Creating Variables: Out-of-state Flag

Sometimes it’s good to see which of your students or donors are in-state because an in-state population may be more likely to enroll or be retained or give than an out-of-state population. Creating an out-of-state flag from a “state” variable allows you to easily differentiate between your in-state and out-of-state prospects. I should also note that it is just as easy to create an in-state flag if that better suits your data. In any case, here’s how:

The first step is to hook your data source to a transform node:

Because we’ll be creating a binary (“yes or no”) variable, we’ll want to click on the “if” button (at the top of the buttons on the right side), which will automatically generate an equation that we can change to suit our data. 

In the “Enter a Formula” window, we’ll want to edit the auto-generated equation so it reads:

Where ‘[A]’ is the variable in our dataset that represents state, and the term it is set equal to (in this case, ‘NH’) is the term in our dataset that represents our institution’s state. Note that we could have set state equal to ‘New Hampshire’ or a numerical code, as long as it matches the term that represents New Hampshire in our dataset. The equation outputs a variable that is equal to ‘1’ when state is NOT New Hampshire and ‘0’ otherwise, thus flagging records which are out-of-state.

The final step before naming and saving your out-of-state flag is to select “binary” from the “Result Type” list.

And, voila, it’s easy as that! You now have a quick way of identifying in-state vs. out-of-state students in your dataset; let the reporting begin!

PS: If you guys have any specific requests for a variable to be featured in the "Creating Variables" series, please leave them in the comments or email me directly!

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Creating Variables: Age

Hi all! Today I’d like to a cover a pretty universally predictive variable: age. Age can be created in relation to the date of a particular event (like an application date or a mailing date), or as a reflection of age today, at this moment. Either way, age is often predictive and easy to add to your dataset by creating it in Veera from a “birth date” field.

The first step in doing so is to hook your data source to a transform node: 

After opening the transform node, we’ll want to click on the function button and select the second “YearsBetween” function.

[Note: Veera is capable of outputting the number of years between two dates in two separate ways. The first function on the list calculates the number of years between two dates, regardless of the actual day and month, while the second function calculates the number of years between two dates taking day and month into account. To illustrate this point, take the dates December 1, 1960, and April 1, 1980. Using the first “YearsBetween” function, the number of years between these dates is 20. Using the second “Years Between” function, the number of years between these dates is 19. See the difference?]

Here, we have two options. We can (a) calculate age today or (b) calculate age at a specific point in time, depending on what we type in the “Enter a Formula” window.

(a) Age today:  

Where ‘[A]’ corresponds to the variable in your dataset that represents birthdate, and “TODAY()” is the Today function from the drop-down menu on the right. 

or

(b) Age at a specific point in time:

Where ‘[A]’ corresponds to the variable in your dataset the represents birthdate, and ‘00/00/0000’ represents the specific date on which you’d like to measure age. 

Be sure to save before exiting the transform node, and there you have it, a brand-new age variable!

PS: If you guys have any specific requests for a variable to be featured in the "creating variables" series, please leave them in the comments or email me directly!

-Caitlin Garrett, Statistical Analyst at Rapid Insight

User Conference or Bust!

Great news, guys – only three more months until our 4^th Annual Rapid Insight User Conference! Here’s what you need to know about this  inspiring, informative, and FREE event:

The user conference will take place here in Conway, New Hampshire, on the campus of Granite State College. It will kick off at 8:45am on Thursday, June 21st, and wrap up around noon on Friday, June 22nd. The conference is free; we’ll be providing continental breakfasts, a lunch, and an evening reception to all registrants. We encourage all attendees to consider extending your stay for a family-friendly long weekend getaway. The Mount Washington Valley area (where the conference site is located) boasts a wide variety of outdoor activities many of which will be made available to attendees at special discounted rates.

In addition to sessions presented by both customers and Rapid Insight staff, there will be “BYOD” (Bring Your Own Data) hands-on lab time where staff members will be available to help you build predictive models using your own data, as well as office hours after the conference where staff will be on-hand to answer questions and consult on projects for the remainder of the afternoon.

Although this is a free event, we do ask that you register in advance here. As the conference gets closer, be sure to check our User Conference page with more information about specific sessions and activities. Looking forward to seeing you all!

Creating Variables: Days Between Application Date and Term Start

Hello everybody! This post will be a continuation of the Creating Variables series. Today we’ll be discussing how and why to create a “days between application date and term start” variable.

At first glance, this variable seems a little long-winded, but I can assure you, it’s worth its weight in characters. As you all know, for any institution that accepts applications on a non-rolling basis, there exists a window of time during which applications must be filed to be considered for acceptance. The amount of time between when an application is submitted and when the relevant admission term begins can be an indication of a student’s interest in a particular institution. For example, a student may turn in an application to his first-choice college during the first week that applications are accepted, but this same student might wait until the day or week before the deadline to turn in applications to his safety or back-up schools. In this way, the amount of time between the day that a student turns in an application and the term start date can be seen as an indicator of that student’s interest. Let’s go ahead and calculate this:

The first step is to hook applicant data into a transform node:

Next, after opening the transform node, we’ll need to select the “Days Between” formula from the drop-down menu:

In the “Enter a Formula” window, we’ll want to enter:

…Where ‘[A]’ corresponds to the variable in your dataset that represents the date each application was submitted, ‘09/01/2012’ represents the start date for the term you’re admitting for, and “date” is actually the date function from the formula drop-down menu:

Before naming and saving this new variable, be sure to switch the “Result Type” to “integer”:

And, voila! Now you have a “days between application date and term start variable” to add to your predictive variable arsenal.

-Caitlin Garrett, Statistical Analyst at Rapid Insight

On Target: Predicting Pregnancy

Call me biased, but I think creative uses for predictive analytics are pretty cool.  Target’s “pregnancy-prediction model”, explained Thursday in a The New York Times Magazine article, is a great example.  It should inspire all of us to take a fresh look at our data and consider what more we can accomplish with a powerful predictive analysis tool (like RI Analytics) and a little bit of creative thinking.

Target’s journey to predicting pregnancy started with an idea conceived by its marketing department. The department had previously conducted surveys which indicated that once a consumer’s shopping habits are ingrained, it can be hard to change them – except during certain brief periods of a person’s life, like after a marriage or the birth of a child, where shopping patterns and brand loyalties often change.  The birth of a child represents a new grocery and household goods list for new parents, as well as the opportunity for Target to sell things like cribs, rugs, furniture, car seats, and other items that a person or couple would not usually buy. Because birth records are public information it was already common practice for companies to send promotional items to new parents; so, to stay one step ahead of competitors, marketers at Target wanted to see if there was a way to predict pregnancy during the second trimester.

Target reviewed the shopping habits of women who had a baby-shower registry as they approached their due dates. Eventually they were able to identify about 25 different products that were indicators of pregnancy, including items like unscented lotion, vitamin supplements, hand sanitizers and washcloths. By treating the purchase of each item as a variable, they were able to create a model that assigned each shopper a pregnancy prediction score based on their purchases. This score was then used to send out relevant coupons and advertisements tailored to each woman at a specific point in her pregnancy – before other retailers even knew she was pregnant. Needless to say, sales in Target’s Mom and Baby department skyrocketed.

This is one way that a creative use of data, combined with some predictive analytics, yields some pretty cool results. Target had the data they needed all along –they just needed the right person to ask the right question. 

-Caitlin Garrett, Statistical Analyst at Rapid Insight