Simplify Complex Information
CUBOT custom analytics apps maximize your data mileage.
Quick to develop and powerful in analytical outputs, CUBOT custom apps solve real world business problems and help you grow with data.
From a Data Perspective
Predominantly unstructured text data sources – they lend themselves to text processing and analytics and require appropriate visualization methods. We process text through standard techniques such as stemming, stopword removal, etc. and apply classification algorithms to work on these data sources. Find out things like popular terms, subjects and sentiment from data.
The velocity and volume of sensor and machine-generated data sources of information lend themselves better to customized analytic and big data solutions. Use a mix of pattern-finding methods and statistics to understand as well as predict future outcomes – for example when an event may occur.
Supervised & Unsupervised Data Clustering
Derive Groups & Outliers
Time to An Event
Predict The Time of an Outcome
Similarity based on Users & Product Purchases
Amazon Style Recommendation Engine
Network Analysis
Derive Groups & Outliers
Time Series Analysis
Revenue & Metrics
Rule-Based Mining & Sentiment Analysis
Identify Key Words, Terms, Sentiment
Determine what impacts an outcome
Attribute & Pattern Finding Methods
Items Frequently Bought Together
Beer and Diaper Classic Example
Analyze Twitter/Facebook Data
Likes, Topics, Words, Sentiment, Trending Insights
Leverage Telecom Data to Perform Better
- Understand customer online activity – what are customer interests and profiles? These in turn can be offered to partners that have products & services for customer segments.
- Network operations – find out when the next fault/failure/incident could occur.
- Make use of location and temporal information more effectively in order to provide timely offers.
- Mine unstructured data sources to understand key patterns and trends, and look for behavioral, purchase, and lifestyle patterns.
Applied Analytics for Organizations in the Financial Sector
- Better Credit Scoring Process by using Customer Transaction Data
- Identify and Prevent Defaults, Improve Collections
- Target existing as well as new customers to the banks products & services through cross-sell and up-sell intelligence
- Mine transaction data to identify unusual and fraudulent behavior, and use data patterns to predict such events.