The bulk of predictive modeling doesn’t involve statistics, but rather research, analysis, and implementation
Some catalogers may be intimidated by the techniques required to build a statistics-based predictive model. But actually generating the predictive model – that is, creating the scoring equation – makes up about only 10% of the entire six-step process. The remaining 90% encompasses the nonpredictive part of predictive modeling: developing a sound research design, creating accurate analysis files, performing careful exploratory data analysis, implementing the model, and creating ongoing quality control procedures.
While many database articles focus on the mathematics-intensive predictive modeling part – step #4 – the rest of the process is just as, if not more, important. Regardless of the modeling technique that you use, if you stint on any of the nonpredictive steps, you’ll probably end up with a model that does not perform well.
Step #1: Developing a research design
In developing a research design, you’re actually coming up with a realistic goal and a practical strategy for attaining it. A sound research design encompasses five components:
- a solvable problem
- representative mailings
- an optimal dependent variable (the behavior that the model is trying to predict, such as response or sales)
- identification of selection bias (factors that can misleadingly skew the results)
- an appropriate modeling universe.
The first component may seem obvious, but many companies look to a predictive model to solve a problem that no model could ever solve. One catalog client wanted to use modeling to double the overall response rate of rental lists while maintaining the size of its prospecting mailings – a task that probably would require a marketing revolution, not a model. Although it’s possible to build a predictive model to find segments of rental lists that perform at twice the average rate, these segments will generally represent just a minor portion of the total universe.
After identifying a solvable problem, you must then select a subset of representative past promotions for the analysis file. Even under ideal circumstances, it’s challenging to predict future behavior by examining the past. At the very least, you must work with a “typical” past that is expected to be similar to the future, and not an unrepresentative historical period. For example, a fundraising mailing for a gun control organization or a National Rifle Association promotion that coincided with the Columbine High School tragedy in Colorado this past April would not be a good candidate for a predictive model, because response patterns during this highly emotional time are not likely to sustain themselves into the future.
It’s also important to determine the optimal dependent variable. The most common factors catalogers try to predict with models are response and sales, both of which can be further broken down into gross vs. net. And sometimes profit is the target. But there are no hard-and-fast rules about which dependent variable to use – the circumstances of your business and associated goals should determine the appropriate variable. Also, good old-fashioned testing can help. You might try building two or three types of models off the same data set using different dependent variables.
Be forewarned that an otherwise well-constructed predictive model can fall victim to selection bias. Any model built off a heavily prescreened group of promotions and then put into production without the same prescreen risks failing. For example, a women’s careerwear cataloger that has identified its target audience as female yuppies may always screen males from prospect mailings to maximize response, so any resulting model will not evaluate gender. If the model is not consequently implemented with a gender screen, there will be nothing to prevent it from identifying male yuppies as excellent prospects.
And finally, you must arrive at the appropriate modeling universe. It often makes sense to create subset universes and build multiple, specialized models, such as splitting multibuyers from single buyers. Many of the variables that are likely to drive a multibuyer model do not apply to single buyers, such as the length of time between the first and second orders.
Step #2: Creating analysis files
You must be sure that the analysis file is accurate, because the complex process of appending response information to the promotion history files can often render an otherwise perfect research design worthless. It’s also possible that the underlying database can be flawed. For instance, a catalog/retail client decided to build a point-of-sale retail database using reverse-phone-number look-up. Checkout counter employees asked customers to supply their telephone numbers, which were then cross-checked against a digital directory to identify the corresponding name and address to the phone number. This way, information about the items purchased could be tracked to specific individuals and households, and a robust historical database constructed.
Although the average order size for the retail side of the business was about $80, the data also showed that a small but significant number of customers had orders totaling several thousands of dollars.
But before the client could envision ways to leverage these superbuyers, research from the analysts revealed that most were hardly super, and many were not even buyers. Certain sales clerks resented having to request a phone number from every customer. Some minimized this obligation by recording the phone numbers of each day’s initial 10-15 customers and then recycling them for all subsequent customers; other clerks entered their own phone numbers and those of their friends; and some did the same with random numbers from the phone book. These strategies generated “pseudobuyers” rather than superbuyers. A predictive model that included such observations would be far from optimal. After all, the oldest rule of database marketing is “garbage in, garbage out.”
Step #3: Exploratory data analysis
Even with a sound research design and accurate analysis files, the work involved in building a model has only just begun. While all of the predictive modeling software packages on the market are able to recognize patterns within the data, it’s hard to identify those patterns that make sense and are likely to hold up over time.
Exploratory data analysis will help you determine whether the relationships among the potential predictors (that is, the historical factors you’re considering including in the model, such as RFM and overlay demographics) and the dependent variable make ongoing sense. But make sure only those relevant potential predictors end up in the final model.
A good analyst will capture the underlying dynamics of the business being modeled, a process that involves defining potential predictors that are permutations of fields within the database. An example might be the total number of orders divided by the number of months on file.
You must also identify and either eliminate or control any errors, outliers, and anomalies. Errors are data that don’t reflect reality; outliers are real but atypical behaviors, such as a $10,000 order in a business that otherwise averages $80 orders; and anomalies are real but unusual behaviors caused by atypical circumstances, such as poor response due to call center problems. (For more on exploratory data analysis, see “Data detectives,” May 1998 issue.)
Step #5: Deploying the model
A model is worthless if you can’t accurately deploy it in a live environment, but database formats can change between the time that you build the model and when you deploy it. It’s also common that the values within a given field will be altered. Say you have a department field in a retail customer model. If the value of “06″ corresponds to “sporting goods” on the analysis file but to “jewelry” on the database, then the model is not likely to succeed.
The chart above makes intuitive sense in that the best-performing decile displays the most recent buyers on average, as well as the highest average number of orders and total dollars. Patterns across the other nine deciles also make sense, with buyers becoming less recent, and average orders as well as average dollars declining consistently.
To gain confidence in a model, you should:
- deploy it on an appropriate universe
- sort the corresponding individuals from highest to lowest performance as predicted by the model, or from highest to lowest model score
- divide these sorted individuals into units of equal size (often, a grouping of 10 units called a decile is used)
- profile each of these units.
Step #6: Creating ongoing quality-control procedures
This is an extension of the previous step, because a model can be deployed for as long as several years, but you have to establish quality-control procedures to ensure that the model continues to function the way the analyst intended.
For effective quality control, you should create profiles of the model units every time you deploy the model in a live environment, and compare the profiles with the original analysis file profiles. If you don’t see consistency over time, there may have been changes within the database, subtle or otherwise, that require further detective work to unravel. Such inconsistencies raise a red flag that can help identify potential difficulties before they become problematic.
As an extreme example of the costly mistakes that can occur without quality control, consider a customer predictive model that was built for a cataloger by an independent analytic consulting firm. The model was forwarded to the service bureau that maintained the cataloger’s database, accompanied with the instruction to “pull off the top four deciles.” No quality-control procedures were included. The service bureau was mindful of industry standards and proceeded to select deciles 1 to 4 for the promotion.
The results were abysmal and came close to putting the client out of business. A post-mortem uncovered the embarrassing fact that the analytic shop, contrary to industry standards, had labeled its best-to-worst deciles from 10 to 1. As a result, the four worst deciles had actually been mailed.
With quality-control reports, this disaster would have been avoided. For example, the service bureau would have known that a problem existed when it saw the following recency profile:
It’s important to remember that there is no magical short cut when building a predictive model. If you want good results, concentrate on the nonpredictive part of the predictive modeling process: developing a sound research design, creating accurate analysis files, performing careful exploratory data analysis, accurately putting the model into production, and creating ongoing quality-control procedures. With careful attention to detail in these five areas, you should be able to better target your most responsive customers and prospects.