Mar 30, 2022
In General Discussions
Advanced digital marketing forces us to email list go beyond what everyone else is doing and approach it from new angles. One of the ways to stand out in your sem analysis and performance is to use advanced techniques like regression analysis. Regression is actually a basic form of machine learning (ml) and a relatively simple mathematical application. This type of analysis can help you make better predictions from your data beyond educated guesses. Regression might sound scary, but it's not that advanced in the world of math. For anyone who passed math in 10th grade, you've probably worked with the regression formula before. We'll look at using regression in your google ads to predict how much conversion you can get by adjusting campaign spend. Building the model and applying it is much easier than you think! What is regression? A regression model is an algorithm that attempts to email list best fit the data presented. In essence, it's a line of best fit. It can be linear, like a straight line passing through the data, or non-linear, like an exponential curve, which curves upward. By fitting a curve to the data, you can then make predictions to explain the relationship between a dependent variable and one or more independent variables. The graph below shows a simple linear regression email list between an independent variable “cost” (daily spend on google ads) on the x-axis and a dependent variable “conversions” (daily conversion volume on google ads) on the y axis. We fitted a linear regression line (blue). We can now say that at $3,000 on the axis, that point on the regression line would correspond to 35 conversions. So, based on the data-fitted regression model, if we spend $3,000, we expect to receive 35 conversions. Headstart on feature selection I've used several of these regression models and will share what I found to be true, which will give you a head start on where to start looking. Multiple regression involves using certain independent variables (rather than just one, as in the example above), to predict a dependent variable. With google ads.