns
• Guess what's working and why

Bid Management: 6 hours

• Adjust bids based on performance
• Try to balance budget across products
• React to competition changes
• Manual ROAS optimization

Audience Management: 4 hours

• Create new audiences
• Test audience combinations
• Pause low performers
• Scale winners

Creative Management: 5 hours

• Track creative performance
• Spot fatigue patterns
• Rotate ads manually
• Brief designers on what to create

Firefighting: 5-10 hours

• "Why did spend spike yesterday?"
• "Why did ROAS drop in Campaign X?"
• "Which keywords are wasting money?"
• Endless Slack questions

Total: 40-45 hours/week (more than full-time job)

After Cogny (Weekly Hours)

Reporting: 30 minutes

• Open Cogny
• Export pre-built reports
• Done

AI handles:

• Data aggregation
• Metric calculation
• Trend analysis
• Anomaly detection

Performance Analysis: 1 hour

• Review AI-generated tickets
• Prioritize by impact
• Decide what to implement

AI handles:

• Deep-dive analysis
• Pattern detection
• Opportunity identification
• Recommendations

Bid Management: 0 hours

• AI monitors and recommends bid changes
• Execute AI suggestions (5 min)

AI handles:

• Continuous monitoring
• ROAS optimization
• Budget balancing
• Competition response

Audience Management: 1 hour

• Review AI audience insights
• Implement consolidation recommendations

AI handles:

• Overlap detection
• Performance tracking
• Expansion suggestions
• Scaling decisions

Creative Management: 1.5 hours

• Review creative fatigue alerts
• Plan rotation based on AI insights

AI handles:

• Fatigue detection
• Performance tracking
• Format analysis
• Refresh recommendations

Firefighting: 30 minutes

• AI alerts to critical issues
• Quick review and response

AI handles:

• 24/7 monitoring
• Anomaly detection
• Root cause analysis
• Solution suggestions

Total: 4.5 hours/week (90% reduction)

Results

Time Savings

Per week: 35-40 hours saved Per month: ~160 hours saved Equivalent: 1 full-time senior marketer

At €75/hour loaded cost: €12,000/month in labor savings

What They Did With The Time

Head of Performance Marketing:

• Finally had time for strategic planning
• Tested new channels (TikTok, Pinterest)
• Improved creative briefs based on AI insights
• Built better relationships with product team
• Actually took a vacation

Performance Marketer #1:

• Focused on creative strategy
• Worked with designers on better ad concepts
• Used AI insights to inform creative direction
• Set up new attribution models

Performance Marketer #2:

• Owned new channel expansion
• Built landing page testing program
• Set up email retargeting flows
• Explored influencer partnerships

The whole team:

• Work-life balance improved
• Burnout reduced
• More strategic, less tactical
• Better results

Business Performance

ROAS Improvement:

• Before: 3.8 overall ROAS
• After: 4.9 overall ROAS
• +29% improvement

How:

• AI found budget inefficiencies humans missed
• Faster response to changes
• Better creative rotation timing
• Optimized product mix

Ad Spend Efficiency:

• Same €180K/month spend
• +29% more revenue
• €890K/month → €1.15M/month

CAC Reduction:

• Before: €68 average CAC
• After: €52 average CAC
• 24% reduction

Customer LTV Increase:

• AI identified high-LTV customer sources
• Shifted budget to quality sources
• Average LTV: €280 → €320
• +14% improvement

Specific Examples

Example 1: The Friday Night Crisis That Wasn't

Before Cogny:

Friday 6 PM: CEO Slack message "Why did we spend €8,000 extra yesterday?!"

Performance marketer drops everything. Logs into Google Ads. Checks Meta. Looks at GA4. Spends 2 hours digging through data.

Finds the issue: A campaign budget doubled accidentally.

Fixes it. Writes explanation email. Ruined Friday night.

With Cogny:

Friday 9 AM: AI ticket generated "Campaign 'Winter Collection' budget increased 2x yesterday. Spend jumped €8K. ROAS maintained at 4.2. Keep or revert?"

Performance marketer sees ticket at 10 AM. Reviews: ROAS is good, just higher spend. Replies to CEO: "Budget increase was intentional for holiday season scale-up, ROAS is strong."

Total time: 5 minutes. No stress. No ruined evening.

Example 2: The Product That Wasn't Working

Before Cogny:

Product manager: "How's the new collection performing?"

Performance marketer: "Let me check..."

Pulls Google Ads data. Checks Meta performance. Looks at product-level ROAS in Shopify. Cross-references with GA4. Builds spreadsheet.

3 hours later: "Okay, here's the breakdown..."

With Cogny:

Product manager: "How's the new collection performing?"

Performance marketer: Opens Cogny, types: "Show performance by product collection"

30 seconds later: Full report showing:

• New collection ROAS: 2.1 (below target)
• Specific products dragging it down
• Which products to push harder
• Budget reallocation recommendation

Total time: 2 minutes.

Example 3: Creative Fatigue

Before Cogny:

They'd notice creative performance decline...eventually.

Usually 2-3 weeks after it started. By then, they'd wasted thousands on fatigued ads.

Manual tracking in spreadsheets never quite kept up.

With Cogny:

AI tracks every creative's performance trajectory.

When CTR drops 15% over 5 days: Ticket generated. "Creative 'Model-Lifestyle-Beach' showing fatigue. CTR down 23% from baseline. Rotate in backup creative 'Model-Lifestyle-City'."

They see it Monday morning. Swap creative in 10 minutes. Performance recovers.

Zero waste.

The Compound Effect

The time savings compounded:

More time for strategy → Better campaigns planned upfront Better campaigns → Less firefighting needed Less firefighting → More time for optimization More optimization → Better performance Better performance → More budget to manage efficiently

Instead of a vicious cycle of reactive firefighting, they entered a virtuous cycle of proactive improvement.

What The Team Said

"I was working 60-hour weeks and still felt behind. Now I work 40 hours, get better results, and actually have time to think strategically. Cogny gave me my life back."

— Head of Performance Marketing

"The best part isn't even the time savings. It's the mental load reduction. I'm not constantly worried that I'm missing something. The AI watches everything. I can actually focus."

— Senior Performance Marketer

"We almost hired two more people. Cogny costs us less than one junior marketer and does the work of three senior people. The ROI is insane."

— CMO

The ROI Calculation

Cost of Cogny: €1,200/month

Labor savings: ~160 hours/month × €75/hour = €12,000/month

Additional revenue: +€260K/month from 29% ROAS improvement

Total monthly value: €272,000+

ROI: 22,600%

Even if you ignore the revenue gains and only count time savings: €12,000 saved / €1,200 cost = 10x ROI on labor alone

Lessons Learned

1. Humans Shouldn't Do Machine Work

Monitoring 67 campaigns, 200+ creatives, 400+ products? That's machine work.

Humans should:

• Set strategy
• Create compelling creative
• Build customer relationships
• Make judgment calls

Machines should:

• Monitor everything 24/7
• Analyze patterns
• Spot anomalies
• Generate recommendations

2. Speed Matters

The faster you spot and fix issues, the less they cost.

AI spots issues same-day. Humans might spot them weeks later.

That speed compounds into huge savings.

3. Burnout Kills Performance

Their team was working hard but burning out.

Burned out marketers make mistakes. Miss opportunities. Take longer to recover from problems.

Well-rested, strategic marketers perform way better.

4. Time != Results

Before: 40 hours/week, mediocre results After: 4 hours/week, great results

More time spent doesn't mean better outcomes. Smarter analysis means better outcomes.

5. Scale Doesn't Require Headcount

They were about to hire 2-3 more people to scale.

AI gave them the capacity of multiple senior marketers. At 1/10th the cost.

Replicability

This works if you have:

✅ €50K+/month ad spend (enough data volume) ✅ 10+ campaigns (enough complexity that manual is hard) ✅ Small team feeling overwhelmed (3-5 people) ✅ Reporting taking significant time (5+ hours/week) ✅ Performance plateauing despite effort

Typical timeline:

• Week 1: Initial setup + quick wins
• Week 2-4: Team adjusts workflow, implements AI recommendations
• Month 2-3: Full time savings realized, performance improves

Expected outcomes:

• 60-80% time savings on optimization tasks
• 10-30% performance improvement
• Equivalent of 0.5-1.0 FTE capacity gained

What's Next For Them

With time freed up, they're:

1. Expanding to New Channels

• Testing TikTok Ads
• Exploring Pinterest
• Building YouTube strategy

Before: No time to test new channels Now: Bandwidth to experiment

2. Improving Creative

• AI insights inform what creative works
• Better briefs for designers
• More systematic testing

Before: Guessing what creative might work Now: Data-driven creative strategy

3. Building Better Customer Experience

• Time to optimize landing pages
• Improve post-purchase journey
• Build loyalty programs

Before: All hands on deck for ads Now: Can invest in full customer journey

4. Strategic Planning

• Actually plan next quarter (not just react)
• Scenario modeling
• Long-term optimization roadmap

Before: Firefighting mode constantly Now: Proactive strategy

Want Your Time Back?

If your team is spending 20+ hours/week on campaign optimization and reporting, you're wasting time and money.

AI can do in minutes what takes humans hours.

Your team should focus on strategy, creative, and growth. Not spreadsheets and firefighting.

See how much time you could save:

Schedule a demo and we'll analyze your current workflow.

We'll show you:

• Where your time is going
• What AI can automate
• Expected time savings
• How your team could reinvest that time

Most teams save 15-25 hours per week.

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About This Case Study

Written by the Cogny team—built by the founders who created AI optimization systems for Netflix, Zalando, and Momondo at Campanja, and scaled growth for Kry, Epidemic Sound, and Yubico through GrowthHackers.se.

Company details anonymized to protect client confidentiality. Time savings and results verified and representative of typical outcomes.

Last Updated: December 8, 2024