Generative AI Product Recommenders: 3‑Week Lift Study

The technology of Generative AI is changing how businesses present product suggestions to their customers.

The actual conversion rate improvement remains unclear from this approach.

Boosta conducted a three-week controlled A/B test to evaluate the performance of traditional rule-based recommendations against AI-generated suggestions in both eCommerce and SaaS onboarding verticals.

The results were striking—and actionable. Here’s what we learned.

Why Generative Product Recommenders Are Booming in 2025

Traditional recommendation engines rely on filters, tags, and fixed rules.

Generative AI models (like LLMs and transformers) go a step further—they:

• Understand real-time user intent
• Analyze behavior + content context
• Suggest product combinations dynamically
• Generate descriptions or bundles on the fly

This shift from static to dynamic suggestions holds major promise—but only if it lifts performance.

What We Measured in the 3‑Week Test

The test setup:

• 2 eCommerce brands and 1 SaaS onboarding flow
• Split traffic 50/50 between control (rule-based) and test (generative AI)
• Used the same UI layouts
• Measured: CTR on suggestions, add-to-cart rate, conversion rate, and bounce reduction

Results Summary – Where AI Won

After 3 weeks, here’s what we saw:

✅ +14.6% conversion rate uplift in eCommerce

✅ +21% CTR on AI-generated suggestions

✅ -17% bounce rate when AI used natural language prompts

✅ The adoption rate of new features increased by 9.3% when SaaS onboarding included personalized feature bundling.

The AI-generated suggestions performed better on mobile devices than rule-based systems because they demonstrated improved context understanding and produced more suitable phrasing.

Why the Uplift Happened

Several factors drove the performance boost:

• Tone matching: Generative models adapted copy based on user behavior—e.g. playful vs technical
• Cross-pairing: Suggested bundles of complementary items users hadn’t considered
• Use-case personalization: For SaaS, the system suggested features based on business size, not just role
• Zero-state magic: On first load, AI could recommend based on intent without waiting for history

In short: AI knew what to say—and when to say it.

Key Takeaways for CRO Teams

1. Start with zero-state optimization Generative AI shines when users land on a page with no history—use it to reduce bounce.
2. Use prompt logic in SaaS onboarding Let the AI ask soft qualifiers (“Are you here for collaboration or personal tracking?”) to narrow suggestions.
3. Make your AI human-like Train it to mirror tone, shorten text, and match the user’s device behavior.
4. Run small, fast tests This 3-week experiment was enough to validate uplift. Don’t overbuild before proving ROI.

How Boosta Deploys AI Recommenders Today

At Boosta, we now integrate generative recommenders in:

• Shopify and custom eCommerce flows
• SaaS onboarding paths via embedded chat or in-product modals
• Email and push personalization logic
• In-app discovery sidebars and zero-state panels

We use conversion-driven prompts, design consistency, and fast A/B cycles to validate performance.

Conclusion

Generative AI recommenders represent more than a hype cycle because they function as a proven growth driver.

This 3-week study proved that with the right implementation, they can drive real gains in CTR, conversion, and feature adoption.

But like any tool, results depend on execution.

Start small, test fast, and tune your AI with the same care you give your UX.

In 2025, product discovery is a conversation. And now, AI is finally fluent in how to sell.