In the age of data-driven advertising, personalization is no longer a luxury—it’s an expectation. One particularly effective strategy involves creating dynamic carousels tailored to specific customer clusters.

By leveraging machine learning and data integration tools, you can identify high-performing products for each cluster and dynamically advertise them to targeted audiences via platforms like Google and Facebook. Here's how you can approach this method to optimize your return on ad spend (ROAS) and enhance customer engagement.

1. Dynamic Carousels by Clusters

The foundation of this strategy is segmenting your audience into clusters based on purchasing behavior, demographics, or engagement metrics. Each cluster represents a group of customers with shared traits or preferences. For instance:

  • High-Spending Customers (VIPs)
  • Repeat Buyers (Loyal Customers)
  • Occasional Shoppers

For clusters large enough to create Lookalike Audiences (LAL), dynamic carousels can be used to propose personalized product feeds in ads. Here’s what to focus on:

  • Most Purchased Products Per Cluster: Machine learning models can analyze purchasing data to identify top-selling products within each cluster. These bestsellers are then dynamically showcased to similar potential customers.
  • Exploring Purchase Probabilities by Cluster: By utilizing predictive analytics, we can uncover which products each cluster is most inclined to purchase. This helps in tailoring ads to be incredibly relevant and engaging!

2. Frequently Bought Together: Item-to-Item Matrix

To further enrich dynamic product recommendations, analyze products that are frequently bought together. This approach involves:

  1. Exporting Order Data: Use tools like Integromat (Make) or custom scripts to export order data from your CMS—filter for orders containing at least two products to capture co-purchase patterns.

Building an Item-to-Item Matrix: Construct a matrix where each product is cross-referenced with others based on their co-occurrence in orders.

For example:

  • Product A is purchased alongside Product B in 30% of orders.
  • Product A is also bought with Product C in 15% of orders.
  1. Dynamic Feed Generation: Use these insights to generate carousel feeds showcasing complementary products. For instance, if a customer is browsing Product A, they might see Products B and C as suggestions.

This operation should be scheduled weekly or monthly to keep the recommendations fresh and relevant.

Most recurrent product bought together

3. Cross-Segmenting with Customer Data for Maximum Impact

For an even more precise approach, integrate your purchase data with customer email lists and segment them further. For example:

  • VIP Customers: Show them a dynamic feed of premium products or exclusive offers.
  • Loyal Customers: Advertise products frequently repurchased by this segment to maximize conversions.

This segmentation amplifies the power of Lookalike Audiences. For instance:

  • Ads targeting a VIP Lookalike Audience will showcase a VIP-specific product feed.
  • Ads targeting a Loyal Customer Lookalike Audience will feature a Loyal-specific feed.

4. Data-Driven Insights to Drive ROAS

The results are often remarkable when you align your ads with customer purchasing patterns. Here’s why:

  • Higher Conversion Rates: Research shows that existing customers have a 60-70% probability of making a purchase, compared to much lower rates for new customers.
  • Increased Engagement with Personalized Emails: 28% of customers are inspired to make purchases after receiving personalized emails with relevant recommendations.

By proposing products that align with customer habits, businesses can boost their ROAS by at least 300%, according to industry benchmarks.

Pro Tips

  • Automate Data Refreshes: Use workflows to update your dynamic feeds regularly, keeping your ads timely and accurate.
  • A/B Test Feeds: Continuously test different carousel structures and content to identify what resonates best with your audience.
  • Leverage AI Models for Scalability: Integrate machine learning models to predict customer preferences and streamline feed generation.

Dynamic carousels powered by customer clustering and machine learning offer an incredible opportunity to elevate your ad campaigns. By delivering hyper-relevant product recommendations to the right audience segments, you enhance customer experience and unlock substantial growth in ad performance.

When executed correctly, this strategy transforms raw data into actionable insights, ensuring your marketing efforts are as efficient as they are impactful.