Why will marketers spend literally hours discussing whether the a headline should be 12- or 16-point type but completely neglect to use analytics to investigate their consumer data and increase response rates?
In a recent survey, I discovered many marketers are interested in more information about predictive modeling. Their self-reported interest level is high, but their knowledge level is low.
If you’re a marketer, especially in the telecommunications, financial, insurance, credit, banking, or retail industries, and you don’t extensively use sophisticated predictive models to increase marketing return on investment (ROI), you need to read this column and implement predictive modeling.
Part one explains the basics of what a model is and how it’s developed. The following parts will describe different types of algorithms (regression, CHAID, etc.) used in model development, as well as how models are used.
What Is a Predictive Model?
A predictive model is simply an equation used to predict something.
In a silly but effective example, I often illustrate what a predictive model is by collecting shoe sizes and heights from a group of people. Then, using SPSS (define), I make a quick x/y plot that shows how generally taller people have larger shoes sizes than shorter people. Then I use SPSS to quickly determine an equation that allows me to predict someone’s shoe size if I know his height.
That equation is a predictive model. That’s really all there is to it.
Obviously in marketing, you’re not usually interested in predicting a customer’s shoe size, but you are interested in predicting:
- How much money a customer will spend on your products
- How likely someone is to respond to an offer
- How likely someone is to stop using your services
If the correct data are available, we can use them (along with some sort of statistical software package) to develop a predictive model that will tell us the answer to each of the above questions. Marketers then use the predictive model (i.e., the equation) to score and rank prospects based how much money they’re likely to spend, how likely they are to respond to an offer, and so on. Marketers can then tailor their efforts accordingly.
Why Is It Important?
Direct marketing’s most important aspect is your specific target market. In the case of email, direct mail, or outbound telemarketing, your target market is specified by your lists. It doesn’t matter how good your product or service is or how wonderful your creative. If you send your piece to the wrong list, results will be poor.
A predictive model provides you with the best names possible. That results in the best possible response to your marketing efforts.
How Are They Developed?
To develop a predictive model, you must first decide exactly what it is you want to predict. There are many possibilities, including:
- Who is most likely to respond to a mailing?
- Who is most likely to buy a specific new product or service?
- Who is likely to defect to a competitor?
- Who is likely to be a high credit risk?
- Who is likely to be a high-value customer?
After you know what you’re trying to predict, you then must make sure the correct data are available for model development. If you don’t have appropriate initial data from which to build the model, a sample test may have to be run to collect some. You might conduct a small test campaign for a new product across a number of market segments and use the resulting data to develop a model of who buys the product.
Once the required data set is acquired, statistical analysis software (e.g., SAS (define), SPSS) is used to determine which variables influence what you’re trying to predict. You might find, for example, the response rate to your mailing varies by recipient’s income and age. Income and age are then considered “predictive” variables, since you can use them to predict how likely someone is to respond. After determining which variables are predictive, the analysis software determines the “weights” (or “coefficients”) for each predictive variable.
Various Algorithms and Software
There are a few different algorithms from which an analyst or marketer can chose when she wants to develop a model. I’ll discuss those in part two (as well as the software packages that feature the algorithms) and describe how models are used. I’ll also discuss the results they can help you achieve.
Marketers need to know what’s in their data and trim out the filler to provide continuous, data-driven ROI for their brands.
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