Data Analytics Services
for Australian RetailerCase Study – Data Analytics Services
Problem Statement:
TL Consulting were engaged with an Australian retailer to deliver Data Analytics services as part of an initial Phase 1 assessment. The retailer serves millions of customers per week across various locations in Australia.
Their big data store is highly complex with various input data sources, volumes, and data formats. As a part of their marketing team’s continuous improvement initiatives, they had a requirement to define an enriched data model, build and orchestrate an end-to-end data pipeline with a target visualisation tool to present the key trends and insights to assist stakeholders with better decision-making and predictive analytics capabilities to better understand their customer behaviours and sentiment.
The client’s team did not have enough resources with the right skills and capabilities to complete the analysis in the timeframe required
The clients in-house analytics solution showed a low accuracy rate based off current benchmarks
Big data assets were not in a conformed format and were not ingested correctly into the right tools to provide accurate outputs and insights
Client Outcomes – Metrics
%
DATA MODEL ACCURACY
WEEKS TO DELIVER END-TO-END SOLUTION
Deeper insights of the customer segmentation to promote better consistent buying patterns
Delivered a 95% accuracy data model (for a given location) that helped with stakeholder decision making processes
Trained & optimized the data model based on selected criteria for other locations and respective populations
The TL Consulting Solution:
TL Consulting were able to support the data requirements and bridge the gap in the client resoucing to establish an end-to-end data analytics solution. This helped the management team to leverage deeper insights into customers’ buying patterns so they could better optimize and manage their inventory and demand for popular products with high precision
Performed data engineering on historical datasets (google analytics, facebook etc) that was in ORC & parquet format and converted to CSV for better standardisation
TL delivered the end-to-end analytics solution 4 weeks ahead of the planned delivery schedule and improved the data model with the help of Machine Learning and Deep Learning techniques with higher accuracy
Better demand & inventory management and store management was driven from the analytics solution leveraging the data pipeline
Case Study – Data Analytics Services
Problem Statement:
TL Consulting were engaged with an Australian retailer to deliver Data Analytics services as part of an initial Phase 1 assessment. The retailer serves millions of customers per week across various locations in Australia. Their big data store is highly complex with various input data sources, volumes, and data formats. As a part of their marketing team’s continuous improvement initiatives, they had a requirement to define an enriched data model, build and orchestrate an end-to-end data pipeline with a target visualisation tool to present the key trends and insights to assist stakeholders with better decision-making and a better understanding of their customer base and sentiment.
Client Outcomes – Metrics
%
DATA MODEL ACCURACY
WEEKS TO DELIVER END-TO-END SOLUTION
The TL Consulting Solution:
TL Consulting were able to support the data requirements and bridge the gap in the client resoucing to establish an end-to-end data analytics solution. This helped the management team to leverage deeper insights into customers’ buying patterns so they could better optimize and manage their inventory and demand for popular products with high precision
TL delivered the end-to-end analytics solution 4 weeks ahead of the planned delivery schedule and improved the data model with the help of Machine Learning and Deep Learning techniques with higher accuracy
Performed data engineering on historical google analytics data that was in ORC & parquet format and converted to CSV for easier ingestion
Better demand & inventory management and store management was driven from the analytics solution leveraging the data pipeline
Delivered a 95% accuracy data model (for a given location) that helped with stakeholder decision making processes
Trained & optimized the data model based on selected criteria for other locations and respective populations
Deeper insights of the customer segmentation to promote better consistent buying patterns
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