760.695.6360
info@infinityaitools.com
AI-Assisted Search

Infinity AI

  • Products
    • Ultimate AI System
    • Customized Solutions
  • Pricing
  • Support
    • 24/7 Chat Support
    • FAQs
  • Resources
    • Articles
  • About
    • About Us
    • Discovery Call
    • Contact
    • Affiliate Program
  • Log In
SignUp

Glossary of A/B Testing Terms for Marketers

A visually engaging digital library setting with books and floating 3D words such as conversion rate, control group, variable, and statistical significance, subtly incorporating modern digital tools l
Ada Astralis
Date Updated: 12 months ago
Reading Time: 3 minutes

 

Glossary of A/B Testing Terms for Marketers

Welcome to the ultimate glossary of A/B testing terms for marketers. Whether you’re a seasoned pro or a newbie in digital marketing, understanding these terms will help you navigate the world of A/B testing with ease. Let’s dive right in and decode the lingo that powers successful A/B tests.

A

  • A/B Test: A randomized experiment with two variants, A and B. It’s used to compare two versions of a webpage or app against each other to determine which one performs better.
  • A/B/n Test: Similar to A/B tests but with more than two variations, often referred to as multivariate testing.
  • Alternative Hypothesis: The hypothesis that there is a significant difference between the variations in your test.
  • Average Order Value (AOV): A metric that measures the average total of every order placed over a defined period of time.

B

  • Baseline: The control version of the test, typically the existing version of whatever you’re testing.
  • Bayesian Statistics: An approach to statistics that interprets findings in terms of probability statements that express a degree of belief.
  • Bounce Rate: The percentage of visitors who navigate away from the site after viewing only one page.

C

  • Call to Action (CTA): A prompt on a website that tells the user to take some specified action, often presented as a button or hyperlink.
  • Confidence Level: A measure that indicates how certain you are that your sample accurately reflects the population.
  • Conversion: The process of turning a visitor into a customer or getting the visitor to complete a desired action.
  • Control: The version of a webpage or app that you are testing against; the ‘original’.

D

  • Dependent Variable: The variable you measure in the experiment and what is affected during the experiment.
  • Direct Traffic: Visitors who enter your site’s URL directly into their browser or have it bookmarked.

E

  • Experimentation: The process of conducting tests or trials to discover how different features or experiences perform.

F

  • Funnel: The path or steps visitors take from initial interest to final conversion.

G

  • Goal: The specific target you aim to meet through your marketing efforts, like sign-ups, purchases, or engagement.

H

  • Heatmap: A visual representation of where users click or scroll on a page.
  • Hypothesis: A proposed explanation made on the basis of limited evidence as a starting point for further investigation.

I

  • Independent Variable: The element you change in an experiment to test its effects on the dependent variable.

K

  • Key Performance Indicator (KPI): A measurable value that demonstrates how effectively a company is achieving its key business objectives.

L

  • Landing Page: The webpage on which a visitor first lands or arrives after clicking a link or ad.
  • Lift: The increase in performance you see from your new variation versus the control.

M

  • Multivariate Testing (MVT): Testing multiple variables at the same time to understand the effect of changes.

P

  • P-value: A measure of the probability that an observed difference could have occurred just by random chance.

R

  • Randomization: The process of making something random; in the context of A/B testing, ensuring test subjects are allocated randomly.
  • Reach: The total number of people who see your content.

S

  • Segmentation: The process of dividing your audience into segments based on characteristics like demographics, behavior, and more.
  • Statistical Significance: A determination that the relationship between variables in your experiment is not due to random chance.
  • Split Testing: Another term for A/B testing, where traffic is split between two versions to determine which is more effective.

T

  • Traffic: The visitors to your website.
  • Treatment: Another term for the variation in an A/B test.
  • Turnover Rate: The rate at which visitors leave the site after visiting.

V

  • Variation: The different versions or alternatives in a test.
  • Visitor: An individual who goes to your website.

Now that you’re armed with a comprehensive glossary of A/B testing terms, go forth and optimize! Remember, every term you grasp gets you a step closer to mastering the science of A/B testing and making data-driven decisions that propel your marketing strategies forward.

 

Start Your 30 Day Free Trial of our Ultimate AI System for Online Growth

Stay in the Know! AI News and Tools.

Stay in Touch

info@infinityaitools.com

Company

→ About

→ Articles

→ Affiliates

→ Pricing

Support

→ 24/7 Chat

→ Chatbot FAQs

→ Tutorials

→ Bug Tracker

  • Refund Policy
  • Terms of Service
  • Privacy Policy
  • About
  • Affiliate Program

© 2024 Infinity AI™ Tools

Facebook
YouTube
Instagram
TikTok
Twitter