Artificial Intelligence

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Customer Service Models

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Urgency

Predict how urgent is the this blog, tweet or comment is for a customer service agent to look at.

Customer Dissatisfaction

Predicts whether customer is dissatisfied or satisfied in the comment posted.

Customer Inquiry

Predicts whether this comment is worth seen by the customer service agent or not.

Service

Predicts whether comment has details of company’s services and products or not.

Location

Using entities extraction and a combination of other algorithms, model predicts the location of the comment or tweet.

Machine Learning Model

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Lapse Rate (Churn)

The Churn model predicts the probability of a particular customer switching to other company’s services.  We have utilized the advancement in the field of Machine learning to develop this model to predict upcoming lapses with high accuracy.

Propensity Model

Model predicts the chances of a customer buying the product. In other ways, it tells how qualified a lead is.

Cross Sell / Up Sell Model

The objective of cross-selling and up-selling is to  increase the revenue derived from the existing clients.

Hiring Model

It tells based on your highly performant employees, which of the applicant will be best suited for the job posting.

Patient Readmission Model

This is a predictive model that gives the probability of an inpatient’s risk of getting re-admitted in the next 30 days of his discharge. This model also predicts the approximate number of patients that would be readmitted in the hospital if the data supplied contains a bunch of patients.

Fraud Prevention Model

Predicting which claim might need close attention by claim reviewer.

Lifestyle Models

To Predict lifestyle of the customer, agent or patient, we build various model which classify lifestyle of the person along the following dimensions:

Games

Predict how much and what games are liked by the person using the text data provided.

Music

Predict which kind of music (if any) is liked by the person.

Finance

Predict how finance or money conscious is the person.

Family

Predict how much family oriented this person is. If a person is talking to about his / her family members, their joy together then he/she will be classified as more family oriented person.

Education

Predict how much person is education oriented.

Political Interest

Predict how much are they involved in political party (and which one)

Travel

Predict if person likes traveling or not.

Religion

Predict how much person is religious based on the text provided.

Apparel

Predict how much importance does a person gives to apparel/clothes.

Health

Predict how much a person is health and wellness oriented.

Business Area Models

Business models classify the business area of the comment in following dimensions:

Product Launch

Predicts whether the text contains product launch related information. This information is very useful for investment bankers and algorithmic trading cases.

Crisis

Predicts whether there is very bad news (level of bad news) or comment.

Pricing

Predicts whether comment has product pricing information or not.

Product Feature

Predicts if the comment is talking about a product feature.

Market Insight

Predicts whether the comment has market insight or not.

Financial Issues

Predicts whether the comment is highlighting about company’s financial issue.

Corporate Governance

Predicts whether this comment should be reviewed by a person responsible for corporate governance or not.

Legal Issues

Predicts if this comment should be reviewed by legal counsel or not.

M&A (MA)

Predicts whether the comment or article is talking about a M&A.

Marketing Models

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Promotion

It predicts whether a comment is a promotional text or not 

Control / Prevent Disease

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Vector Borne Disease Prediction

Using weather, biological ruleset of disease, claims data, and socio-economic data, predict the risk of VBD at a particular geo location.

Sentiment Engine

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Sentiments

Artificial algorithm that predicts sentiment of the text into Positive, Negative, and Neutral.

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