- Los Angeles
- Retail & E-commerce
- 12 weeks
- B2C
- Python, SQL, Tableau, Power BI, Google BigQuery, Segment, Optimizely
Project Brief
A rapidly developing online apparel store wishes to enhance the customer experience and boost sales through customised recommendations and targeted marketing methods. They intended to use business intelligence to gain a better understanding of client preferences, behaviour, and purchase trends.
Client Background
The company, known for its fashionable yet affordable apparel, has grown significantly in recent years. However, they were challenged with increased competition and the necessity to distinguish themselves in a crowded field.
Key Challenges:
Solution:
1. Discovery and Planning
We worked together with the marketing and data divisions of the shop to develop specific KPIs, business objectives, and a thorough business intelligence plan. We conducted a thorough examination of their current data infrastructure, found possibilities and limitations, and developed a plan for data integration, analysis, and visualisation.
2. Development
To extract, analyse, and load data from several sources into a single data warehouse, our expert business intelligence team used Python and SQL.We used Google BigQuery to build a dependable data pipeline that ensures data accessibility and integrity. Next, we used Tableau and Power BI to construct interactive dashboards that provided us with a full insight of customer behaviour, the performance of the product, and marketing campaign success.
3. Implementation
We collaborated with the retailer's IT and marketing divisions to ensure that the business intelligence platform fit easily into their existing operations. We gave personnel full training on how to utilise the dashboards and evaluate the data.
Tools & Technology Used
Python
Programming Language
Tableau
Data Visualization
Google BigQuery
Data Warehousing
Optimizely
A/B Testing
Power BI
Data Visualization
Features:
Personalized Product Recommendations
The system examines user browsing and purchasing history to recommend things that align with their interests.
Targeted Marketing Campaigns
Using data insights, the store may design campaigns geared to certain customer demographics, increasing conversion rates and ROI.
Inventory Optimization
Predictive analytics may help shops optimise inventory levels, decreasing stockouts and overstocking.
Customer Churn Prediction
Machine learning models can predict customer churn, allowing for proactive retention tactics.
A/B Testing
The platform allows retailers to test multiple website designs, product locations, and marketing messages to improve consumer experience and conversions.