Home     |     About us     |     consulting     |     training     |     Analytics     |     IOT     |     Services     |     Projects     |     Contact Us

Data Analytics services team provides consulting services using customized analytics tools. These tools are designed to drive decision making by generating visualizations that lead to meaningful insights. We combine our quantitative modelling expertise with deep understanding of business needs and state-of-the-art technologies to solve complex problems. All of our analytic solutions are interactive and can be delivered in different formats that vary as per accessibility and cost using techniques:

Data Science: Using Predictive Modeling, Behavioral Modeling, Machine Learning, Optimization, Text Mining, Natural Language Processing, Information Retrieval.

  • We use interdisciplinary knowledge about processes and systems to extract knowledge and insights from data in various forms, either structured or unstructured,[which is a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics, similar to Knowledge Discovery in Databases (KDD).
  • We are having hands-on exposure of machine learning, statistics, predictive modeling, text mining and optimization problems of our clients.
  • We use various tools including R, Python, SQL. Data visualization is achieved using Tableau.
Data Engineering: Using Big Data, Web Mining, Large Scale Data Processing, Unstructured Data, Cloud Solutions.
  • We having strong capabilities in data engineering for both traditional and big data needs. Whether it is scraping, managing data, integrating analytics solutions, and customize the solution per client’s needs and constraints.
  • We use tools include RDMS solutions like MY SQL or MS SQL Server data management solution. Our big data architects have designed and implemented Hadoop systems, and are very familiar with technologies such as Map Reduce, Hive, and Pig. We have executed scalable machine learning projects in a big data environment using open source technologies
Business Insights: Dashboards, Data Visualization.
  • We provide insights with a variety of real-time business intelligence reports using Dashboards and Scorecards. Our reports are a combination of data tables, charts and inferences based on statistical analyses in the context of the business objectives.

Live Projects:

1. Media and Entertainment Project
Digital technology increasingly driving day to day lives but traditional formats and channels are still very much active. By advanced analytics we are transforming by helping them combine digital and traditional data to gain a competitive advantage.

Recommendation Engine:
Our Recommendation system is impacting or even redefining lives in many ways. One example of this impact is how our online shopping experience is being redefined. As we browse through products, the Recommendation system offer choices for different products a customer might be interested in. Our Recommendation system works in well-defined, logical phases of data collection, ratings, and filtering.

2. Telecom project:
Our Analytics engine is helping service providers to innovate and maximize their revenue potential. It necessarily requires right solution to be in place so that they can harness volume, variety and velocity of data coming into organization and leverage actionable insight from that data.

Personalized behavior:
This allows telecom companies to offer personalized services or products at every step of the purchasing process. Businesses can tailor messages to appear on the right channels (e.g., mobile, web, call centre, in-store), in the right areas.

Customer Behavior:

  • Voice, SMS and Data usage patterns
  • Customer care history
  • Social Media activities
  • Past purchase patterns Customer Demographics:
  • Age, address and gender
  • Service usage
Churn Prevention:
Customer churn – when subscribers jump from network to network in search of bargains – is one of the biggest challenges confronting a telecom company. It is far more costly to acquire new customers than to cater to existing ones. Common causes of churn include high prices, poor service, poor connection quality, new competitors and outdated technology.
To prevent churn, data scientists are employing both real-time and predictive analytics to:
  • Combine variables (e.g., calls made, minutes used, number of texts sent, average bill amount) to predict the likelihood of change.
  • Target specific customer segments with personalized promotions based on historical behavior.
  • React to retain customers as soon as change is noted.

Home | About us | Services | Projects | Contact Us
Copyright ATPL © 2011. All rights Reserved
Designed By Ebizinfo