Step 1: Customer Research That Fueled Everything

Before we touched spend, we went deep into the voice of the customer.

→ Analyzed thousands of brand + competitor reviews
→ Identified emotional buying triggers, repeat purchase drivers, and objections
→ Mined Facebook groups and forums for language patterns and unmet needs
→ Used post-purchase surveys to ask: “What nearly stopped you from buying?”

The result?
A content and messaging strategy built entirely around what customers already wanted to hear.

📌 Try this:
→ Review your top 3 competitors on Trustpilot or Amazon
→ Highlight recurring pain points or motivations
→ Use those insights to craft 2 new ad hooks or landing page headlines this week

2. Fixing What Was Leaking Conversions

We didn’t try to scale traffic until the site could convert it.

→ Site speed: Desktop 32 → 96 | Mobile 13 → 66
→ Rebuilt navigation with simplified, conversion-focused categories
→ Reworked PDPs with lifestyle, UGC, and product-in-use visuals
→ Tested advertorials and ambassador-style landers - outperformed PDPs by 18%
→ Implemented AOV boosters: bundle offers, free shipping thresholds, and tiered discounts
→ Introduced upsells/cross-sells at checkout based on user behavior

📈 Result:
→ +23.88% conversion rate
→ +18% AOV
→ Significantly higher revenue from the same traffic

📌 Try this:
→ Add a “Spend X, Get Y” incentive and monitor basket size impact
→ Run heatmaps on your PDPs and checkout flow to identify friction
→ Ask: “What almost stopped you from buying?” and turn that into a pre-purchase objection-buster

3. Paid Media That Scaled With Control

The ad accounts were bloated, with no naming conventions, campaign overlap, and no testing logic.

We rebuilt them from the ground up:
→ Consolidated campaigns to eliminate overlap and regain control
→ Allocated 20% of the budget permanently to structured creative testing
→ Transitioned Google into Performance Max with tighter audience signals + exclusions
→ Built a testing > scaling > evergreen framework across Meta
→ Used manual bidding + cost caps to stay profitable while scaling
→ Weekly audits of Meta breakdowns + Google search terms to kill wasted spend fast

📌 Try this:
→ Launch a separate campaign just for creative testing
→ Kill your worst 10% spenders and reallocate to winning campaigns
→ Create a campaign naming convention that’s clear and scalable

4. Creative That Multiplied Output (Without More Work)

Before: 5 creatives refreshed monthly.

After: A modular system that produced 20+ high-performing variations per week.

→ Hook, visual, CTA, and offer became swappable building blocks
→ Each ad mapped to a distinct persona and stage of awareness
→ Performance-informed briefs only (no guesswork)
→ Top-performing angles (e.g. “Softest fabric ever” or “Luxury feel, everyday wear”) were recycled, refreshed, and scaled

This wasn’t a UGC dump.

It was systematic, scalable creative with data behind every iteration.

📌 Try this:
→ Break your best-performing ad into 3 parts: hook, asset, CTA
→ Create 4 new versions by swapping just one piece at a time
→ Set a weekly cadence: 3 new variations + 1 refresh of a proven winner

5. Creative + Media Working as One

Most brands have separate creative and media buying teams that don’t really communicate.

We created a tight feedback loop:
→ Weekly syncs between creative and media teams
→ Media flagged performance trends > creative rebuilt fast
→ Winning angles turned into new briefs, with variation baked in
→ Rolling testing ensured momentum never stalled

📌 Try this:
→ Have your media buyer send a weekly summary of top-performing angles
→ Use those to inform next week’s creative, no opinions, just data
→ Track fatigue early: Look at frequency + performance dip across placements

TL;DR:

This wasn’t a lucky spike or a one-off win.

It was the result of consistent, strategic execution:

→ Creative built from real customer insight
→ A site that converted more of the traffic they already had
→ A media buying structure built for scale
→ A feedback loop that made every part of the funnel stronger

Result: $240K → $1.19M/month in 9 months.