scoring 101: leveraging insights to improve...

1EDUCATION888.856.0534 www.velocify.com/edu © 2012 Velocify, Inc.

SCORING 101: LEVERAGING INSIGHTS TO IMPROVE ENROLLMENT RATESExecutive SummarySchools that compete for students know that not all student inquiries are created equal. Inquiry quality is deter-mined by a large number of factors that can vary widely. Understanding what those factors are can give schools a significant advantage over their competition. This study looks at historical data, across millions of inquiries, to draw enlightening insights about key attributes that impact inquiry quality. Each of the results included in this research, on its own, can suggest a number of actionable changes to the types of inquiries that should be priori-tized, the type of data that should be collected, and the best ways to handle different types of inquiries. However, the potential value of this data can be maximized when the combined effect of these attributes is used to acquire inquiries, produce or enhance inquiry scores, and determine optimal prioritization, distribution, and nurturing strat-egies within an enrollment management software solution.

Scoring Process

Distribute and Prioritize Inquiries

According to Scores

Dynamically Score Each Inquiry

(as it comes in andas actions are taken)

Analyze Performance Data Determine/Revise

Scoring Criteria• Survey Staff

Assign Pointsto Each

Characteristic

1

24

3

5

• Discover Quality Attributes• Evaluate Scoring Model

BackgroundInquiry scoring is quickly becoming an im-portant aspect of enrollment management. Many of the leading private sector schools have already begun to experience the in-creased enrollment rates that can result from the effective use of scoring. There are many software applications and services out there that can help you score your prospective stu-dents, but there are also things you can do on your own to get started with the valuable pro-cess of identifying ‘best fit’ students for your school and prioritizing your outreach to them.

Scoring is simply the process of assigning a relative value to your inquiries according to the perceived and/or previously observed probability of them enrolling. The first step to scoring is to identify which attributes or char-


acteristics make an inquiry more or less likely to become a student for your specific school. In order to do this, it is crucial to truly know your students. One way determining what attributes to use is to simply guess based on your knowledge and experience with your students. Most admissions departments have a strong under-standing of the common attributes their successful students share based on the cumulative knowledge of admissions staff and those that regularly interact with students. Your guesses and your staff’s perceptions may in fact be correct, but even if wrong, they’re a starting point. Similarly, data that provides insight on inquiry per-formance across the private sector education industry, such as the data provided in this report, can supply an alternative or comple-mentary foundation.

Ideally, you also have access to historical enrollment data that can be analyzed to help you validate your guesses. Student data may also help you uncover attributes you may not have thought about. Analyzing historical data on successful and unsuccessful enroll-ments won’t only help you verify that you’re looking at the right cri-teria, but it will also help you identify the actual effect of each char-acteristic on the probability of enrolling. For example, you might realize that age is an important factor in determining the likelihood of a prospect enrolling, but you may not truly understand how im-portant of a factor age might be and whether older is always better than younger or vice versa. Looking closely at your data should help you answer those questions.

Once you have a good grasp of the criteria you’d like to use for your scoring model and the impact they have, you can begin as-signing points to each of them to arrive at a final score for each inquiry. Each inquiry’s score can be just a raw number, such as the

Study Methodology

To aid in your inquiry scoring ef-forts, this report analyzes inquiry data across a large cross section of Velocify customers in the education industry. Both intrinsic and extrinsic characteristics were selected based on common frequency of use across multiple clients. Each characteristic was then evaluated to measure its impact on enrollment rate. In total, millions of inquiries were examined in this study.

We identified common data fields, actions, and statuses that were captured by a significant number of clients and determined how they each contributed to the likelihood of a prospect enrolling. Most findings reported in this study are based on performance from thousands to hun-dreds of thousands of inquiries that share that same data attribute. In or-der to be included in this report, indi-vidual data entries had to be shared by at least 100 inquiries.

sum of all points assigned, or it can be a letter grade or any kind of quality scale you might want to use. An impor-tant distinction you may want to make is between extrinsic and intrinsic criteria because each of those cumulative scores can influence the way you choose to take actions on each of those inquiries.

Extrinsic criteria include facts about your prospective student, which are typically provided by the prospect or ac-quired through data appending services. These facts will likely tell you if a prospect possesses the characteristics that typically make an inquiry more likely to apply to your school. Intrinsic characteristics are better indicators for the seriousness of your prospects and their readiness for enrolling because intrinsic data is based on observed be-havior or inferred from data that may not be directly provided by your prospects. The combination of both intrinsic


and extrinsic data will result in the most reliable scores. Some schools keep these scores separate, using intrinsic factors to determine urgency and extrinsic factors to determine level of persistence.

The scores you calculate and assign can be used to prioritize your inquiries, to distribute them to the right admis-sions advisor, or to determine the necessary type and modality of follow up. Enrollment management software like Velocify helps you implement the prioritization, distribution, and nurturing strategies you want based on business rules you can customize using scores as well as hundreds of other criteria.


EducationThe external attributes analyzed for the education industry included:

MIL

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MIS

SOUR

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NEW

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.

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MEX

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ARIZ

ONA

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-100%

0%

25%

50%

75%

-75%

-50%

-25%

100% 476%

Perf

orm

ance

rela

tive

to a

vera

ge e

nrol

lmen

t rat

e

54% 50%45% 45% 44% 43%

37%

-24% -27%-33% -36% -36% -39% -40%

-46%

Fig. 1 - Education: Home State

Bottom 8

Top 8

Home State

One of the extrinsic characteristics analyzed was the prospect’s home state. It is interesting and valuable to note which geographic regions seemed to have the more serious student candidates. Figure 1 shows both the eight top and eight bottom performing states and regions in terms of enrollment rates. Interestingly enough, the two regions that showed the highest probability of enrollment are outside of the U.S. Prospects from Canada had enrollment rates that were almost four times higher than the average prospect, and prospects from AE (the abbreviation for the Armed Forces in Europe, Middle East, Africa, and Canada) had enrollment rates that were almost six times greater than average. Results from Canadian inquiries have been left out of Figure 1 because a small number of Canadian schools accounted for the vast majority of Canadian prospects. Reporting on them alongside mostly American schools did not seem practical, especially because that information is not actionable for American or Canadian schools.

EnrollmentIntent

MaritalStatus

EducationLevelHome State Email


Email Address

The geographic home regions above suggest that military prospects perform very well in terms of enrollment. It is therefore no surprise that email addresses with an MIL domain name also enroll at the highest rates when com-pared to prospects with email addresses using other domain names. Figure 2 shows the eight top performing domain names as well as the bottom eight. The top eight domains include prospects who might be slightly more tech savvy, such as Mac users with me and mac email domains. In contrast, the bottom eight include prospects with email domains that are losing popularity, such as Netscape and NetZero, a possible indication of prospects’ lower levels of familiarity or comfort with new technology.

MIL

MAC M

E

YAHO

O IN

TL.

GMAI

L

OPTO

NLIN

E

HOTM

AIL

ROCK

ETM

AIL

CABL

EONE

COX

VA.G

OV

JUNO

MAI

L

PEOP

LEPC

NETZ

ERO

NETS

CAPE

-100%

0%

25%

50%

75%

-75%

-50%

-25%

100% 157%

Perf

orm

ance

rela

tive

to a

vera

ge e

nrol

lmen

t rat

e

57%

34%

16% 15% 12% 10% 10%

-38% -40% -40%-47%

-56% -56% -57%-65%

Fig. 2 - Education: Email Address Enrollment Rate

Bottom 8

Top 8


Figure 3 shows the to-tal distribution of email domain names. In ed-ucation, almost 90% of all inquiries provide email addresses, much higher than in the other industries. Those that don’t provide an email address have an enroll-ment rate that is almost 16% lower than the av-erage.

Fig. 3 - Education: Email Address Volume

Live2%

MIL 1%

Comcast 1% MSN

1%

Yahoo

40 %

Gmail17 %

All Other Domains

12%

Blank11%

Hotmail 10 %

AOL 5%

Education Level

The completed education level reported by education prospects also proved relevant in regards to their potential for enrollment. Figure 4 shows that prospects at the extreme points of education levels, those with less than a high school diploma or with doctorate or professional degrees, are considerably less likely to enroll than most prospects that have a level of education that falls somewhere in between. The size of each bubble is proportional

-100%

0%

40%

Fig. 4 - Education: Education Level

20%

-20%

-40%

-60%

-80%Perf

orm

ance

rela

tive

to a

vera

ge e

nrol

lmen

t rat

e

High School

2%

Less than High School

-59%

ProfessionalDegree

-59%

SomeCollege

4%

Associate’sDegree

-30%

Bachelor’sDegree

14%

Master’sDegree

-8%

to the number of prospects that fell within each category. Most programs offered at for-profit schools don’t really target those who already hold professional degrees, and those who don’t have a high school diploma may not be quite ready to handle the academic demands of many of these programs. It is not surpris-ing that inquiries at those oppo-site extremes of education levels have the lowest enrollment rates and also account for the smallest percentages of total inquiries.


Enrollment Intent

We analyzed over 3,000 prospects that provided enrollment intent information and found that inquiries that pro-vided this information were 366% more likely to enroll than those that did not provide enrollment intent informa-tion. This does not necessarily suggest that collecting enrollment intent information will automatically boost your enrollment rates, but the data does indicate that prospects that provide that information and/or schools that collect that information have considerably higher enrollment rates. The reason for the significant increase in enrollment rate may be that this piece of information is generally collected when a prospect has gotten further along in the application or qualification process. Nonetheless, it may be valuable for schools to collect this information earlier in the process because as Figure 5 illustrates, prospects that intend to enroll part-time are considerably more likely to enroll than those interested in enrolling full-time. The size of the bubbles is proportional to the number of prospects that fell into each category.

-20%

0%

50%

Fig. 5 - Education: Enrollment Intent

20%

30%

40%

10%

-10%Perf

orm

ance

rela

tive

to a

vera

ge e

nrol

lmen

t rat

e

Full-Time

-5%

Part-Time

49%


Marital Status

As was the case with ethnicity, the nearly 3,000 education prospects whose records contained marital status infor-mation tended to enroll at significantly higher rates. They actually enrolled at more than triple the rate of prospects that did not include that information in their inquiries. However, in this case, there was a more significant difference in enrollment rates between the three groups. Divorced prospects tended to enroll at almost half the rate of single or married prospects. In Figure 6, the size of the bubbles is proportional to the number of prospects that fell within each category. Married prospects make up the largest group of prospects that provide marital status and they enroll at a higher rate, perhaps because they are more likely to have the stability and support in their personal lives that allow them to begin a new study program.

-60%

0%

30%

Fig. 6 - Education: Marital Status

10%

20%

-10%

-20%

-30%

-40%

-50%Perf

orm

ance

rela

tive

to a

vera

ge e

nrol

lmen

t rat

e

Single

-5%

Divorced

-47%

Married

11%

Actions Taken (Intrinsic Criteria)

Velocify clients in education seem to have more uniformity around intrinsic values, which reflect actions taken with or by a prospect, than do clients in insurance and mortgage. Figure 7 highlights ten such intrinsic values that have both positive and negative effects on the likelihood of a particular prospect’s enrollment.

One of the somewhat surprising results of this analysis was that the one action that resulted in the greatest im-provement in enrollment rate was “contact follow up. According to our analysis, prospects that have a “contact follow up” action assigned to them are 470% more likely to enroll than prospects that never have that action as-


signed. It might not be surprising that those prospects are more likely to enroll because they have shown some interest by asking someone to follow up with them, but perhaps what is surprising is that they actually enroll at a higher rate than even prospects who are interviewed. (This latter group still enjoys a 290% boost in enrollment over the average education prospect.) The ease with which Velocify’s clients can set follow up reminders and track appointments within the enrollment management software probably contributes to the success of enrolling pros-pects with follow up actions at such a high rate. Figure 7 also shows that prospects are more likely to enroll when schools receive inbound emails from them, when admissions advisors make a comment in a prospect’s record, and when a prospect enters our system either manually or through an imported Excel or CSV file, as opposed to through an automatic import from a provider or Web inquiry form.

Fig. 7 - Education: Impact of Intrinsic Criteria on Enrollment

100% 150% 250%-100% -50% 50% 200%-150% 0% 300%

Performance relative to average enrollment rate

471%

263%

168%

158%

287%

CONTACT FOLLOW UP

INTERVIEWED

INBOUND EMAIL

COMMENT

MANUALLY ENTERED LEADS

-28%

-50%

-66%

-68%

-69%

CALL: LEFT MESSAGE

NURTURE

CALL NO ANSWER

SMS OPT-OUT

CALL BUSY

On the negative side, we see that when Velocify’s clients call inquiries and they get a busy tone or no answer, prospects become almost 70% less likely to enroll. The same goes for prospects that choose to opt-out of text messaging. Although prospects that have been left a voicemail (Call: left message) or have been put into a nurtur-ing status also experience a lower enrollment rate than the average prospect, we see that the decrease isn’t quite as drastic, perhaps highlighting the value of leaving a message when calling a prospect and the value of having a well thought out nurturing plan for prospects that may not enroll as quickly as others.

Just as companies in different industries collect different sets of data on their prospects, companies within the same industry can also collect different information on their prospects. Even within the same company, not all of the same information is collected for every prospect, especially when certain actions or attributes don’t apply. Therefore, the data presented above includes different sample sizes depending on the number of prospects that had this information across all education clients included in the study.


When a prospect originally enters a lead management system, its lead score is usually exclusively determined by its extrinsic characteristics. After an inquiry’s status changes or after actions are taken with or by that prospect, the prospect’s potential for enrollment can either increase or decrease (as illustrated in Figure 7). Therefore, the prospect’s score should be adjusted after the status or action changes their disposition. Intrinsic characteristics not only illustrate the importance of re-scoring based on actions and statuses taken on inquiries, but perhaps more importantly, they also remind us that we need similar dispositions for our prospects. For example, if you don’t currently use or track contact follow-ups maybe you need to make that action available to your staff or you need to remind them to use it more frequently, especially since it currently results in almost six times the enroll-ment rate for prospects that do have it assigned.

Application for Education

Velocify’s enrollment management software allows institutions to easily prioritize, distribute, and take actions on their inquiries based on every one of the characteristics included in this study. System managers can set up a number of business rules that consider these and any other tracked characteristics about an inquiry. Based on the results of this research, institutions should consider collecting some of this information about their prospects if they’re not currently doing so. Using these results to make the most out of each inquiry is not only useful for those doing lead scoring. Benefits can definitely be gained by applying this newfound industry-wide knowledge using the basic, yet sophisticated tools already built into Velocify’s enrollment management software.

Nevertheless, for those interested in fully pursuing scoring, we highly recommend working with a reputable com-pany that provides scoring services. Velocify’s enrollment management software is both highly configurable and ready for real-time integration with just about any scoring vendor or with proprietary scoring engines. To begin exploring scoring on your own, the following should serve as an example of how select data presented in this report might be used by a school to begin scoring prospects:

Fig. 8 - Education: Extrinsic Scoring

EnrollmentIntent

MaritalStatus

EducationLevel

MIL +2

Mac or Me +1

Mail, Peoplepc, Netzero,

or Netscape -1

Military Overseas +2

CO or RI +1

AZ, ND, OK, SD,or ID -1

Professional Degree or Less

Than High School -1

Part-Time +2

Full-Time+1

Married +2

Any MaritalStatus

InformationEntered +1

Home State Email


Most intrinsic critera would be added once an action has been taken on a prospect. The prospects initial score might change de-pending on the actions taken. For example, the prospect previously described would receive a new score of +5 if a follow up ac-tion (+2) were to be added to that prospect’s record.

For instance, using the criteria shown in Figure 8, a new education prospect entering the sys-tem with the characteristics shown in Figure 9 would be scored a +3. That inquiry should be placed ahead of prospects with lower scores and might be distributed to an advisor who is better at enrolling high quality prospects.

Fig. 9 - Education: Sample Score

From Arizona

-1From Arizona

-1

MIL Email

+2MIL Email

+2

High School Grad

0High School Grad

0

Married

+2Married

+2

EnrollmentIntent

0

EnrollmentIntent

0

Total Prospect Score

+3Total Prospect Score

+3

Fig. 10 - Education: Intrinsic Scoring

Contact Follow-Up +2

Interviewed +1

Inbound Email +1

Positive Attributes+

Call Busy -1

SMS Opt-Out -1

Call No Answer -1

Negative Attributes-


ConclusionUnderstanding your prospective students and the attributes that make them more or less likely to enroll is critical to running a successful admissions department. The data provided in this report should provide some insight to begin adjusting your enrollment management practices to maximize enrollment yield. It is important to note that most of this information is not easily actionable without the right enrollment management tools in place. In order to truly benefit from this type of information, organizations should have an enrollment management system that effectively includes:

• Automated inquiry importing• Optimal contact velocity• Automatic inquiry assignment based on intelligent, highly customizable, and

easy-to-configure prioritization and distribution rules• Built-in workflows that reflect best practices, but are easy enough to customize• Real-time admissions performance insight• Comprehensive reporting and analytics (that help assess inquiry quality)• Automated email and text messaging nurturing capabilities

• Customer-defined data fields that can accept and store score data• Automatic, real-time integration with scoring engines in order to immediately

take appropriate actions on incoming inquiries or prospects according to scores• Dynamic scoring capabilities, as new data is added to a prospect’s record• Distribution, prioritization and communications based on a score

The combined effect of all of the insight gained from this analysis -- and what can be added from the analysis of each organization’s own data -- is best captured through an inquiry score, as described in this report. Working with a reputable scoring partner or developing your own scoring engine in conjunction with a top-of-the-line enrollment management solution is the best way to capitalize on all of the benefits scoring offers. To implement a full scoring process, an enrollment management system should also include:

Happy scoring!

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scoring 101: leveraging insights to improve...

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