Wouldn’t it be great if there was a more accurate way to predict whether your prospect will buy rather than just taking an educated guess? Well, there is…if you have enough data on your previous prospects. The tool that makes this possible is called Logistic Regression and can be easily implemented in Excel. Logistic Regression can be hugely valuable tool to a marketer.

**Customer Quality Scores Are Created With Logistic Regression**

Marketers use Logistic Regression to rank their prospects with a quality score which indicates that prospect’s likelihood to buy. The more data you’ve collected from previous prospects, the more accurately you’ll be able to use Logistic Regression in Excel to calculate your new prospect’s probability of purchasing. Why is that valuable? Logistic Regression can enable a marketer to determine which prospects are worth extra attention. The old saying goes: “I don’t want every sale, just the next one.” Logistic Regression greatly increases the probability that the next sale you decide to focus on will go your way.

**What Is Logistic Regression?**

Logistic regression (LR) is normally used to calculate the probability of an event occurring. Logistic regression analysis is performed by fitting data to a logit regression function logistic curve. **cargo Surabaya Bandung** The input variables (the predictor variables) can be numerical or categorical (dummy input variables).

LR is often called logit regression, the logistic model, or logit regression.

**Using Logistic Regression**

Logistic regression is used in social and medical sciences. For example, one medical use of LR might be used to predict whether a person will have a stroke based upon the person’s height, weight, and age. Marketers often use logistic regression to calculate the probability of whether or not a prospect will purchase.

Here is how the calculation is done (without wasting much time on theory):

The only variable in the above equation is L. L is called the Logit. The formula for L depends on the input variables. As a logistic regression example, if we were trying to predict the probability of a new prospect buying based upon the prospect’s age and gender, then the equation for the Logit (L) would be the following:

We need to solve for Constant, A, and B. Once we have solved for these, we have solved for L. L can then be plugged into the probability equation P(X) above and we have the probability of the prospect purchasing.