Dynamic E-Commerce Retargeting

Dynamic retargeting campaign dashboard showing personalized product ads for fashion e-commerce

Context

A fast-growing online fashion retailer was struggling with cart abandonment rates exceeding 70%. Their static display campaigns failed to re-engage shoppers who had browsed specific products, resulting in wasted ad spend and a declining return on ad spend across their programmatic channels.

Kalithea Media deployed a deep learning retargeting strategy powered by dynamic creative optimization (DCO) connected directly to the retailer's product feed. By serving hyper-personalized ads featuring the exact items each user had viewed — along with algorithmically recommended complementary products — the campaign transformed abandoned sessions into completed purchases at scale.

Fashion e-commerce product catalog with dynamic ad creative variations

Diagram of dynamic creative optimization workflow with product feed integration

Our approach and solutions

A full-funnel retargeting engine built on real-time product data and predictive bidding.

Product feed DCO integration

Connected the retailer's live product catalog to our DCO engine, enabling real-time ad assembly with accurate pricing, availability, and promotional overlays for thousands of SKUs.

Deep learning audience segmentation

Built predictive models to segment users by purchase intent — separating casual browsers from high-value abandoners — and allocated budgets proportionally to maximize conversion probability.

Sequential retargeting journeys

Designed multi-touchpoint sequences that evolved messaging from product reminder to urgency trigger to incentive offer, adapting creative and frequency based on user engagement signals.

Cross-device identity resolution

Unified user profiles across mobile, desktop, and tablet to ensure consistent retargeting without oversaturation, reducing wasted impressions by over 40%.

Our key strengths

Real-time creative assembly

Our DCO platform generates personalized ad units in milliseconds, pulling live product data including price, stock status, and promotional badges to ensure every impression is relevant and accurate.

Predictive bid optimization

Machine learning algorithms continuously adjust bids based on conversion likelihood, time of day, device type, and user recency — maximizing ROAS while respecting budget constraints.

Transparent performance reporting

Full visibility into product-level performance, creative variant testing results, and attribution paths — empowering the retailer to make informed merchandising and marketing decisions.