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Hearting Integration

Success Story

Hearting Integration for Seamless Product Discovery

Driving engagement, personalization, and repeat visits.

Real-time favorites

customers can save items instantly during browsing.

Faster personalization

the Redis caching powers low-latency, scalable performance.

Technologies

Redis

Java

JavaScript

TypeScript

React

Expertise

Microservices

Web development

Front-end engineering

Monitoring

Back-end engineering

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Engagement gap

Without an easy save/heart action, customers dropped off rather than building shopping intent.

Personalization limits

Missing signals reduced the ability to tailor recommendations and remarketing.

Navigation friction

Customers had to leave product listing pages to view or save details, interrupting discovery.

Missed conversion opportunities

Limited re-engagement with favorite items weakened sales potential.

Have a Similar Problem?

Let’s design seamless customer journeys that boost engagement.

Contact Sales

Retail-centric engineering

Zoolatech has proven success building personalized shopping features that enhance product discovery and engagement in high-traffic retail environments.

Scalable integration skills

Our expertise in microservices, caching, and API orchestration enabled the team to deliver a resilient solution optimized for performance and future growth.

Zoolatech is a senior-heavy engineering firm with Silicon Valley roots and a Miami HQ, specializing in legacy modernization, system re-architecture, and AI deployment to drive long-term, compounding value.

2017

Year Founded

600+

Employees

96%

Client Satisfaction

Phase 1

Discovery and design

Defined lightweight interaction goals and mapped technical dependencies.

Phase 2

API and caching strategy

Built an Experience API layer to unify wish list access and integrated Redis caching for speed.

Phase 3

Front-end implementation

Designed heart states and session-persistent toggling across product listing pages.

Phase 4

Integration testing

Validated real-time accuracy with parallel data fetches from product and wish list APIs.

Phase 5

Deployment and monitoring

Released feature with observability hooks to ensure scalability under high traffic.

Giving shoppers the ability to save favorites instantly removes friction from discovery, strengthens intent signals, and transforms casual browsing into a personalized, recurring engagement loop.

Instant hearting action

Customers could toggle a heart icon to save products without leaving listing pages, with the state updating in real time during browsing.

Session-persistent favorites

The heart state was retrieved from the Wish List API on page load and persisted dynamically, ensuring consistency across sessions.

Optimized API layer

A new Experience API integrated with the Wish List API and Redis cache to reduce latency, distribute load, and return accurate heart states to the frontend.

Synchronous and asynchronous communication

Synchronous communication between services is handled via HTTP, ensuring real-time data exchange and interactions across the platform. For asynchronous communication, the platform relies on Kafka events, which are integrated via our client’s proprietary implementation called Proton.

Data services integration

The platform leverages AWS services for data storage and analysis. Data generated by the platform is stored in S3 and processed using AWS Athena. The Retail Hub uses this data to generate reports and visualizations, providing the company with real-time insights into their operations.

External access and security

While most of the Retail Hub’s applications operate within our client’s VPN, some are accessible externally via Fastly, enabling remote users to interact with the platform securely.

Option	Risk	Mitigation
Performance bottlenecks from API load	Direct calls to the Wish List API for every heart action could have created latency and slowed the shopping experience.	Redis caching and an Experience API layer were introduced to reduce load and return fast, consistent responses.
State inconsistency across sessions	Customers could see inaccurate heart states if cached data did not align with the actual wish list.	Real-time sync with the Wish List API on page load ensured accuracy and consistency across sessions.
Scalability during peak shopping events	High-volume traffic during seasonal sales and promotions could overwhelm APIs and degrade feature performance.	A microservices-based Experience API distributed requests and supported parallel data fetching to maintain speed at scale.

The new wishlist and hearting feature increased customer interaction, improved personalization signals, and reduced friction in the browsing-to-purchase journey.