AI-Driven Recommendation Ecommerce Dashboard

Project category

Project Name :

Smart Basket

Industry :

Ecommerce

Country :

United States

Duration :

4 Months

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Our client, a leading E-commerce Grocery Mart based in California, USA, wanted to enhance customer experience and increase sales through personalization. We implemented an AI-driven recommendation engine integrated with a custom Power BI dashboard. The solution helped the client monitor performance in real-time, optimize product suggestions, and drive stronger customer engagement — resulting in a 20% uplift in online sales.

Main KPI’s Tracked

The dashboard was designed to focus on the most critical KPIs for retail & e-commerce:

  • Conversion Rate (before vs. after personalization)
  • Average Order Value (AOV)
  • Revenue per Visitor (RPV)
  • Click-Through Rate (CTR) on Recommendations
  • Add-to-Cart Rate from Recommendations
  • Repeat Purchase Rate
  • Customer Lifetime Value (CLV)
  • Cross-Sell & Upsell Revenue %

Solution Offered: Power BI Dashboard Development

We followed a 5-step approach to deliver a scalable, data-driven personalization and analytics solution:

Step 01

Data Integration & Preparation

Unified website analytics, CRM, and transactional data into a centralized SQL database. Ensured data quality, consistency, and availability for analytics.

Step 02

AI Model Development

Leveraged Azure Machine Learning to build and deploy advanced recommendation engine models using historical purchase data and user behavior patterns.
Step 03

Dashboard Design & Visualization

Developed a custom Power BI dashboard to visualize real-time performance metrics, including recommendation effectiveness, engagement trends, and product-level insights.

Step 04

Real-Time Monitoring & Automation

Integrated automated alerts for anomalies, sales uplift, and retention signals. Embedded AI/ML model performance tracking directly within Power BI reports.

Step 05

Insight Sharing & Collaboration

Enabled secure sharing of reports across teams, ensuring business leaders, marketers, and product owners could make data-backed decisions faster.

Main Features of the Dashboard

The dashboard delivers an intuitive and data-rich experience, combining real-time insights with AI-driven analytics. It empowers teams to monitor key performance metrics, track personalization impact, and make faster, informed business decisions.

Real-time, interactive visualizations for key KPIs

Drill-down by category, product type, or customer segmen

AI/ML model performance tracking within reports

Automated alerts for sales uplift and retention signals

Shareable dashboards for quick decision-making

Business Benefits of AI-driven Personalization & Recommendation Ecommerce Dashboard

The AI-powered recommendation and personalization dashboard enables key stakeholders — from CMOs to data teams — to uncover actionable insights, optimize customer journeys, and make data-driven decisions that enhance growth and efficiency.

01

Boost Sales & Margins

Empower marketing and product teams to identify high-performing products and personalize offers that increase conversions and overall revenue.

02

Enhance Customer Engagement

Enable CMOs and campaign managers to track user behavior and deliver hyper-relevant product recommendations that improve CTR and AOV.
03

Increase Loyalty & Retention

Help customer experience leaders and CRM teams predict churn, strengthen repeat purchases, and enhance long-term customer lifetime value (CLV).

04

Optimize Inventory Movement

Support merchandisers and operations teams with insights into demand patterns, ensuring efficient stock utilization and reduced overstock.

05

Enable Faster Decision-Making

Provide business leaders and BI teams with a unified view of performance, enabling quicker, insight-driven strategic actions across departments.

Who Gains Actionable Insights from This Dashboard?

CMO (Chief Marketing Officer)

Marketing Managers

E-commerce Managers

Product Owners

Merchandising Heads

Customer Experience Managers

Data Analysts

Data Scientists

Sales Directors

Business Intelligence (BI) Teams

Technology Stack

We used proven and modern technologies to ensure scalability and accuracy

Power BI

To create interactive dashboards with drill-down, KPIs, and real-time visualizations.

Azure Machine Learning

To build and deploy the AI-driven recommendation engine models.

SQL Database

To store and integrate transactional, product, and customer data before feeding into Power BI.

Results Achieved

The AI-powered solution delivered measurable improvements across sales and customer engagement metrics.

Key Results:

20%

Increase in overall online sales

25%

Higher Click-Through Rate (CTR) on product recommendations

18%

Rise in Average Order Value (AOV)

15%

Improvement in Repeat Purchase Rate

These results validate the impact of combining AI-driven personalization and recommendation with real-time Power BI analytics. The client now benefits from faster decision-making, improved marketing ROI, and stronger customer relationships — positioning them as a leader in data-driven e-commerce personalization in the U.S. grocery sector.