Twitter Analytics
Market Basket Analytics

Simplify Complex Information

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Box & Whisker Plots
Great for distributions
Displays outliers easily and describes data spread
Sankey Diagram
Show Flows & Filters
Great for customer flow to highlight drop-off points
Network Diagram
Highlight Relationships
Understand product or customer relationships clearly
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Cluster Plots
Show groups of information
Visualize naturally occurring groups
Hierarchical Dendrogram
Show information hierarchies
Display relationships
Decision Trees
Highlight causative factors
Understand root cause easily

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.

Analytical Programming
Analytical Programming
Use computational methods to process data and get desired analytical views. These can be used to create derived variables, derived attributes and other fields based on business concepts and ideas.
Data Science
Use established data science methods to derive deeper insights from your data. Use data inferences to test hypotheses, and ultimately create actionable outputs.
Application Development
We create and deploy analytics to the enterprise. Web-based and acccessible by authorized devices/security levels, create presentable and shareable knowledge to the enterprise - FAST.

From a Data Perspective

Chat Transcripts/E-mail Data, Social Feeds

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.

CRM Data

Understand operational efficiency patterns, location intelligence and identify predictors to successful sales conversions.

Transaction Information

Mine transaction data to identify key patterns in bills. Find out items purchased together, temporal and geographic patterns. Use these to suggest product recommendations to existing/prospective customers. Incorporate cross-sell and up-sell intelligence into your systems.

Revenue & Financial Data

Use historical time-series patterns to understand what might happen tomorrow. Forecast Revenue using configured statistical methods. Mine Data to be informed on Costs. Understand what are the top predictors for upward sales and focus on areas that matter.

Sensor & Machine 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.

Advanced Analytics Techniques
Clustering & Classification

Supervised & Unsupervised Data Clustering

Derive Groups & Outliers

Survival Analysis

Time to An Event

Predict The Time of an Outcome

Collaborative Filtering

Similarity based on Users & Product Purchases

Amazon Style Recommendation Engine


Network Analysis

Derive Groups & Outliers


Time Series Analysis

Revenue & Metrics

Text Analysis

Rule-Based Mining & Sentiment Analysis

Identify Key Words, Terms, Sentiment

Top Predictors

Determine what impacts an outcome

Attribute & Pattern Finding Methods

Market Basket Analytics

Items Frequently Bought Together

Beer and Diaper Classic Example

Social Media Analytics

Analyze Twitter/Facebook Data

Likes, Topics, Words, Sentiment, Trending Insights

Applied Telecom Analytics

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 Financial Services Analytics

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.