Data Services

Data Services

Fire Engine RED’s world-class data services are not only available to our Student Search clients, they’re also available to schools à la carte. No other data services provider is getting the results that we’re achieving for schools, and we’d love to do the same for you.

Our Offerings

Select any offering below for a description of the benefits, what sets our service apart, and to view case studies.

Student Search Modeling

Benefits of Student Search modeling.

Helps you achieve multiple strategic goals.

The best way for you to achieve multiple strategic goals at once is through the use of Student Search modeling. For example, your goals could include:

  • Increasing enrollment
  • Increasing tuition revenue
  • Boosting your academic profile
  • Entering new markets more efficiently

Saves you money.

In addition, Predictive Modeling can save your school money, by helping you:

  • Purchase only the names of students who are most likely to apply and enroll
  • Segment student populations more effectively
  • Determine which students should receive additional communications, such as print and digital advertising

What sets our Student Search models apart?

Our predictive models are getting great results for our Search clients. Here’s how:

We focus on applications and enrollment. We start with the end in mind: enrollment. That means we identify students who are most likely to apply and enroll, not just inquire.

We take a custom approach. What’s predictive for other institutions may not be predictive for yours.

We build multiple models for each of your goals. Similarly, what’s predictive for one goal (increase enrollment) may not be for others (boost revenue and enter new markets).

We tap into unique data sources. In addition to your historical data, commercially available data, and our own proprietary data, we’re able to tap into Segment Analysis Service™, which:

  • Is a geodemographic service provided by the College Board … Segment Analysis Service data can be applied to ALL of your sources, including ACT, CBSS, non-responders, NRCCUA, and your inquiry pool
  • Enables us to tag your data with 150+ unique educationally relevant academic, social and economic factors that predict where students choose to go to college
  • Helps us make our models more predictive and informative

    Student Search Modeling Case Studies.

    Our Student Search Modeling gets results, and here’s the proof.

    Strategy: We developed a predictive model to trim names from weaker geographic areas, and redeployed resources to purchase names in specific new growth areas (AZ, NM, TX).

    Result: We increased inquiries in these states between 29% and 55% (630 students); inquiries from eliminated areas fell by only 1% (22 students). So, for the same budget, we generated 608 more inquiries

    Strategy: We developed a predictive model to strategically expand into a market on the opposite coast by purchasing names of students most likely to apply and enroll. We then customized our copy/creative to appeal to students in this market.

    Result: We saw dramatic increases in the yield rate AND academic quality. Yield for admitted students went up by 300%, and the average SAT score reached 1275.

    Strategy: We combined our proprietary geodemographic modeling data with student-provided data to predict students’ willingness to travel. We then targeted those students as part of a Junior & Sophomore campaign.

    Result: We increased response rates (web form submissions and completed business reply forms) between 15% and 33%.

    Strategy: We developed a predictive model to evaluate in-state markets, and purchased only the names of students identified as likely to apply and enroll, rather than purchasing the names of all students in the entire state. We removed 743 ZIP Codes from the Search geography used by the client’s previous vendor.

    Result: We met or exceeded our client’s goals – every single month – for applications, completed applications, admits, and deposits. Year-end goals for completed applications and admits were met in March.

    Strategy: We developed a predictive model to identify and purchase only the names of students likely to apply and enroll.

    Result #1: We purchased 29.8% fewer names year over year, resulting in 24% fewer inquiries, and saving the client a total of $155,478.

    Result #2: The client received 10% more applications, admitted 16% more students, and enrolled 11% more students, resulting in a return on investment of 52 times their first-year Search investment in net revenue (including discount rate).

    Recorded Webinar.

    View our informative, 40-minute recorded webinar, “How to Boost Your Search ROI Using Predictive Modeling & Segment Analysis Service.”

    Strategy: We sent print and email to 50% of randomly selected students. The other 50% received email only.

    Result #1: The response rate for students who received print and email was 18% higher than for students who received email only. Important: We define a “response” as a web form submission or completed business reply form.

    Result #2: Print also drove improved response rates for key recruitment cohorts, including a 25% increase for underrepresented students and a 21% increase for male students.

    Digital Modeling

    Digital Modeling Case Study.

    Strategy: We used test scores and drive time from campus to create a model, and targeted high-achieving students with specific digital ads inviting them to attend campus visit days.

    Result #1: Students who received targeted digital messages were nearly three times as likely to visit campus, and twice as likely to apply and be accepted.

    Result #2: Targeted students yielded at rates similar to other admits.

    Result #3: A fairly modest investment in digital helped drive 22 more students to enroll.

    Enhanced Segmentation Modeling

    Enhanced Segmentation Case Studies.

    Case Study #1 – Junior & Sophomore Search

    Strategy: We tagged student records with location information (rural, suburban, or urban) and predicted family incomes. We then sent students in rural areas targeted messaging about financial aid.

    Result: Our client saw a 13% increase in inquiries from rural areas – the first increase in seven years.

    Case Study #2 – Junior, Sophomore & Freshman Search

    Strategy: We leveraged a combination of list-source data, including academic interest, gender, and race/ethnic identifiers, along with indicators such as predicted family income and propensity to travel out of state for college. Then, we sent print to students who were most desirable but least likely to respond to an electronic-only campaign.

    Result: Despite reducing print mailings by one-third, response rates for the most-desirable students fell by only a fraction of one percent.

    *Important: We define a “response” as a web form submission or completed business reply form.

    Inquiry Scoring

    Inquiry Scoring Case Study.

    Strategy: We used inquiry scoring to identify low-yielding cohorts in an effort to save money (e.g., not send them expensive print mailings).

    Result: After identifying the bottom half of the inquiry pool (24,415 names), and adjusting for campus visits, the client chose not to send print pieces to 22,750 of those students. This resulted in an estimated savings of $273,000. Ultimately, only five (5) enrolled students came from the bottom half of the inquiry pool.

    Recorded Webinar.

    Watch our valuable, 30-minute recorded webinar, “How to Use Inquiry Scoring to Help You Make Your Class and Save Money.”

    Alumni Matching

    Alumni Matching Case Study.

    Strategy: We used alumni data to find previously unidentified legacy matches on the client’s purchased Search lists and in their inquiry pool. Then, we sent those students specifically targeted messages.

    Result #1: We discovered over 1,000 new potential legacy students for our client.

    Result #2: This newly identified legacy cohort applied at a rate of 36% (367/1,023), a 75% increase over the school’s overall 20% inquiry-to-applicant rate.

    Sibling Matching

    Sibling Matching Case Study.

    Strategy: We used our client’s historical applicant records to identify likely younger siblings of currently enrolled or previous applicants.

    Result: We matched over 3,000 new potential sibling-related students for the classes of 2019 – 2022. Of the 1,409 matches in the class of 2019, 258 applied, and over 77 enrolled.

    Phone Number Identification

    Phone Number Identification Case Study.

    Strategy: Fire Engine RED ran the list of 671 students through our phone number matching service, resulting in 450 matched phone numbers. The university provided the numbers to their group of tele-counselors, who reached out to those individual students.

    Result: Of the 450 students who were called, 12 applied, 10 were admitted, and three deposited. We tagged prospect records with phone numbers – within a few business days and for a cost of less than $200 – which contributed to the enrollment of three additional students, resulting in hundreds of thousands in net revenue.

    Travel Modeling

    Travel Modeling Case Study.

    Strategy: Assess effectiveness of historical travel and identify more productive travel opportunities.

    Result: Based on our recommendation, this client trimmed their out-of-state travel by nearly 7% (in terms of counselor days on the road), entered two new markets, and saw a slight increase in travel-generated inquiries.

    Financial Aid Optimization

    Fire Engine RED has teamed up with Maguire Associates to provide our clients with Financial Aid Optimization.

    See our Financial Aid Optimization page on how our partnership with Maguire Associates can benefit you and your school.