Big Data – Retail
A large European Retailer had data marts spread across different countries, hence it was difficult to get a single view of business. Retailers wanted to build single scalable platform so as to
- Improve store performance through better understanding of the drivers of individual store potential.
- Ingest new multistructured data sources like transaction logs, clickstream data.
- Inventory forecasting as part of store ordering solution.
- Analytics solution to provide ability to generate reports quickly from diverse sources.
- Build better multichannel shopping behaviour of customers.
- Lambda Architecture with Hadoop and HBase
- A single view of the business by mapping all diverse data to a single structure
- Data capture using Apache Kafka
- Added semistructured source like transaction logs, clickstream data to analyse with existing data
- Scalable, Fault-tolerant architecture
- Reduced ETL time
- Market Basket Analysis
- When an item goes on sale, let retailers know about adjacent products that benefit from a sales increase as well
- Real time store performance
- Implemented realtime dashboards using HBase to understand performance of region/country/store
- Pattern/Behavioural Analysis
- Analysis of clickstream data and uncommitted transaction logs data to improve customer retention programme, marketing/promotion programme
- Inventory forecasting
- Forecasting of products in 24 hours cycle instead of weekly cycle.
Value to Customer
- Lower total cost of ownership because of single platform
- Increased processing speed
- Realtime view of business
- Increased store performance to baseline.
- Optimized product assortment mix to store demographics.
- Facilitates effective customer centric marketing.
Tags: Analytics, Big Data, Migration, Retail