Inconsistent Sales Tracking: Inaccuracies in sales data caused misalignment between expected and actual sales performance, making it difficult to measure true sales progress.
Limited Insights into Non-Buyer Behavior: The client lacked a comprehensive understanding of why potential customers abandoned their purchases, impacting revenue growth without clear data on purchase barriers.
High Cart Abandonment Rate: A significant number of users were leaving items in their carts without completing the transaction, resulting in lost sales opportunities with unknown causes.
Location-Based Sales Analysis: Difficulties in accurately tracking sales across different regions hindered the client’s ability to understand regional consumer behavior and optimize marketing strategies for specific locations.
Missing Funnel Analysis: The client required an in-depth analysis of their sales funnel to identify where customers were dropping off for tracking and improving conversion rates.
To address the client’s data challenges, a structured data infrastructure was designed with Amazon Redshift and Power BI.
Organized Data Views: Created simple, organized views of data using SQL to transform raw data into clear, actionable tables that fit the client’s business needs.
Secure Data Transfer via VPN: Established secure VPN connections to ensure all data transfers between sources, the data lake, and analysis tools were encrypted and protected.
Automated ETL Process: Automated the data cleaning, organizing, and loading process (ETL) so that data would always be ready in real time for analysis, without manual delays.
Power BI Dashboards: Built interactive dashboards in Power BI to show key insights like sales trends and conversion rates, using easy-to-understand visualizations for timely decisions.
Scalability for Future Needs: Designed the system to be easily expanded as needed, with ongoing improvements to keep the dashboards and data fresh and relevant.
The solutions implemented led to improvements in transaction completion, data accuracy, and customer engagement for the client.
Cart abandonment decreased by 43%, translating to roughly 5,000 fewer abandoned carts per month due to enhanced checkout features and personalized reminders.
Advanced tracking methods improved sales data accuracy by 30%, allowing the client to track and report 15,000 sales monthly with far greater precision.
With insights into customer behavior, revenue projections improved by 20%, helping the client make informed financial decisions and forecast an additional $1 million in revenue annually.
Personalized experiences and smoother checkout led to a 22% increase in customer retention, with around 4,200 customers now returning each quarter.
Location-specific tracking provided a clearer picture of sales by region, contributing to a 18% increase in regional conversions, adding approximately 1350 new sales monthly in high-performing areas.
Fashion
India
Managed Analytics
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