# How to improve Klaviyo email performance with product intelligence > Your Klaviyo flows are solid. But if every abandoned cart email shows the same generic recommendations, you're leaving revenue on the table. **Author:** Ecaterina Capatina **Published:** April 17, 2026 **Tags:** Industry Tips, Product Recommendations --- # How to improve Klaviyo email performance with product intelligence Your Klaviyo account is set up properly. Abandoned cart flows are running. Welcome series is live. Post-purchase sequence is triggered. The automations work. The metrics are acceptable. Acceptable. Not great. Not transformative. Just acceptable. The problem isn't Klaviyo. It's what's inside the emails. ## The product content gap Open Klaviyo. Look at your abandoned cart email. What products does it show? The abandoned items, obviously. That's the trigger. But what else? A row of "You might also like" products that are either bestsellers (shown to everyone) or "frequently bought together" items based on simple co-purchase rules. Now look at your browse abandonment email. What products does it feature? The viewed products, plus… the same bestsellers. The same generic recommendations. The automation logic is personalized — the right email goes to the right person at the right time. But the product content inside those emails is barely personalized at all. This is the gap where revenue hides. And it's the gap that [Product Intelligence](/en/product-intelligence/) closes. ## What Product Intelligence adds to email Klaviyo excels at behavioral triggers and flow logic. It knows when to send and who to send to. What it lacks is deep product understanding — the ability to determine what to show based on individual customer behavior and granular product relationships. It's important to understand what Product Intelligence actually does: it analyzes your product catalog — purchase frequency, product attributes, category relationships, and repurchase cycles — to predict buying patterns and understand how products relate to each other. When combined with individual customer behavior, this product-level knowledge makes personalization sharper and more relevant. Product Intelligence adds three capabilities: **1. Individual-level product matching** Instead of showing "bestsellers" or "frequently bought together," Product Intelligence determines which specific products are most relevant to this specific customer based on their full behavioral history — searches, views, purchases, and click patterns. A customer who browsed three premium running shoes and then abandoned their cart shouldn't see budget running shoes as recommendations. They should see the premium trail socks, running vest, and GPS watch that complement the shoe they almost bought. The recommendations should reflect what the customer actually cares about, not what your store sells the most of. **2. Smarter recommendations for new customers** New customers present a challenge for personalization: without a behavioral history, most engines default to generic bestsellers. Product Intelligence changes this. Because Product Intelligence understands product-level patterns — what products are typically bought together, what categories have the strongest cross-sell relationships, how products cluster by need and use case — it can surface relevant recommendations based on just a few initial interactions. A new customer who views two or three products gives Product Intelligence enough signal to make meaningful recommendations, rather than showing the same top sellers everyone else sees. This means your welcome flow and early post-signup emails can carry genuinely relevant product content from the very first send. **3. Lifecycle-aware recommendations** Different customers need different products at different times. Product Intelligence understands the product catalog at a structural level — what's consumable, what has natural accessories, how long typical repurchase cycles are. Layered on top of a customer's purchase and browse history, this produces recommendations that reflect where that customer actually is. A first-time buyer gets relevant exploration. A repeat customer gets depth in their preferred categories. A lapsing customer gets re-engagement with fresh inventory that matches their established preferences. Product Intelligence adjusts what it recommends based on what the product data and the customer's history together indicate — not just what email flow they're in. ## Practical improvements for existing Klaviyo flows You don't need to rebuild your flows. You need to upgrade the product blocks inside them. | Flow | Before | After | |------|--------|-------| | Abandoned cart | Cart items + bestseller recommendations | Cart items + personalized alternatives (in case the original choice isn't quite right) + complementary products based on Product Intelligence | | Browse abandonment | Viewed products + generic "trending now" products | Viewed products + similar products from a different angle (different brand, price point, style) + products from adjacent categories based on behavioral patterns | | Post-purchase | Order confirmation + "Customers also bought" at day 14 | Order confirmation + product care tips (immediate value) + complementary recommendations at the right moment (varies by product type) + replenishment reminder if consumable | | Win-back | "We miss you" + bestseller showcase | "Here's what's new since your last visit" + personalized new arrivals matching their preferences + price drops on previously viewed products | **The key insight for abandoned carts:** an abandoned cart sometimes means "I wasn't sure about this product" rather than "I forgot." Showing alternatives alongside the abandoned items addresses the uncertainty that caused the abandonment. **Browse abandonment** is research behavior. The customer is exploring, comparing, and evaluating. Your email should support that process by expanding their consideration set with intelligent alternatives. **Post-purchase timing** matters more than most teams realize. Suggesting running socks the day after someone buys running shoes is too soon — they probably already have socks. Suggesting them three weeks later, when they've broken in the shoes and are ready for their next run, hits the right moment. **Win-back emails** fail when they show the customer the same store they left. Product Intelligence ensures the email highlights what's genuinely new and relevant to their specific interests. ## Connecting Klaviyo to Product Intelligence — and going further with Product Agents Hello Retail connects to Klaviyo in two ways, and it's worth understanding the distinction. For existing flows — abandoned cart, browse abandonment, win-back — the integration works by adding dynamic product blocks to your existing Klaviyo templates. When the email renders, the product block queries the Product Intelligence engine with the recipient's behavioral profile and the email's context. The engine returns the most relevant products for that specific customer at that specific moment. Your existing flow logic stays intact. Your triggers don't change. Your segments don't need rebuilding. [Product Agents](/en/product-agents/) are different. Rather than [enriching existing flows](/en/blog/2026-02-02-introducing-product-agents/), they introduce a fundamentally different model: **autonomous agents that decide on their own when to send**. With a standard Klaviyo flow, you define the trigger and the timing. With Product Agents, the agent monitors product and customer signals continuously and determines when a message is worth sending — without you setting a schedule. This is particularly powerful for scenarios where the right timing is genuinely uncertain: - **[Replenishment Reminder](/en/blog/2026-04-09-replenishment-reminders-complete-guide/)** — sent when a customer is likely running low on something they previously purchased, based on expected repurchase behavior rather than a fixed delay - **Recommended Add-ons** — sent after a purchase when the product data suggests accessories or complements would be relevant, at the moment they're most likely to land - **Alternative Picks** — sent when a customer could benefit from exploring comparable options based on their purchase or browse behavior - **[Price Drop Agents](/en/blog/2026-02-21-price-drop-alerts-klaviyo/)** — notifies customers when products they've viewed, purchased, or that are similar to products they care about drop in price Product Agents run through a dedicated Klaviyo flow — separate from your existing automations — triggered by Hello Retail events. Setup is done once; after that, the agents run themselves. ![A Klaviyo flow triggered by Hello Retail, showing the trigger step and email action with a live status](/images/product-agents/klaviyo-flow-integration.webp) This is not about replacing your flows. It's about adding a layer of outreach that your flows can't generate, because standard flow logic can't know when the moment is right. For stores exploring price drop alerts and replenishment reminders, Product Agents add entirely new [trigger types that Klaviyo's native functionality doesn't provide](/en/blog/2026-04-08-triggered-emails-ecommerce-guide/). ## Measuring the improvement The beauty of upgrading email content while keeping flow structure the same is that measurement is straightforward. A/B test the product blocks. Send 50% of each flow with the existing product recommendations and 50% with Product Intelligence-powered recommendations. Compare: - Revenue per email - Click-through rate on product recommendations - Add-to-cart rate from email clicks - Average order value from email-attributed purchases The flow logic, send timing, and audience are identical in both variants. The only variable is the product content. This isolates the impact cleanly. For a framework on measuring broader personalization impact, see our guide on measuring personalization ROI. ## Key takeaways - The gap in most Klaviyo programs isn't the flow logic — it's the product content inside the emails, which is often generic bestsellers rather than personalized recommendations - Product Intelligence analyzes product catalog data to predict buying patterns; combined with customer behavior, it produces sharper recommendations — including for new customers with little behavioral history - You don't need to rebuild flows — upgrade the product blocks inside them and A/B test the impact - Product Agents go further: autonomous agents that decide when to send, running through their own dedicated Klaviyo flow, covering replenishment, add-ons, alternatives, and price drops - The measurement is clean: same flow, same audience, different product content. Compare revenue per email. --- *This content is from the Hello Retail blog. For the full experience with images and formatting, visit [helloretail.com/en/blog/2026-04-17-improve-klaviyo-email-performance](https://helloretail.com/en/blog/2026-04-17-improve-klaviyo-email-performance)*