I still recall the early days of running conversion optimization projects. Clients would come in with horrible websites, outdated designs, and never had paid any attention to usability.

You make a few fixes and test them against the control. And BAM! You have an amazing increase in conversions.

For most websites, long gone are these days.

Sites are well designed. Most good designers understand fundamental usability principals. Most websites have the basics covered.

The good news is that we can now focus on more pressing conversion issues. The bad news is that it takes significantly longer to …

Any experiment that involves later statistical inference requires a sample size calculation done BEFORE such an experiment starts. A/B testing is no exception. Calculating the minimum number of visitors required for an AB test prior to starting prevents us from running the test for a smaller sample size, thus having an “underpowered” test.

I will take you back to 2007 to one of the very first CRO projects we conducted for a large online retailer. The website received a massive amount of traffic (100k per day). Since it was one of our first projects, and the team was a bit trigger happy with AB testing, our first test included 30 different designs to run against the control. The headache of implementing all of these designs was overwhelming. None the less, we launched the test after about six weeks of implementation.

Seven of our challengers beat the original design within five days. The team …

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I have meant to write this post for a long time. There has been a lot written about one-tailed vs. two-tailed tests. However, most of the articles approach the topic from a purely statistical perspective providing many formulas but do not show how to do the calculations. Others articles approach the issue from a high-level business perspective. So, here is my attempt to delve deep into the topic. Some sections in this article are heavy on stats and formulas. If you are not interested in these, you can skip them! Special thanks to our data sciences team …

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Test this, test that…and always be testing.

As I listened to one speaker after the next, I wondered why a client would hire them in the first place. If you are a CRO agency, and the best advice you can provide to attendees in a conference is to test this and test that, then I am not sure why these companies would hire you in the first place.

All you need to do is start AB testing, find that one fantastic test that will take you from absolute anonymity to an overnight web success…that is what brings most …

A conversion optimization project is typically divided into four different phases that intertwine:

Scrutinize stage: You conduct various activities to determine possible testing areas on a platform or campaign Hypothesis: you create a hypothesis for the different testing ideas identified during the scrutinize phase Implement: you validate your hypothesis through the process of AB or usability testing Propagate: you look for insights, share them within the organization, across marketing channels and verticals

As you go through the process of identifying conversion issues on your website (scrutinize), you can easily identify anywhere from 150 to 250 possible test scenarios. Shana Rusonis …

Wild guesses and opinions often only result in delusional outcomes. Though opinions are by far the most widely accepted truth, they rarely are factual. It is a better practice to lean on deductive reasoning and logic instead of guesswork. But how do you arrive at what is labeled as a fact or a valid statement? Experimentation is the key.

Businesses realize the importance of scientific experimentation and testing. Many product-based enterprises have imbibed experimentation in their organizational culture. The reason – innovation cannot result without experimentation. Moreover, though there are millions of ideas that spur every minute, every hour, it …

There are few key performance indicators that everyone focuses on for an e-commerce store: conversion rates, average order value and the number of monthly visitors. These metrics translate into money in the bank (or lack thereof).

Each of these KPIs is influenced by a variety of other metrics that causes them to go up or down.

It is important to state that abandonment rates are incredibly site specific. Establishing market standards for them is difficult. Each site, with its customer base and specific audience, will require its unique evaluation and solutions.