How Nocepharm Increased Revenue by 10% using Predictive AI

Pharmacy e-commerce isn’t like selling clothes or gadgets. Shoppers come with very specific needs — they want fast, relevant, and trustworthy guidance. They often return repeatedly for the same prescriptions or health products. And for Nocepharm, an Italian online pharmacy built by pharmacists across four generations, this human behavior shaped every decision.

By combining Clerk.io´s AI-powered search and product recommendations, Nocepharm improved how customers find, evaluate, and reorder products — resulting in a 10% increase in revenue with predictive AI.

‍“In pharmacy e-commerce, customers don’t want to be sold to. They want confidence in their choice — AI gave us a way to do that online.”
— Nunzio Nocerino, Owner, Nocepharm

Customers wanted guidance —
not just a list of products

When users arrived on the Nocepharm site, many had a clear need —
a symptom, an active prescription, or a health concern. But the journey often stopped after finding the first product.

Customers struggled to discover:

• Relevant alternatives
• Complementary products
• Personalized reorder options if logged in

This wasn’t a lack of interest — it was a lack of relevant guidance.

“Our customers come with specific health questions. They don’t browse — they want the right answer quickly.”
— Nunzio Nocerino, Owner, Nocepharm

The team realized that traditional bestsellers or manual rules weren’t helping. What shoppers needed was guidance at the point of decision.

AI Search + AI Recommendations — A combined approach that works

Rather than add more manual merchandising or rules, Nocepharm turned to predictive AI across two key areas:

🔍 AI-Powered Search

Understanding intent is critical — especially with health terms, synonyms, or typos. AI search helped customers reach relevant products faster and more reliably.

🤝 AI-Driven Recommendations

Nocepharm uses predictive AI to guide customers wherever key decisions happen throughout the webshop.

AI-driven recommendation banners adapt to real behavior and context across multiple touchpoints:

Homepage: surfaces relevant products early, helping users quickly orient themselves and find what matters most.

Category pages: make large assortments easier to navigate, helping users find the right products faster and reducing the need for customer support.

Product pages: suggests complementary and logical next products in context.

By learning continuously from browsing and purchase behavior, recommendations stay relevant without manual rules. The result is a shopping experience that feels helpful and consistent — from the first visit to repeat orders.

Personalization that supports repeat customers

Repeat orders are major in pharmacy. People come back for prescriptions or ongoing care products. Nocepharm took this seriously.

By personalizing recommendations for logged-in users, the store now:

• Surfaces reorder suggestions
• Shows relevant add-ons for medications
• Helps returning customers complete their carts faster

“For returning customers, personalization is about saving time — not pushing extra products.”
— Nunzio Nocerino, Owner, Nocepharm

This deepened loyalty and reinforced the sense that Nocepharm gets its customers.

Clear impact: +10% in revenue

The combined effect of better search and recommendations was measurable and meaningful:

✔ 10% increase in revenue
✔ 32% increase in average order value
✔ More comprehensive, relevant baskets
✔ Faster product discovery for new visitors
✔ Easier reorders for returning customers
✔ Less manual merchandising workload

“Analyzing search behavior helped us see opportunities we didn’t notice before — without compromising trust.”
— Nunzio Nocerino, Owner, Nocepharm

Unlike aggressive upselling, this uplift came from relevance and guidance.

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Why this approach works specifically in pharmacy
e-commerce

Health shoppers are different:

• They want answers, not options
• They return for the same product again and again
• They trust relevance over sales pressure

Clerk.io´s predictive AI meets these expectations by continuously learning from real user behavior — and applying those insights where it matters most.

Instead of generic lists, customers see suggestions that feel like expert guidance.

‍If you want to maximize your e-commerce performance, predictive AI and strategies like these can deliver real, measurable results.