Monthly Archives: April 2018

How to Measure Customer Satisfaction

                      Customer Satisfaction Score (CSAT) Customer Effort Score (CES) Net Promoter Score (NPS®)* Question How would you rate your experience with your … (e.g. recent support requirement)? The organization made it easy for me to handle my issue On a scale of 0-10 how likely would […]

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Dealing with Imbalanced Data

  What is Imbalanced Data? Imbalanced data typically refers to a problem with classification problems where the classes are not represented equally. Imbalanced data example: the red points are greatly outnumbered by the blue. In reality, datasets can get far more imbalanced than this. Here are some examples: About 2% of credit card accounts are […]

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Customer Experience

Good customer experience leaves people feeling heard and appreciated; it minimizes friction, maximizes efficiency and maintains a human element. We asked 15,000 consumers in 12 countries what it takes to deliver the kind of experience that keeps them satisfied and coming back. Bad experiences are driving customers away—faster than you think If you think you’ll […]

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How to Handle Missing Value?

Imputation vs Removing Data Before jumping to the methods of data imputation, we have to understand the reason why data goes missing. Missing at Random (MAR): Missing at random means that the propensity for a data point to be missing is not related to the missing data, but it is related to some of the observed data […]

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Multicollinearity

In statistics, multicollinearity (also collinearity)  refers to predictors that are correlated with other predictors in the model.It is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy. In the presence of high multicollinearity, the confidence intervals of the coefficients tend to become very wide and the statistics […]

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Measuring the Success of a Feature

Step 1: Measure basic usage of the new feature First, you need to answer the most basic question: are people using the feature? There are a few key metrics you should look at to get a complete answer to that question: total number of times people are using the feature the number of unique users […]

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AI in Banking and Payments

How artificial intelligence is cutting costs, building loyalty, and enhancing security across financial services?   Artificial intelligence (AI) is one of the most commonly referenced terms by financial institutions (FIs) and payments firms when describing their vision for the future of financial services. AI can be applied in almost every area of financial services, but […]

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Millennials (Generation Y)

 

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Machine Learning Algorithms

      1. Supervised Learning How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the […]

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How to Detect and Avoid Overfitting

What is overfitting? overfitting is when the ML model does not generalize well to data it has not seen before. It overfits the training data, usually indicating it has too much complexity in regards to the training data size. This trade-off between too simple (high bias) vs. too complex (high variance) is a key concept […]

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