What is Data Analytics? Data Analytics are AI-based systems.
They use advanced analytic techniques on huge and diverse data sets including structured and unstructured data,
streaming data and batch data. Enormous amount of data comes from sensors, devices, video/audio, networks, log files,
web, social media and internal applications data. The size and type of data sets are beyond the ability of traditional
relational databases to manage and process.
Advanced analytics are mainly used in predictive analytics, machine learning, data mining, text analytics and
natural language processing.
We are here to fill the skill gap between data analytics know-how and domain knowledge owners
and provide skill transfer if needed. Below are our Analytic Services:
(A) Retail / eCommerce / B2C
It is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical internal and external data.
Predictive models use known results to train a model that will be used for predicting values for new data. The model generates a probability of the target variable values based on a set of input variables.
Predictive models can do the following:
- Customer Segmentation & Targeting
- Predictive Product Recommendations
- Product Price Optimization
- Predictive Analytics on Customer Behavior Pattern
You are welcome to visit Discover Behavior Pattern
our community version.
(B) Text Mining / Sentiment Analysis
This includes mining and analyzing text-based information from documents, reports, emails, news feed, database,
web-sites, blogs, and social media, e.g. the information in the company's internal documents, and information on Facebook,
twitter, Google, Yahoo, LinkedIn, and other websites to derive relevant, new, useful high-quality information and actionable
Based on the text-based analytics, a company can perform sentiment analysis: analyze the unstructured text of websites
contents, blogs, social media, social networking posts, incoming documents and emails to determine the user sentiment
related to particular companies, brands or products.
(C) Inventory Prediction
Retailers are always interested in predicting the level of their stock items so that they could reduce the waste of
overstock and maximize their revenue by not understock.
Similarly manufacturers needs to have good prediction on their inventory items so that their production will not be
truncated, contract will not be jeopardized and revenue will be maximized.
(D) Fraud Detection
Probability of Default (PD) is the most important component of credit risk, the goal is to identify efficient features on PD and estimate PD for new loans in the category of corporate asset. Machine learning algorithms have quite good predictive power on PD.