Client Goals

When this home accessories store approached us, they were already generating €150,000 in monthly revenue. However, they wanted to break through their growth plateau and scale significantly. Their goals were:

• Increase monthly revenue while maintaining profitability.
• Improve customer retention and lifetime value (CLV) and retention rate.
• Leverage data-driven insights to optimize marketing efforts (it means: increase the ROAS)

The Context

The store had a strong product catalog featuring stylish and functional home accessories, but they faced several challenges:

• Their marketing campaigns were generic and lacked targeting, leading to wasted ad spend.
• They didn’t fully understand their customer base or how to segment it effectively.
• Product recommendations on the website were static and irrelevant, resulting in missed upselling opportunities.

We knew that leveraging artificial intelligence (AI) would be key to solving these issues and unlocking their growth potential.

Solution

Over three years, we implemented a comprehensive AI-driven strategy focused on personalization, automation, and data analysis. Here’s how we did it:

Customer Segmentation with AI

We started by analyzing their customer base using AI-powered tools like RFM (Recency, Frequency, Monetary) analysis. This allowed us to group customers into meaningful clusters based on their buying behavior:

• Loyal Customers: Frequent buyers with high spending habits.
• Dormant Customers: Previously active buyers who hadn’t purchased in months.
• First-Time Shoppers: New customers who needed nurturing to become repeat buyers.

Applying RFM Customer Segmentation to Your Business right now

Come generare più vendite con la segmentazione dei propri clientiL’analisi RFM è un metodo pratico per trovare i tuoi migliori clienti, capire il loro comportamento e poi eseguire campagne di email/marketing mirate per aumentare le vendite, la soddisfazione e il valore della vita del cliente.Alex GenoveseAlex Genovese

Alex GenoveseAlex Genovese

Personalized Product Recommendations into Layout sections

We replaced the store’s static product recommendation engine with an AI-powered solution that dynamically suggested items based on browsing history, purchase patterns, and popular trends.

For example:

• A customer browsing kitchen accessories would see complementary products like tableware or storage solutions.
• Loyal customers received curated collections featuring new arrivals tailored to their past preferences.

This personalization significantly increased cross-selling and upselling opportunities. This can be easily achieved with a RAG that push your product recommendation rules into HTML sections when called.

👇Below the article about the AI chatbot for customer support:

Revolutionizing Customer Support: Building Intelligent RAG-Powered Retail Chatbots 🤖

Master the Art of Intelligent Retail Support: Step-by-Step Guide to Building RAG-Powered Chatbots Without Code

Alex GenoveseAlex Genovese

Automated ad campaigns through marketing data cycle

We implemented a marketing data cycle to automate advertising campaigns across platforms like Facebook and Google Ads:

1. Data Collection: AI analyzed customer behavior on-site (e.g., abandoned carts, viewed products) and off-site (e.g., email engagement).
2. Segmentation: Customers were grouped into clusters like “high-value shoppers” or “price-sensitive browsers.”
3. Ad Personalization: Each segment received tailored ads—for instance:
   • High-value shoppers saw premium collections with free shipping offers.
   • Price-sensitive browsers were targeted with discounts or bundle deals.
4. Optimization: AI continuously monitored ad performance and adjusted budgets in real-time for maximum ROI.

Retention-Focused Email Automations

To improve CLV, we created automated email workflows triggered by specific actions:

• A “Thank You” email with personalized product recommendations after every purchase.
• A re-engagement campaign offering discounts to dormant customers who hadn’t shopped in 90 days.
• Exclusive early access to sales for loyal customers as a reward for their repeat business.
• others...

Results

After three years of implementing these strategies, testing and improving month by month, the results were phenomenal:

The combination of AI-powered segmentation, personalized recommendations, and automated ad campaigns allowed the store to scale sustainably while maximizing profitability.

Example of AI Automation in Action

One standout example was how we used AI-driven automations for abandoned cart recovery ads:

1. A customer added a set of decorative cushions (€80) to their cart but didn’t complete the purchase.
2. Within an hour, the AI system triggered a Facebook ad showing the exact cushions alongside complementary items like throws and rugs.
3. The ad included a limited-time discount code (“Complete your look—10% off if you order within 24 hours!”).

This approach resulted in a 38% recovery rate for abandoned carts—far above the industry average of 20%.

FAQ

1. How long did it take to see results?

We started seeing improvements within six months of implementing the changes, but reaching €550,000/month took three years of consistent optimization.

2. What tools did you use for AI-driven personalization?

We used platforms like Nosto for product recommendations and Klaviyo for email automations tailored by customer segments.

3. Was this strategy expensive?

While there was an upfront investment in AI tools and setup, the return on investment (ROI) was significant—especially through increased revenue and reduced ad waste.

4. Can these strategies work for other industries?

Absolutely! The principles of segmentation, personalization, and automation are universal and can be adapted for any e-commerce business.

This case study demonstrates how leveraging AI can transform an already successful business into an industry leader by focusing on data-driven decisions and personalized customer experiences!