SMB owners and marketers would love to be able to read the minds of their customers.
And why not? Such insights could allow them to provide better products and better services for their customers and increase customer value.
But this can be difficult in practice, but there are options for leveraging existing data to make better decisions and provide greater value to customers.
One such approach is known as a method called logistic regression.
A logistic regression is a method that quantifies the probability of some event occurring and is a useful way to measure the odds a customer will buy your product, use your service, and loyalty.
This statistical method has applications across numerous disciplines in science and can be a valuable tool in the arsenal of an SMB.
The logistic model is similar to other forms of regression: Logit = L = Constant + a*v1 + b*v2 +...
x*vn The difference from more traditional regression approaches is that the logistic regression relies on categorical data.
The logit is the binary variable being regressed (buy/no buy, yes/no, etc.
) represented as 1 or 0.
Each explanatory variable (v) can be binary or continuous value that predicts the logit, and the related coefficients are how much the odds will change based on incrementing or decrementing that explanatory variable by 1 unit.
The logistic regression is simple to apply and interpret, which makes it a popular option for analyzing categorical events.
Here are 3 ways you can use logistic regression to support your decision-making framework.
1.
Credit Risk Scoring Logistic regression can be developed to determine the level of risk of default.
Customer variables (purchase behavior, age of person/company, size of company, etc.
) and macro variables (GDP, seasonality, etc.
) will quantify the likelihood that a particular will default.
This will allow you to make better decisions on the terms of offering credit to customers.
2.
Customer Scoring Wouldn't it be great to understand which customers are likely to buy your product or service? By using the data you have collected on your customers' characteristics such as geography and demographics to estimate the probability that a customer will purchase.
The resulting scoring can be used to focus resources on the highly likely buyers and test different ways to improve buy rates on lower performing customer segments.
3.
Customer Loyalty As we have all heard, it is much less expensive to retain a customer than it is to acquire a new one.
The logistic regression can help you figure out which customers are likely to renew and/or come back.
This is valuable information that will allow you to target segments with communications that will encourage loyal behavior.
And why not? Such insights could allow them to provide better products and better services for their customers and increase customer value.
But this can be difficult in practice, but there are options for leveraging existing data to make better decisions and provide greater value to customers.
One such approach is known as a method called logistic regression.
A logistic regression is a method that quantifies the probability of some event occurring and is a useful way to measure the odds a customer will buy your product, use your service, and loyalty.
This statistical method has applications across numerous disciplines in science and can be a valuable tool in the arsenal of an SMB.
The logistic model is similar to other forms of regression: Logit = L = Constant + a*v1 + b*v2 +...
x*vn The difference from more traditional regression approaches is that the logistic regression relies on categorical data.
The logit is the binary variable being regressed (buy/no buy, yes/no, etc.
) represented as 1 or 0.
Each explanatory variable (v) can be binary or continuous value that predicts the logit, and the related coefficients are how much the odds will change based on incrementing or decrementing that explanatory variable by 1 unit.
The logistic regression is simple to apply and interpret, which makes it a popular option for analyzing categorical events.
Here are 3 ways you can use logistic regression to support your decision-making framework.
1.
Credit Risk Scoring Logistic regression can be developed to determine the level of risk of default.
Customer variables (purchase behavior, age of person/company, size of company, etc.
) and macro variables (GDP, seasonality, etc.
) will quantify the likelihood that a particular will default.
This will allow you to make better decisions on the terms of offering credit to customers.
2.
Customer Scoring Wouldn't it be great to understand which customers are likely to buy your product or service? By using the data you have collected on your customers' characteristics such as geography and demographics to estimate the probability that a customer will purchase.
The resulting scoring can be used to focus resources on the highly likely buyers and test different ways to improve buy rates on lower performing customer segments.
3.
Customer Loyalty As we have all heard, it is much less expensive to retain a customer than it is to acquire a new one.
The logistic regression can help you figure out which customers are likely to renew and/or come back.
This is valuable information that will allow you to target segments with communications that will encourage loyal behavior.
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