Looding...

Zaamo

Elevating Fashion with AI-Driven Shopping Experiences

Fashion
case study

Business Impacts

Enhanced Click-Through Rate (CTR) in search results

Elevated Shopping Experience through Personalization

Improved Relevance of Search Results

Customer Key Facts

  • Country : India
  • Industry : Fashion
  • About : Zaamo is an Indian retail brand that enables influencers and content creators to bespoke clothing collections and customers to shop from Zaamo's website and app platforms.

Enhancing Zaamo’s search and recommendations

Zaamo employed a rule-based recommendation algorithm and a text-based search engine to provide product recommendations to its customer base. Acknowledging the ever-evolving landscape of AI-driven solutions, Zaamo sought to enhance its capabilities through the adoption of Google’s advanced Discovery AI technology. This strategic move was intended to leverage Google’s Retail Search and Recommendations AI functionalities, promising a tangible uptick in customer satisfaction and product relevance for Zaamo.

Challenges

  • Limited availability of user events data, a critical component for deriving valuable insights and elevating the customer experience
  • Dependence on a single product catalog data source, potentially constraining the diversity and accuracy of product recommendations
  • Mapping user events with requisite parameters necessitates a systematic approach and data integration efforts to enhance the quality of recommendations
  • Irrelevant product recommendations and limited search functionalities for end-users, resulting in a decline in overall customer satisfaction

Technologies Used

Google Big Query

Google Big Query

Google Retail Search

Google Retail Search

Google Cloud functions

Google Cloud functions

Google Recommendations AI

Google Recommendations AI

Google BigQuery Data Transfer Service

Google BigQuery Data Transfer Service

Google Cloud Scheduler

Google Cloud Scheduler

Solution

  • Quantiphi harnessed Google Discovery AI standards to meticulously engineer the data, establishing a standardized schema that seamlessly integrates with Google's Retail Search and Recommendations AI
  • Quantiphi imported Zaamo's data into the Retail Search and Recommendations AI console, ensuring that all relevant data was readily accessible and primed for in-depth analysis
  • Leveraging the wealth of product catalog data from Google Merchant Center and the invaluable user events data sourced from Google Analytics 4 (GA4), Quantiphi diligently ensured the meticulous analysis and transformation of both datasets to meet the prerequisites for implementing Discovery AI in the context of Retail Search and Recommendation AI
  • Quantiphi empowered Zaamo's web application with a suite of Google Retail Search features, encompassing Intelligent Search, Dynamic Faceting, Pagination, Filtering, and Ordering
  • Additionally, Quantiphi equipped Zaamo's web app with Google Recommendations AI capabilities, including the recommendation of Similar Items, "Others You May Like," and "Recommended for You" products

Results

  • Elevated user experience through personalization by enhancing search relevancy and optimizing product recommendations
  • Improved relevance of search results, guaranteeing users access to accurate and helpful information for their search queries
  • Optimized shopping experience through personalization by tailoring user experiences within Zaamo's web application

Thank you for reaching out to us!

Our experts will be in touch with you shortly.

In the meantime, explore our insightful blogs and case studies.

Something went wrong!

Please try it again.

Share