Inferenz

Pricing and Promotion Strategy for Sporting Goods company

• Tableau
• Python
• Snowflake
• Apache Airflow
• R

Challenges

• Maximizing revenue while maintaining customer demand poses challenges in determining the most effective pricing strategy.
• Setting the right price is crucial in competitive markets to maintain competitiveness.
• Accurate revenue forecasting relies on understanding the impact of price changes on demand, which can be achieved through price elasticity modelling.
• Price elasticity insights help organizations understand customer sensitivity to price changes, preventing potential negative impacts on sales and revenue.

Solutions

• Data Collection and Analysis: Gather historical sales data and prices to establish the basis for price elasticity modeling.
• Model Selection and Calibration: Choose suitable price elasticity models based on product, market, and data attributes. Adjust model parameters for accuracy and dependability.
• Scenario Analysis: Perform scenario analysis to evaluate the effects of various price adjustments on demand and revenue.
• Real-Time Monitoring:  Implement a system to monitor market dynamics and customer behavior in real-time, enabling prompt responses to changes in price sensitivity.

Benefits

• Understand price-demand relationship.
• Optimize pricing strategy.
• Identify price-sensitive customer segment.
• Make data-driven decisions on promotions and pricing strategies.
• Assess the potential revenue impact of price changes.
• Evaluate the impact of price adjustments on profit margins.

US based Leading retailer for sporting goods (ecommerce)
The challenge lies in devising an effective pricing strategy to maximize revenue while meeting customer demand, necessitating the implementation of robust data analysis techniques and real-time monitoring systems to adapt to dynamic market conditions and mitigate potential negative impacts on sales and revenue. 

Data warehouse, ETL and Analytics implementation​

• Tableau
• Redshift
• SQL
• Java
• AWS Services
• Python

Challenges

• Data acquisition complexity arises from diverse sources, including headwaters, requiring sophisticated integration strategies.
• Data visualization complexity stems from the sheer volume and intricacy of datasets, demanding substantial cost for meaningful representation.
• Performance bottlenecks during data loading, exacerbated by large dataset sizes, hinder operational efficiency and may delay new version releases.

Solutions

• Utilizing EMR and other AWS services to seamlessly transfer big data into our transactional Redshift tool for efficient storage and management.
• Leveraging our analytics solution to visualize data effectively in Tableau, enhancing accessibility and insights for stakeholders.
• Implementing the Katana framework to optimize data refresh performance in Tableau, overcoming limitations associated with handling extensive and bulky datasets.

Benefits

• Ensuring 100% information availability in near real-time, facilitating timely decision-making and strategic planning.
• Enhancing the accuracy of new device version releases through comprehensive dashboard creation, fostering informed decision-making and streamlined development processes.

US based Telecommunication company
The challenge lies in optimizing data acquisition and visualization processes while overcoming performance bottlenecks. Since they are associated with large dataset sizes, the necessity of a migration to AWS architecture for cost reduction and enhanced operational efficiency was 

Gen AI-Based Personal Stylist for clothing industry

• SQL
• Java
• Python
• Tableau
• Redshift
• AWS Services

Challenges

• Data Collection and User Profiling: Collecting accurate user data for personalized recommendations.
• Style Ambiguity: Interpreting user preferences accurately for suitable recommendations.
• Real-Time Fashion Trends: Staying up-to-date with changing fashion trends.
• Integrating User Feedback: Improving recommendations based on user feedback.
• User Adoption and Engagement: Encouraging users to try and regularly use the AI-based personal stylist

Solutions

• Integrating LLM and Langchain frameworks to create a local LLM for fashion e-commerce product queries.
• Generating personalized recommendations and outfit suggestions using Generative AI.
• Fine-tuning the model and collecting user data to set preferences.
• API for text input and providing desired output (product, image, textual query answer).
• Deployment on a cost-effective architecture.

Benefits

• Enhanced user satisfaction through personalized fashion recommendations
• Efficient shopping experience with time and effort saved through the AI-based personal stylist.
• Tailored recommendations can lead to higher conversion rates and increased customer loyalty, boosting the application’s revenue
• Cutting-edge AI-based personal stylist differentiates the application, attracting more users and establishing market leadership
• Offering insights into fashion trends and expanding users’ fashion knowledge, enabling them to discover new looks

 
Gen AI-Based Personal Stylist for clothing industry
In fashion e-commerce, the pressing need arises to implement an AI-based personal stylist solution adept at overcoming hurdles in user data collection, style interpretation, trend adaptability, feedback integration, and user engagement, thereby revolutionizing the clothing industry experience.

Data and AI for Connected Smart Cameras

• spaCy
• NLP
• Python
• NoSQL
• ElasticSearch
• AWS Services
• BERT

Challenges

• Large number of events generated by the smart cameras
• Cameras required detailed device management
• Managing timeseries and CVR capabilities and Playback
• Providing a powerful search platform to the user with milliseconds latency

Solutions

• Implemented streaming solution to retrieve large number of events real time from cameras
• Indexed events to provide search capabilities using ELK stack
• Implemented time series DB solution to provide search capabilities
• Implemented smart search solution using Natural Language Processing
• Implemented NoSQL DB solution for faster retrieval to achieve milliseconds latency

Benefits

• Achieved milliseconds latency to retrieve events analytics from millions of device events
• Data analytics capabilities to capture security and operational metrics
• Smart search to search historical as well as real time events
• Alerts and notification for suspicious events for security 

Data Science platform -  Automating ML Lifecycle

• MLOps
• AWS CodePipeline
• AWS CodeBuild
• AWS CloudFront
• AWS SageMaker
• Machine Learning

Challenges

• High project stall rate – Data scientists face difficulties in deploying ML models, with 80% or more projects stalling before completion.
• Lack of structure in the ML lifecycle – The absence of a formalized process hinders successful model deployment.
• Difficulty in rolling in and out machine learning models, monitor them and retrain them in a structured manner 

Solutions

• Leverage the capabilities of AWS Sagemaker to create automated pipeline for preprocessing ,training, testing, deploying and monitoring Machine Learning models
• Use SageMaker’s AutoML ability to automatically create models by providing just the dataset and target variable. Use the generated artifacts by AutoML to further fine tune the model.

Benefits

• Accelerates Machine Learning development by reducing the training time from hours to minutes with further optimized structure.
• Streamlines the Machine Learning lifecycles by automating and standardizing MLOps practices across the organization which are using AWS services, to build, train, deploy and manage models at large scale
• Efficiently find the optimal solutions to Machine learning problems using AutoML

US based Leader in consumer robots and IoT devices building
The customer faces substantial hurdles in efficiently deploying machine learning models, evidenced by a high project stall rate and a lack of structured processes throughout the ML lifecycle, prompting the need for transformative solutions to streamline model deployment and lifecycle management.