Data Science

Applying a data first approach to tackle the toughest business challenges through advanced machine learning solutions

Four Tenets of our Data Science Practice

1. Techniques

While implementing the core data science techniques, our focus is to derive maximum insights and improve the performance of the ML models. Our data scientists have explored the breadth and depth of different baseline techniques to develop an excellent perspective of the different techniques that are prevalent in the Data Science area. This has helped us easily navigate critical business problems across industries and geographies.

SUPERVISED LEARNING

  • XGBoost
  • Random Forest
  • Neural Networks
  • Support Vector Machines (SVM)
  • Logistic Regression
  • Naive Bayes

UNSUPERVISED LEARNING

  • Pattern Mining
  • Association Rule Mining
  • Topic Models
  • Clustering
  • Connected Graphs

REINFORCEMENT LEARNING

  • Markov Decision Process
  • Multi-Armed Bandits
  • Monte Carlo Simulations
  • Dynamic Programming

TRADITIONAL AI

  • Breadth-First Search (BFS)
  • Depth-First Search (DFS)
  • A* Search
  • Dijkstra
  • Heuristics
  • Pruning
  • Constraint Satisfaction Problem (CSP)

NEW AGE TECHNIQUES

  • Quantum Computing
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Long Short-Term Memory (LSTM)
  • Generative Adversarial Networks (GAN)

TOOLS AND TECHNOLOGIES

2. Problem Solving

Demystifying cryptic problems is in every Sigmoidian’s DNA. We continuously challenge ourselves with unknown and unseen analytics problems across different industries to maximize the performance of our intelligent data solutions. We believe that sound knowledge is acquired by the diversity of experience as well as the length of experience.

Our problem solving approaches are based on our 3Cs Principle:

CRITICAL THINKING

CURIOSITY

CREATIVITY

Our 3Cs Principle includes a strong focus on problem understanding, abstraction development, divide and conquer, causality/root-cause identification, constraint identification, hypothesis validation, and test and learn methods, among others.

3. Innovation in Every Solution

We believe that every problem is a new and fresh opportunity. The same call planning problem or a distribution network problem has different connotations across different industries. Furthermore, these would be different problems from one company to another.

Our innate curiosity to understand and solve problems propel us to get to the nitty-gritty of the business problems in the context of the overall functioning of the micro environment connected directly or indirectly with the project. This helps us in building personalized and tailor-made solutions. In many occasions, the clients already have in-house analytics solutions built. As Sigmoid, we champion in improving the existing systems.

4. Execution

With our three key groups: Data Science, Data Engineering and DevOps teams – we have the capability to work on end-to-end analytics business problems. We believe in making our solutions work in real life, and have proven track record of building “executed” solutions. We work with our clients very closely to make sure that our solutions get adopted across stakeholders in their organizations. With Engineering and DevOps teams, we bring in the last mile connectivity as well to embed the Data Science solutions in the existing production environments.

Success Stories

Demand Forecasting

Estimate future sales, optimize inventory, plan pricing and promotions with our specialized data-driven approach to maximize forecast accuracy and minimize stockouts

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Personalized Recommendation

Get the most out of your customer behavioural data to increase customer satisfaction and offer better personalization with advanced self-learning systems that drive profitability

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Fraud Detection

Protect your business from evolving fraud trends, techniques and minimize the cost of manual reconciliations using predictive, descriptive and social network techniques

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Customer Lifetime Value Prediction

Determine customer behaviour, predict future revenue, measure long-term business success for better customer relationships and effective marketing with our ML solutions

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Marketing Effectiveness

Leverage minute details of customer behaviour using prescriptive analytics that focus on consumer-driven insights from your marketing campaigns to deliver the best promotions

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Identity Graphs

Identify users in a household through adaptive and self-learning graphs that link and match customer records from disparate sources to fine-tune customer targeting

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Our Prowess across the Analytics Value Chain

CUSTOMER ANALYTICS

  • Customer LifeTime Value
  • Identity Graphs
  • 360° Customer Journey
  • Sentiment Analysis
  • Customer Churn Prevention

PRICING ANALYTICS

  • Price Elasticity Analysis
  • Markdown Optimization
  • Price Optimization
  • Competitor Price Sensitivity
  • Channel Efficiency Analysis

MARKETING ANALYTICS

  • Personalized Recommendation
  • Marketing Effectiveness
  • Customer Segmentation
  • Market Basket Analysis
  • Marketing Attribution

SECURITY ANALYTICS

  • Predictive Analytics
  • Fraud Detection
  • Risk Analytics
  • Preventive Maintenance
  • Network Optimization

SUPPLY CHAIN ANALYTICS

  • Demand Forecasting
  • Inventory Optimization
  • Real-Time Monitoring
  • Stock Analytics
  • Logistics Route Optimization

ADVERTISING ANALYTICS

  • Campaign Optimization
  • Device-Household Matching
  • Click Prediction
  • Viewability Prediction
  • Real-Time Customer Behaviour

How We Deliver Successful Data Science Projects

Consulting

This is usually a 2 weeks activity at the client site. In this phase, our experienced data consultants visit the client’s offices, and conduct a series of workshops to understand the detailed project requirements. Based on these discussions, we layout a detailed project plan and roadmap.

PoC

In this phase we develop a comprehensive solution using a selected data sample. We layout the detailed solution architecture, measurable and relevant KPIs, and business insights, considering the subsequent production grade scaling of the ML model.

Production

The solution is then rolled out exhaustively to the whole environment. Our Data Engineering team is also involved in this phase, to swiftly scale up and productionize the ML models. Both our Data Science and Data Engineering teams work together to bridge the gaps in the last mile sprint and achieve the business objectives.

Engagement Lifecycle

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