Predictive analytics for business applies to a variety of company problems faced today, and more people are beginning to recognize its value. Companies are using predictive analytics for business to answer real business questions like “What segment of potential customers will respond best to our message” and “Why am I losing customers, and how can I stop them from leaving?”
Even though the use of predictive analytics for business holds so much value for companies, the general problem with implementing them is that the knowledge of how to do predictive analytics for businesss is not readily available. Many people struggle when trying to make sense of good analysis practices, choosing appropriate predictive models for a given situation, and understanding the underlying statistics. To fill this gap of knowledge and provide an easy way to learn and take advantage of predictive analytics for business, Data Crunch Corp released new book.
Our predictive analytics for business book contains detailed chapters describing how to do good analysis, how to choose an appropriate predictive model for your situation, and how to make sure the statistics powering the model are set up right. This is all done and explained in the familiar environment of Excel 2007, so that it can benefit those who may not have access to more advanced predictive analytical packages such as SAS and SPSS.
Below, I’ve copied a section from the book that I think is extremely valuable for anyone new to predictive analytics for business. It describes two of the most important fundamentals: Seeing the data in context, and segmentation.
Seeing the Data in Context for Predictive Analytics for Business
To start with predictive analytics for business, understanding what the data is telling you within the context of the business situation being analyzed is extremely important. This will help you avoid making faulty conclusions and keep your analysis appropriate for the business question being answered. The best way to learn this fundamental is to see it in action, so we will take an example.
We will look at a type of direct mail campaign analysis. In this predictive analytics for business model, we want to know how many calls are expected to come into our call center after we execute the campaign. First, we take some historical data showing us the percentage of total calls coming in according to the number of days after starting a mail campaign, shown below.
After creating a scatter plot of the data, we try to fit a logarithmic regression line as a model, shown below.
Even though the R2 tells us that the fit is good, the model may not be the best way to explain this data when the context and purpose of this analysis is considered. We want our predictive analytics for business model to be able to predict what percentage of total calls will come in from a mailing campaign so we can staff the call center. If I were to use the line above as the model, I would be predicting low values for incoming calls between about day 20 and 100, and high values thereafter. Because of this error, we would not be staffing the call center correctly.
To create a better predictive analytics for business model, I would consider the fact that, in this context, it is not necessary to fit a trend model to the entire data set. Consider the following model, which can be used to predict the percentage of total calls coming in between days 4 and 35 after the mailing campaign:
You will notice that this predictive analytics for business trend model does not contain the same high and low errors as the previous model did. Further, upon doing some calculations on the data in the spreadsheet, we know that anything before day 4 makes up for just 8% of all calls, and anything after day 35 makes up for just 15% of all calls. I have highlighted with a model the time period of the biggest growth to the call percentage, while summarizing the remaining percentages on either side. This will give just the right amount of information needed to staff the call center, while minimizing errors I would have made trying to fit a single trend model to the data. Now that is how you should do predictive analytics for business!
The point here is to look at the data in the context of the purpose of the analysis. What are you going to use the predictive model for? Is it necessary to fit a model to the entire data set? How exact do you need to be with the prediction? What is the most important part of the data set to model? These and other questions are important to consider when performing analysis using predictive analytics for business.
The second fundamental of analysis in predictive analytics for business is the practice of segmenting the data. As with seeing the data in context, this is best described with an example. Consider the analysis presented below, which shows a linear regression model to predict how much someone will likely donate to your cause according to their age.
The fit of the model is extremely weak, and there seems to be no relationship between donation and age. However, this data was taken and aggregated from two different cities, Boston and New York. If we separate out the data according to those two cities (otherwise known as segmenting by them), we get the following when we run a regression analysis:
By segmenting the data first, we notice that there is, in fact, a relationship between donation and age, but that relationship differs depending on what city you are in.
The value of predictive analytics for business becomes apparent when you realize the following. First, making correct and data-driven business decisions has enormous value. Second, the sole focus of analytics is to help make the correct decision.
Why Use Predictive Analytics?
In business, decisions matter. What product line should you choose to achieve the most revenue? Which marketing campaign should you do to give you the highest ROI? What strategic moves should you make to take advantage of opportunities and avoid failure?
Make the wrong decision, and the results could mean anything from a small loss to complete company collapse. Make the right choice, and you can find yourself in a very good position.
Benefits of Using Business Predictive Analytics
Predictive Analytics for Business reduces uncertainty. Also, it gives you a better view of the situation. Furthermore, it can predict certain outcomes so that businesses can make correct decisions. This also allows companies to take the most productive actions to solve a problem. And the good news is, predictive analytics can be applied to just about anything. That is as long as you just understand where and how to measure data so you can get the right information.
It you think you have a business problem that can’t be solved with predictive analytics for business, I challenge you to read the book How to Measure Anything and see if it’s still unsolvable. I’m willing to bet that there’s a way to use analytics to solve your problem, and it’s probably easier than you think.