Overview About Matics Analytics

Project description: Designed and developed an advanced recommender system to maximize user engagement in e-commerce, boosting CTR by 13.7%. Technologies: PySpark, Scikit-learn, Azure, Databricks, Delta lake, MLFlow Value Delivered: Increased Engagement: A 13.7% boost in CTR and a 3.6% reduction in bounce rates signified enhanced user engagement and interaction with the platform. Improved Retention: By addressing user pain points and delivering personalized experiences, our client witnessed improved retention rates.

Project description: The goal was to determine the most effective communication channels (Direct Mail, Email, Both) for brand's marketing campaigns. Analyzed ~10M records of past customer transactions and campaign data and developed 3 different models using bagging and boosting algorithms (RF, XGBoost, CatBoost, LightGBM) that predicts the customer segments most likely to respond through a particular communication channel. Value Delivered: This resulted in a 20% increase in response rates and optimized resource allocation, significantly boosting ROI.

Project Description: A renowned sauce brand needed to analyze competitors websites, extract product and pricing information, and create a user-friendly dashboard for continuous monitoring of competitors new product launches, pricing and offers/promotions. Approach: To meet this challenge, we employed a multi-faceted approach Data Extraction Data Preprocessing Data Analysis and Visualization Filtering, Sorting and Charting The Outcome: Delivered a real-time dashboard helping the client identify pricing gaps, new product opportunities and gain marketing insights

Project description: As the real estate landscape grappled with uncertainties brought by Covid-19, client needed a more accurate risk assessment tool for financial behaviour analysis and tenant allotment. Value Delivered: Our innovative solution led to tangible benefits across various stakeholders: Landlords: Experienced a reduced risk of defaults & enhanced property value through a more accurate risk assessment tool. Tenants: Fair & transparent assessments, leading to better leasing opportunities for those deserving. Portfolio Management: Improved risk mitigation & decision-making capabilities.

Description: A Houston-based sporting goods chain aimed to boost credit card usage but faced low returns despite investing $65K annually in marketing. By leveraging Propensity Modelling & Marketing Analytics, we unified and analyzed their data, developed a predictive model, & helped targeting high-potential customers. Value Delivered: Targeting high-potential customers led to significant revenue growth and cost savings, resulting in 2.38x sales incremental sales uplift of $220K and saved $18K in marketing costs Tech Stack: Azure, Databricks, Data Lake, PySpark, ML Libraries, Azure DevOps

Project Description: A hotel chain with more than 20 properties across Europe, and North America, faced challenges in operational efficiency and guest satisfaction due to outdated systems and scattered data sources. Provided Solution: 1. Data Engineering: Robust pipelines, data quality checks, and exploratory analysis for hotel data. 2. Demand Forecasting: Ensemble ML models (93% accuracy) predicted occupancy rates. 3. Dynamic Monitoring: Real-time system tracked performance and offered insights. Value Delivered: This resulted in ADR up 10%, RevPAR up 15%, boosting hotel revenue!

Description: UK based gym chain faced fragmented customer profiles and missed revenue opportunities due to limited customer insights and redundant marketing efforts. Built a robust data integration pipeline with NLP and Fuzzy matching for text similarity matching to identify unique customers. Value Delivered: - With a unique customer identity, the gym chain eliminated duplicate up-sell and cross-sell marketing efforts - Delivered 360-degree dashboards enhancing decision-making and business efficiency.

Description: - Developed text summarization & categorization engine with open source LLMs. - Integrated with client existing infra for optimal performance and usability. Value Delivered: - The Gen-AI powered system achieved a remarkable 30% reduction in complaint resolution time within just 60 days of its launch - Resulting in improved customer satisfaction scores and agent productivity. Tech stack: Huggingface, Langchain, Azure Databricks

Description: The core goal is to develop a Propensity to Open (PTO) ML model for targeted marketing, predicting customers likelihood to open new credit card account, thus enhancing precision and improving cardholder acquisition. Value Delivered: - Propensity modelling boosts new account openings by 15% and improves customer acquisition with targeted future campaigns. - Leveraging the predictive strategy to select the right target audience increased the likelihood of customers opening a card account by 2.38x times compared to an un targeted approach.

Project Description: - Customer Attrition: The brand was facing major challenges in retaining customers and understanding the root causes of customer churn. - Decreasing Loyalty: Customer loyalty was diminishing. Solution: - Utilized advanced analytics and ML to predict customer churn proactively - Resulted in a significant 25% increase in customer retention, cultivating a more stable and loyal customer base. - The brand successfully reduced customer churn, contributing positively towards company's revenue stream.

Project Description: Real money gaming has increased fraud, demanding stronger measures. Adaptive AI detection is crucial to counter evolving tactics. Solution: 1. Accurate Detection: Trained on user features and social graphs for exceptional accuracy. 2. Advanced Techniques: Semi-Supervised and Reinforcement Learning predict and flag fraud precisely. 3. Real-time: Minimize financial losses for near real time anomaly predictions.

A leading logistics company with a vast network of warehouses faced inefficiencies in resource allocation, leading to high costs and customer dissatisfaction due to fulfillment delays. Solution: - Implemented demand forecasting algorithms using historical data. - Regional and state-specific forecasts based on localized demand patterns. - Real-time monitoring through an interactive dashboard enabled quick adjustments. Value Delivered: Improved demand patterns, significant cost savings, and enhanced customer satisfaction through timely and accurate fulfillment.

We optimized and scaled LLMs (OpenAI's Whisper model) deployment for Ecosmob Technologies, enhancing performance and resource utilization. Solutions implemented were parallel distribution on GPUs, enhanced throughput and dynamic configuration management. Value Delivered: The project doubled processing capacity, reducing processing time from 22.46 to 11.23 seconds. This led to a 100% improvement in throughput, increased operational efficiency, and cost efficiency by maximizing GPU resource utilization. Tech Stack: OpenAI, Hugging Face, PyTorch, TorchServe, FastAPI, Kubeflow.