🧠 Case Study 3 — AI E-commerce Personalization Engine
 

Overview:
An AI recommendation engine for e-commerce struggled to prove ROI and communicate value. Store owners didn’t understand how AI-driven upsells, predictive analytics, and personalization impacted sales. The agency built a multi-channel marketing system to increase installs and boost feature activation.

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Challenges Identified:

• Merchants didn’t understand the revenue impact.
   

• High churn from users who never activated core features.
   

• Ads weren’t highlighting revenue increases clearly.
   

• Weak onboarding and low product usage.
   

Agency Strategy & Execution:

Paid Acquisition:

• Instagram Ads for visual storytelling
   

• Twitter/X Ads focusing on revenue gains and AOV improvement
   

• Google Search targeting high-intent e-commerce merchants
   

High-Converting Landing Pages:
Interactive demos, before/after revenue examples, and strong “Install App” CTAs.

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Content Strategy:

Short-form videos, carousels, and posts explaining how AI boosts conversions, AOV, and customer retention.

 

Activation Campaigns:

Automated onboarding, analytics setup guides, and usage reminders.

Data Reporting:
AOV lift, conversion rates, activation metrics, churn, and total revenue impact.
 

Results:

• 3,450 new installs across Shopify and WooCommerce.
   

• 21% increase in AOV among active users.
   

• 33% reduction in churn from improved onboarding.
   

• $7.4M in additional revenue across merchants using the engine.
   

Key Takeaway:
With platform-specific ad strategies and conversion-focused onboarding, the AI personalization tool became essential for e-commerce stores aiming to increase revenue per visitor.