Create a product recommendation feed in Klaviyo?

Setting up a personalized product recommendation flow in Klaviyo is an advanced marketing strategy that radically transforms the effectiveness of your customer communications. By automatically suggesting items perfectly tailored to each customer's individual preferences and behaviors, you create a customized shopping experience that drives repeat sales and strengthens brand loyalty. This detailed guide explains how to set up and optimize a product recommendation flow in Klaviyo to maximize your conversions and revenue.

Why product recommendation flows in Klaviyo are essential for your e-commerce strategy

Personalized product recommendations generate impressive business results, as confirmed by numerous studies:

• Significant increase in sales: According to McKinsey, 35% of Amazon purchases come from personalized recommendations.
• Major impact on average order value: Recommendations can increase the average order value by 10% to 30%.
• Improved customer engagement: Emails containing personalized recommendations achieve a click-through rate up to 300% higher than generic emails.
• Reduced churn rate: Customers exposed to relevant recommendations are 4.5 times more likely to return to your site.

A well-designed recommendation flow in Klaviyo leverages these opportunities by creating automated communications that present exactly the right products at the right time, based on each customer's unique profile.

Different approaches to generating product recommendations in Klaviyo

Klaviyo offers several methods for generating personalized recommendations, each suited to specific objectives:

1. Recommendations based on purchase history

This approach analyzes previous purchases to suggest complementary or similar products:

• Frequently bought together products: Suggests popular combinations.
• Replacement products: Recommends consumables to repurchase.
• Products from the same category: Offers items similar to previous purchases.

2. Recommendations based on browsing behavior

This method uses recent interactions on your site for relevant suggestions:

• Recently viewed products: Recalls items that sparked interest.
• Products similar to viewed items: Expands options within the same category.
• Abandoned content: Highlights products viewed but not purchased.

3. Recommendations via collaborative filtering

This advanced technique analyzes purchasing patterns across different customers:

• "Customers who bought this also bought...": Suggestions based on similar behaviors.
• Popular products in your segment: Items favored by similar customers.
• Personalized discoveries: Recommendations based on comparable purchasing profiles.

Technical setup of a product recommendation flow in Klaviyo: step-by-step guide

1. Prerequisites and data integration

Before creating your flow, ensure these fundamental elements are in place:

Setting up synchronization with your e-commerce platform

1. Verify that the integration between Klaviyo and your platform (Shopify, WooCommerce, Magento, etc.) is correctly configured.
2. Ensure that the complete product catalog is synchronized with Klaviyo.
3. Confirm that behavioral events (views, add-to-carts, purchases) are correctly tracked.

Organizing your catalog for optimal recommendations

For relevant suggestions, structure your product data carefully:

1. Precise and consistent product categorization.
2. Use of tags to identify important characteristics.
3. Implementation of key attributes (season, style, color, material, etc.).
4. Configuration of relationships between products (variants, collections, sets).

2. Creating the recommendation flow in Klaviyo

Follow these steps to configure your first recommendation flow:

1. In your Klaviyo account, go to "Flows".
2. Click on "Create Flow".
3. Select a trigger suitable for your strategy:
   • "Viewed Product" for navigation-based recommendations.
   • "Placed Order" for post-purchase suggestions.
   • "Time-based metrics" for recurring recommendations.
4. Configure appropriate filters (e.g., only customers who viewed but did not purchase).
5. Set the delay before sending the recommendation (often 24-48h for viewed products).
6. Name your flow explicitly (e.g., "Viewed Product Recommendations").

3. Configuring product recommendation blocks

To integrate dynamic recommendations into your emails:

1. In the Klaviyo email editor, search for and insert a "Product" block.
2. Select "Dynamic" as the source type.
3. Choose the appropriate recommendation method:
   • "Recently viewed products" by the recipient.
   • "Frequently bought with" the viewed/purchased item.
   • "Popular products" in the relevant category.
   • "Recommended products" based on the customer's overall profile.
4. Define the number of products to display (typically 3-5).
5. Customize the appearance and style of the product block.

4. Advanced personalization with dynamic variables

Leverage the power of Klaviyo variables to create ultra-personalized content:

Example code for contextual recommendations

{% if event.name == ‘Viewed Product’ %}

  <h2>Based on your interest in {{ event.item.product.title }}</h2>

  {% for product in event.item.product.recommendations limit:3 %}

    <div class=”product”>

      <img src=”{{ product.image_url }}” alt=”{{ product.title }}” />

      <h3>{{ product.title }}</h3>

      <p>{{ product.price|money }}</p>

      <a href=”{{ product.url }}”>View Product</a>

    </div>

  {% endfor %}

{% else %}

  <h2>Selected for you</h2>

  {% for product in person.recommended_products limit:3 %}

    <!-- Similar content for general recommendations -->

  {% endfor %}

{% endif %}

Message personalization based on context

{% if event.name == ‘Viewed Product’ %}

  You recently viewed {{ event.item.product.title }}. Here are other similar items you might like.

{% elif event.name == ‘Added to Cart’ %}

  The items in your cart would pair perfectly with these suggestions.

{% elif person.orders_count > 0 %}

  Based on your previous purchases, we think these products match your style.

{% else %}

  Discover our selection specially chosen for new members of our community.

{% endif %}

Advanced strategies to optimize your recommendation flows in Klaviyo

1. Sophisticated segmentation for targeted recommendations

Refine your approach based on different customer segments:

By engagement level

• Active customers: Recommendations for complementary or premium products.
• Occasional customers: Suggestions of bestsellers and high-converting products.
• Inactive customers: Recommendations of new arrivals or high-impact items.

By customer value

• High-value customers: Recommendations for premium and exclusive products.
• Medium-value customers: Suggestions for gradual upselling.
• Low-value customers: Entry-level recommendations and special offers.

2. Strategic timing of recommendations

Optimize the sending time of your recommendations:

• Immediate post-browsing (1-3h): For products with quick purchase decisions.
• Short delay (24h): For most navigation-based recommendations.
• Medium delay (3-7d): For post-purchase and cross-sell recommendations.
• Long delay (30d+): For consumable products with predictable repurchase cycles.

3. Systematic A/B testing for continuous optimization

Experiment methodically to maximize your performance:

• Number of products: Test different quantities (2 vs 3 vs 5 recommendations).
• Type of recommendations: Compare the effectiveness of different methods.
• Visual presentation: Evaluate various display formats for products.
• Contextual content: Test different wording and explanations.

4. Multi-channel integration of recommendations

Extend your recommendations beyond emails:

• SMS: Concise recommendations with direct links for high-intent products.
• Web push: Targeted suggestions based on recent browsing.
• Personalized website: Synchronization of recommendations on your site.
• Advertising remarketing: Alignment of recommended products in your ads.

Concrete examples of high-performing recommendation flows in Klaviyo

Case Study 1: High-end fashion site

A luxury clothing boutique implemented this strategy:

• Email 1 (24h after viewing): "Complete your look" with complementary products.
• Email 2 (7 days later): "Similar inspirations" with items of the same style.
• Recommendations segmented by viewed collection and purchase history.

Results: 28% increase in conversion rate and 34% increase in average order value thanks to complementary purchases.

Case Study 2: Natural cosmetics shop

A skincare brand adopted this approach:

• Recommendations based on identified preferred ingredients.
• Suggestions for a complete routine starting from a single viewed product.
• Smart reminders for consumable products (3 months after purchase).

Results: 42% increase in repurchase rate and 26% improvement in customer lifetime value.

Measuring and optimizing your recommendation performance

Key metrics to monitor in Klaviyo

To evaluate and improve the effectiveness of your recommendations:

1. Engagement indicators:
   • Open rate of recommendation emails.
   • Click-through rate on recommended products.
   • Click-through rate by recommendation position (1st vs 2nd…).
2. Conversion indicators:
   • Overall conversion rate of recommendations.
   • Revenue generated by recommended products.
   • Average order value including recommended products.
   • Sales attribution by recommendation type.
3. Product affinity indicators:
   • Conversion rate by product category.
   • Most effective product combinations.
   • Discovery of new categories via recommendations.

Analytical dashboards in Klaviyo

Create dedicated dashboards to track these metrics:

1. Go to "Analytics" in Klaviyo.
2. Configure custom reports focused on your recommendation flows.
3. Visualize performance by segment, recommendation type, and period.
4. Identify trends and optimization opportunities.

Conclusion: transforming the shopping experience through personalized recommendations

Setting up a product recommendation flow in Klaviyo represents a major evolution in your marketing strategy. By leveraging the rich behavioral and transactional data of your customers, you create a personalized shopping experience that simulates the attention of an expert salesperson who perfectly knows each customer's tastes and needs.

This data-driven approach not only generates immediate business results in terms of incremental sales and average order value but also helps build a lasting customer relationship based on relevance and added value. Personalized recommendations transform your marketing communications into true value-added services for your customers, thereby strengthening their positive perception of your brand.

By implementing the techniques and best practices presented in this guide, you will be able to develop sophisticated recommendation flows that continuously refine and generate significant additional revenue. The large-scale personalization offered by Klaviyo allows you to create a premium experience for every customer, regardless of the size of your database.

Start configuring your recommendation flows in Klaviyo today and observe the positive impact on your sales, customer engagement, and overall growth.