A large online commerce platform was faced with the challenge of collecting a huge amount of data on customer behaviour, but was unable to use it effectively for business decision making. The company wanted to improve the customer experience and increase sales by analysing the available data.
Solution used
The company implemented a complex Big Data analytics system that consisted of three main components:
Data collection and storage:
– Customer browsing history.
– Transactional data
– Shopping cart abandonment patterns
– Search history
– Social media interactions
Analysis methods:
– Predictive analysis to predict shopper behaviour
– Data mining to uncover hidden patterns
– Real-time analysis to support immediate decision-making
Implementation
1.Data collection and cleaning
– Integrating large volumes of data in a variety of formats
– Combining structured and unstructured data
– Ensuring data quality and validation
- Analysis process
– Clustering to identify customer segments
– Regression analysis to predict customer behaviour
– Application of machine learning algorithms for pattern recognition
3.Implementation of results
– Creating personalised offers
– Develop dynamic pricing strategies
– Inventory optimisation based on forecasts
Lessons learned
The project has proven that Big Data analytics is not just a technological improvement, but creates real business value.
The key to success lay in the following factors:
– Treating data as an asset
– Examining the whole population instead of sampling
– Continuous learning and optimization
– Supporting real-time decision making
The Results
- - 25% increase in conversion rate
- - 30% reduction in cart abandonment
- - 15% increase in average basket value
- - 40% more efficient inventory management
