Case Study: Argos achieves scalable, high‑relevance search and analytics with Elastic

The Digital Transformation for Argos Search and Browse

Argos, the UK’s #1 multichannel retailer with 800+ stores, 29M store customers, ~1bn online visits and 60,000 products, needed to modernize search and browse to cope with extreme peak loads (e.g., Black Friday: 800k visitors/hour, 2M visits in 4 hours, 84% mobile) while delivering a consistent omnichannel experience, correct business logic for product variations and availability, and higher customer satisfaction.

The team built an in‑house search platform using Kafka, Apache Spark/ES‑Hadoop, Elasticsearch, custom backend APIs (Spring/Kotlin, Flask) and a Node/React frontend, plus mobile apps and chatbots. Features like nested product variants, autocomplete, a phrase suggester, Kibana-powered customer insights and relevancy regression enabled the platform to scale to Black Friday traffic, unify web/mobile/in‑store search and improve relevance, availability and overall customer experience.

Argos

Chris Crispin

Software Development Engineer II