“Big Data is not about the Data. It is about the Analytics.”

The title is a very popular quote on big data by Gary King, a professor at Harvard University. Every business understands the power of data, but very few are able to successfully harness it. The limitations are not around the answers you derive from data. The challenges are around asking the right questions. Only a

The Best Metric to Measure Accuracy of Classification Models

Unlike evaluating the accuracy of models that predict a continuous or discrete dependent variable like Linear Regression models, evaluating the accuracy of a classification model could be more complex and time-consuming. Before measuring the accuracy of classification models, an analyst would first measure its robustness with the help of metrics such as AIC-BIC, AUC-ROC, AUC- PR, Kolmogorov-Smirnov chart, etc. The

How to Conduct Cohort and Segment Analysis

CleverTap recently answered a question on our Quora channel. The developer is a creating a mobile app that will eventually have a web interface. The App is being built off of the API and they have already created a”Web” Back-end (They decided to pivot to a mobile first apporach). The developer is seeking a comprehensive solution where they can

A Primer on Logistic Regression – Part I

In the real world, we often come across scenarios which requires to make decisions that result into finite outcomes, like the below examples, Will it rain today? Will I reach office on time today? Would a child graduate from his/her university? Does sedentary lifestyle increase the chances to get the heart disease? Does smoking lead

A Neat Trick to Increase Robustness of Regression Models

The first predictive model that an analyst encounters is Linear Regression. A linear regression line has an equation of the form, where X = explanatory variable, Y = dependent variable, a = intercept and b = coefficient. In order to find the intercept and coefficients of a linear regression line, the above equation is generally solved by

How Do We Perceive Analytics or Data Science?

Is the purpose of analytics or data science to draw some insights from data or some cool visualization or is it just a recommendation based on some metric we deem important? The list could be endless. But, what is true analytics or data science? Let’s begin with the definition of Analytics and Data Science given by Wikipedia. Analytics: Analytics

The Fallacy of Seeing Patterns

Human beings try to find patterns to explain the reason behind almost every phenomenon, but that doesn’t mean that there is a pattern to rely on. Superstitions are a classic example where spurious patterns were generalized to explain many a phenomena. As Analysts, we are on the lookout for patterns and quite often, either knowingly

Data Analytics Model’s Life Cycle

What does a data analytics model’s life cycle look like? How often do they get deployed in a company? And what happens to the ones that aren’t deployed? Sunil Thomas, CEO for CleverTap recently answered this question on his Quora channel. Just like software development methodologies have progressed over time to become more agile and

Using Time of Day to Create Segments and Campaigns

One of the most crucial insights you can gain about your users is understanding when they perform activities in your app. People who are active early in they day may behave completely differently from night owls. Some businesses built around serving users at specific times of the day such as the morning commute or lunchtime depend on

I Wish I Had Autobots for Data Transformation

Being a sci-fi movie buff, I would always wonder if my variables could turn into Autobots just like the movie ‘Transformers’ and make my life building statistical models that much easier. Until that day, I will have to use the available tools to transform my variables. Data Analysis Before drawing valuable insights or building predictive

A Brief Primer on Linear Regression – Part III

In Part I, we learnt the basics of Linear Regression and in Part II, we have seen that testing the assumptions in simple and multiple regression before building a regression model is analogous to knowing the rules upfront before playing a fair game. Building a regression model involves collecting predictor and response values for common samples, exploring

How to Compare Apples and Oranges ? : Part III

In the part 1 and part 2 of the series, we looked at ways to compare numerical variables and categorical variables. Let’s now look at techniques to compare mixed type of variables i.e. numerical and categorical variables together. Please read this article to visually analyze the relationship between mixed type of variables. We will work with

How to Compare Apples and Oranges ? : Part II

In the previous article, we looked at some of the ways to compare different numerical variables. In this article, we shall look at techniques to compare categorical variables with the help of an example. Assume you have been given a dataset totaling 10,000 rows containing user information on Operating System, Gender and whether the user

A Brief Primer on Linear Regression – Part II

In the first part, we had discussed that the main task for building a multiple linear regression model is to fit a straight line through a scatter plot of data points in multidimensional space, that best estimates the observed trend. While building models to analyze the data, the foremost challenge is, the correct application of

Powerful User Segmentation to Drive Growth

At CleverTap we’ve always been on the leading edge of user segmentation capabilities when you send out campaigns. Aggregate data from thousands of our customer campaigns sent every month tells us that the best performing campaigns in terms of click through rates and conversions are the following: 1. Campaigns triggered because of User Inaction These are campaigns where you trigger

How to Compare Apples and Oranges? : Part I

How often have you come across the idiom “Comparing apples and oranges”. It is a great analogy to articulate that two things can’t be compared due to the fundamental difference between them. As an analyst, you deal with such difference and make sense of it on a daily basis. Let’s take an example and understand some ways to

What is the Next Generation of Mobile Analytics Software

Mobile analytics software is at the core of a changing Data Science industry. With data-rich information that can be retrieved and evaluated across all smart devices, a competitive advantage is now available to marketers and developers on a daily basis. Unfortunately, analyzing data is not always easy. What is the next generation of mobile analytics software that will catapult