Technology Stack

• Data Platforms: Azure Blob, Data Bricks, Azure Data Factory, Salesforce Commerce Cloud, Salesforce Marketing Cloud, SQL Server
• AI Platforms: Azure ML Studio, Tensor flow, Python
• Generative AI Platforms: Open AI, Lama 2

Overview

Client

The client is a leading fashion jewellery and lifestyle accessories retailer selling via proprietary TV shopping channels and e-commerce platforms. They have direct customer access to over a hundred million households in the US, the UK, Canada, and Ireland.

With a growing online presence, they sought to enhance customer engagement and operational efficiency and maximize sales potential.

Industry

As an E-commerce App Development Company, creating engaging and captivating experiences is a must. Consumers today are inundated with vast buying options making them increasingly discerning and even skeptical.

The client recognized that optimizing user experience and minimizing technical issues are paramount to maintaining a competitive advantage.

Challenge

To accomplish the above goals, the client sought to leverage AI-driven solutions to support the actions listed below –

• Inventory management
• Demand forecasting
• Personalized recommendations
• Anomaly detection for proactive issue resolution
• Strategic decision-making

Solution Provided

The AI Development Services provided by Q3 technologies were able to deliver the below innovations in the identified areas –

1. AI-Powered Inventory Management

The solution enabled inventory dynamics to be viewed in real-time and offered recommendations for optimal stock management using generative AI interfaces.

Deep learning algorithms analyzed historical and current data to recommend dynamic adjustments to inventory levels, minimizing stockouts and excess inventory.

2. Advanced Demand Forecasting

Deep learning algorithms combined with an intuitive interface allowed the analysis of historical sales data and external variables to create demand forecasts with high levels of accuracy.

The ability to interactively explore demand forecast scenarios made proactive inventory replenishment easier, further mitigating the cost incurred through stockouts or overstock situations.

3. Personalized Recommendations

Using generative AI in the recommendation system helped deliver customized product suggestions to users using a wide and deep model for the recommender system.

Using techniques like Gradient Boosting Tree, Deep Knowledge-Aware Network, Neural Collaborative Filtering, Restricted Boltzmann Machine, and xDeepFM, the model combined the positive traits of linear models and deep learning.

This allows the recommendation system to analyze vast customer data like browsing history, purchase patterns, and demographics to deliver highly tailored product recommendations.

4. Anomaly Detection

The system used sophisticated Graph Neural Networks(GNNs) to capture intricate dependencies and anomalies that traditional methods may overlook to analyze the complex relationships within transactional data.

The AI interface provided actionable intelligence through intuitive visualizations of detected anomalies and their root cause analysis for proactive intervention.

Impact and Results

The seamless integration of AI-driven solutions and deep learning principles across these critical areas gave the client a competitive advantage by empowering them to adapt rapidly to market dynamics and ensure sustained growth.

The advantages the customer derived from the AI Development Services of Q3 Technologies are summarized in the below points –

• 10-15% new customer acquisition
• Identified new market opportunities
• 20% increase in sales conversion
• Optimized business processes
• Enhanced customer engagement
• Optimized inventory management

Recommendation Engine

A recommendation engine is an AI algorithm that uses machine learning to analyze big data and effectively help consumers discover product or services that interests them. These systems are trained to anticipate the user’s needs and preferences based on several factors collected through interaction with the platform.

These are of three types –

• Collaborative Filtering – Suggestions based on preference information from many users with similar behaviour.
• Content Filtering – Suggestions based on attributes of an item that interest the user to recommend similar or related items.
• Context Filtering – Contextual information like country, device, date, and time is used to predict the user action.

Use Cases:

• E-Commerce & Retail: It can make buying suggestions to the customers based on their demographics, what they have viewed, bought previously, etc.
• Personalized Banking: Detailed financial situations and preferences can help financial institutions create offers, recommend services, and tackle queries effectively 24/7.
• Media & Entertainment: Personalized recommendations make it easier for users to discover the content they can most engage with based on past purchase behaviour and patterns.

Anomaly Detection

Anomaly detection is a technique used to identify outlying patterns that deviate significantly from the expected behaviour. The activity improves efficiency, security, and decision-making in the organization, while also playing a key role in proactive risk management.

Using machine learning algorithms to power statistical methodologies, large datasets can be analyzed to proactively address potential issues and gain valuable and actionable insights.

Use Cases:

• Fraud Detection: Being trained on historical transaction data, ML algorithms can recognize patterns typical of fraudulent activities and flag the potential fraud for further investigation
• Predictive Maintenance: Patterns identified from sensors on machines that collect data such as temperature, vibration, and sound can allow maintenance to be scheduled before they break down.
• Network Security and Intrusion Detection: With an understanding of what normal network traffic patterns are like, AI models can quickly identify unusual activities and help prevent data breaches.