# CleverTap (18)

• The danger in comparing your campaign performance against an average - Oct 26, 2017.
Performance measurement is only meaningful when compared against a benchmark. While “average” is a good, and easy to understand metric, it could be very deceptive.

• A Data Analyst guide to A/B testing - May 9, 2017.
A/B testing is key to improving results in any marketing campaign. We examine the issues involved in its 3 main components: message variants, user group selection, and choosing the winning version.

• The Best Metric to Measure Accuracy of Classification Models - Dec 7, 2016.
Measuring accuracy of model for a classification problem (categorical output) is complex and time consuming compared to regression problems (continuous output). Let’s understand key testing metrics with example, for a classification problem.

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• A Primer on Logistic Regression – Part I - Aug 24, 2016.
Gain an understanding of logistic regression - what it is, and when and how to use it - in this post.

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• A Neat Trick to Increase Robustness of Regression Models - Aug 22, 2016.
Read this take on the validity of choosing a different approach to regression modeling. Why isn't L1 norm used more often?

• The Fallacy of Seeing Patterns - Jul 26, 2016.
Analysts are often on the lookout for patterns, often relying on spurious patterns. This post looks at some spurious patterns in univariate, bivariate & multivariate analysis.

• How to Compare Apples and Oranges ? : Part III - Jul 6, 2016.
In the previous article, look at techniques to compare categorical variables with the help of an example. In this article, we shall look at techniques to compare mixed type of variables i.e. numerical and categorical variables together.

• A Brief Primer on Linear Regression – Part III - Jul 5, 2016.
This third part of an introduction to linear regression moves past the topics covered in the first to discuss linearity, normality, outliers, and other topics of interest.

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• How to Compare Apples and Oranges, Part 2 – Categorical Variables - Jun 21, 2016.
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.

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• How to Compare Apples and Oranges – Part 1 - Jun 17, 2016.
We are always told that apples and oranges can’t be compared, they are completely different things. Learn as an analyst, how you deal with such difference and make sense of it on a daily basis.

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• A Brief Primer on Linear Regression – Part 2 - Jun 13, 2016.
This second part of an introduction to linear regression moves past the topics covered in the first to discuss linearity, normality, outliers, and other topics of interest.

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• A Brief Primer on Linear Regression – Part 1 - Jun 6, 2016.
This introduction to linear regression discusses a simple linear regression model with one predictor variable, and then extends it to the multiple linear regression model with at least two predictors.

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• Do You Need Big Data or Smart Data? Part 2 - Jun 2, 2016.
It can be easy to get carried away with the deluge of big data and to rely on its abundance to deliver better models. However, use of data without context and objective could prove counterproductive; contextual and objective driven samples from the large volume and variety of data can be effective tools.

• Do You Need Big Data or Smart Data? Part 1 - Jun 1, 2016.
Analyzing Big Data without paying attention to its characteristics and objective can be detrimental, the fix for which can be correct and effective sampling. Read on to transform your Big Data to Smart Data.

• How to Use Cohort Analysis to Improve Customer Retention - May 2, 2016.
Cohort analysis is a subset of behavioral analytics that takes the user data and breaks them into related groups for analysis. Let’s understand using cohort analysis with an example of daily cohort of app users.

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• How to Remove Duplicates in Large Datasets - Apr 27, 2016.
Dealing with huge datasets can be tricky, especially the data cleaning process. One of such processing is de-duplication, find out how you can solve this using the statistical techniques.

• How to Use Cohort Data to Analyze User Behavior - Mar 10, 2016.
In the world of data analysis, cohorts are often pushed aside due to their seemingly complex nature. Learn what this analysis can offer and how to do it.

• Deriving Better Insights from Time Series Data with Cycle Plots - Mar 9, 2016.
Visualization plays key role in analysis of time series data, to understand underlying trends. Here we are demonstrating the cycle plot which shows both the cycle or trend and the day-of-the-week or the month-of-the-year effect.