Analytical Areas

Our team has worked on various Analytical solutions across verticals. Collectively, we have solved problems related to:
  • Anomaly Detection

    Anomaly detection or outlier detection is the identification of objects, events or observations which do not conform to an expected pattern or expected business rules. These techniques can detect fraud, a structural defect or medical problems etc.

  • Sentiment Analysis

    Sentiment analysis determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document by the use of natural language processing, text analysis and computational linguistics.

  • Entity Extraction

    Entity identification and extraction results in identifying things such as persons, places and organizations. Entity extraction can add a wealth of semantic knowledge to the content to help quickly understand the subject of the text. This is useful in network analysis and Master Data Management.

  • Social Network Analysis

    Social Network Analysis and Community Detection these techniques are used to identify various networks to uncover terrorists or communities (entities have a shared feature sets), affinities between various entities. These are important in establishing linkages in various fields such as crime detection, influence identification and fraud detection.

  • Recommender System

    Recommender Systems providing suggestions for items to be of use to a user. These are essentially reducing the vast choices/alternatives generally available to for a user purpose and filter those that are most relevant. These are generally powerful ways to create most relevant, personalized content to a user or a purpose.

  • Ranking / Scoring

    Our team has a repertoire of various recommenders and ranking/scoring algorithms. Most Data products such as ?Whom you may know?, ?Who also bought?, ?Followers?, ?trending topics? and ?Relevant Information? Consolidators.

  • Segmentation

    Segmentation is the process of classifying objects into groups based on similarities, and clustering is the process of finding similarities. These are extensively used techniques with wide variety of applications.

  • Forecasting

    Forecasting is the process of making statements about events whose actual outcomes have not yet been observed. By using formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods.

  • Churn / attrition Prediction

    Churn prediction for consumer/retail services/products, RFM Analysis, Behavioral scoring, Lifetime value of a customer, Market Basket Analysis and Identifying up-sell and Cross Sell opportunities.