lead scoring 101: leveraging lead quality insights to...

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RESEARCHRESEARCH

LEAd SCoRing 101: LEvERAging LEAd QuALity inSigHtS to impRovE ConvERSion And EnRoLLmEntExecutive SummarySavvy sales and marketing professionals know that all prospects are not created equal. Lead quality varies widely based on a large number of factors. Understanding what those factors are can give organizations that work with leads and prospects a significant advantage over their competition. This study looks at historical data, across millions of leads, to draw enlightening insights about key attributes that impact lead quality. Each of the results included in this research, on its own, can suggest a number of actionable changes to the types of leads that should be pursued, the type of data that should be collected, and the best ways to manage different leads. However, the potential value of this data can be maximized when the combined effect of these attributes is used to acquire leads, produce or enhance lead scores, and determine optimal prioritization, distribution, and nurturing strategies within a lead management solution.

Lead Scoring Process

Distribute and Prioritize Leads

According to Scores

Dynamically Score Each Lead

(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

BackgroundLead scoring is quickly becoming an impor-tant aspect of lead management. Many of the leading organizations that generate and/or purchase large lead volumes have already begun to experience the increased conver-sion rates that can result from the effective use of lead scoring. There are many software applications and services out there that can help you score your leads, but there are also things you can do on your own to get started on this valuable process.

Lead scoring is simply the process of assign-ing scores to your leads according to the per-ceived and/or previously observed probabil-ity of them becoming customers. The first step to lead scoring is identifying which attri-butes or characteristics make a lead more or

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less likely to become a customer for your specific organization. In order to do this, it is crucial to truly know your customers. One way to determine what attributes to use in scoring is to simply guess based on your knowledge and experience with your customers. Speaking to your sales staff and to those that regularly interact with your customers might be a good place to start. Your guesses and your staff’s perceptions may in fact be correct, but even if wrong, they’re at least a starting point. Similarly, data providing insight on leads’ performance across your industry, such as that provided in this report, can provide an alternative or complemen-tary foundation.

Ideally, you also have access to historical customer data that can be analyzed to help you validate your guesses. Customer data may also help you uncover attributes you may not have thought about. Analyzing historical data on successful and unsuccessful leads 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 closing customers. For example, you might realize that age is an important factor in determining the quality of a prospect, 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 lead. Each lead’s score can be just a raw number, such as the sum of all points assigned, or it can be a letter grade or any kind of qual-ity scale you might want to use. An important 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 leads.

Study methodology

To aid in your lead scoring efforts, this report analyzes lead data across a large cross section of Velocify cus-tomers in the education, insurance, and mortgage industries. Both in-trinsic and extrinsic characteristics were selected based on common frequency of use across multiple cli-ents. Each characteristic was then evaluated to measure its impact on conversion rate. In total, nearly 4 million leads were evaluated in this study.

For each industry, we identified com-mon data fields, actions, and status-es that were captured by a significant number of clients and determined how they each contributed to a pros-pect’s likelihood of conversion. Most individual data points reported in this study are based on performance from thousands to hundreds of thou-sands of leads that share that same data attribute. In order to be includ-ed in this report, individual data en-tries had to be shared by a minimum of 100 leads.

Extrinsic criteria include facts about your prospect, which are typically provided by the prospect or acquired through data appending services. These facts will likely tell you if a prospect possesses the characteristics that typically make leads more likely to become customers for you. On the other hand, intrinsic characteristics are better indicators for the seriousness of your prospects and their readiness for your product or service because intrinsic data is based on observed behavior or inferred from data that may not be directly provided by your pros-pects. The combination of both intrinsic and extrinsic data will result in the most reliable lead scores. A reason


to keep these scores separate might be to determine urgency based on the intrinsic factors and desired level of persistence based on extrinsic factors.

The lead scores you calculate and assign can be used to prioritize your leads, distribute them to the right groups or individuals, or to determine the type of follow up needed according to each lead’s score. Lead management software such as Velocify Lead Manager, helps you implement the prioritization, distribution, or nurturing strate-gies you want based on business rules you can customize using lead scores as well as hundreds of other criteria.

ResultsAs would be expected, individual organizations generally collect different types of information about their leads, and in different industries the disparity is even greater. Nevertheless, there are certain pieces of information that are common across the three industries analyzed for this study. For example, the industries examined in this report all collected sufficient data to assess the effect a lead’s home state and email address have on the lead’s prob-ability of conversion. We also found a set of other intrinsic and extrinsic characteristics that had a noteworthy impact on conversion: some of these characteristics are shared across industries and others are industry-specific.


EducationThe external attributes analyzed for the education industry included the following:

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Fig. 1 - Education: Home State

Bottom 8

Top 8

Home State

One of the extrinsic characteristics analyzed was the prospect’s home state. It may be 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 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 leads 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.

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57%

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-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 edu-cation, almost 90% of all leads provide email addresses, much high-er than in the other in-dustries. 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

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Fig. 4 - Education: Education Level

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High School

2%

Less than High School

-59%

ProfessionalDegree

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SomeCollege

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Associate’sDegree

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Bachelor’sDegree

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Master’sDegree

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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.

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Fig. 5 - Education: Enrollment Intent

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Full-Time

-5%

Part-Time

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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.

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Fig. 6 - Education: Marital Status

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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, who still enjoy 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 Lead Manager software probably contributes to the success of enrolling prospects with follow-up ac-tions at such a high rate. Figure 7 also shows that prospects are more likely to enroll when schools receive in-bound 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 Lead360’s clients call prospects 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 a prospect’s status changes or after actions are taken with or by that pros-pect, 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 the prospect’s 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 if we don’t have similar dispositions for our prospects, maybe we should consider them. 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 enrollment rate for prospects that do have it assigned.

Application for Education

Velocify Lead Manager software allows institutions to easily prioritize, distribute, and take actions on their inqui-ries based on each and 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 Lead Manager.

Nevertheless, for those interested in fully pursuing lead scoring, we highly recommend working with a reputable company that provides lead scoring services. Velocify’s Lead Manager is both highly configurable and ready for real-time integration with just about any lead scoring vendor or with proprietary lead scoring engines. To begin exploring lead 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 closing 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-


insuranceThe external attributes analyzed for the insurance industry included the following:

CreditRating

Prior orCurrent

InsuranceCarrier

EducationLevelHome State Email

Home State

When it comes to geographic distribution of insurance leads, Figure 1 highlights the top and bottom eight states in terms of conversion rate in the insurance industry.

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Fig. 1: Insurance: Home State

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Top 8


Email Address

Figure 2 presents the top and bottom eight email domain names used by insurance leads according to their performance above or below the average conversion rate. Although Comporium had a relatively small num-ber of leads that used its domain, its 100+ leads were by far the most likely to convert. On the other end of the spectrum, leads using Hushmail and Petlovers emails, were the least likely to convert. Furthermore, even though they each had over 100 leads using their domain, not a single one of those leads had converted to date.

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Fig. 2 - Insurance : Email Address Conversion Rate

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Figure 3 shows the volume dis-tribution of the domain names most commonly used by insur-ance leads. Insurance leads are almost three times more likely not to provide email addresses than are education prospects (almost 90% of education pros-pects provide an email address). Those that don’t provide an email address in the insurance industry are 14% less likely to convert than those that do have an email address on file.

Fig. 3 - Insurance - Email Address Volume

Yahoo

24%

Blank32%

Gmail10%

AOL 7%

Hotmail 7%

All OtherDomains

11%

SBC1%

Verizon1%

Live1% Roadrunner

1%

ATT1%

MSN1%

Comcast3%

Education Level

The education industry is not the only industry that cares about its prospects’ education level. In fact, more than a quarter million insurance lead records contained education information, an even higher number and percentage than in education. Figure 4 illustrates why education can be very important in scoring an insurance lead. Accord-ing to our analysis, all leads with bachelor’s degrees or higher convert at below average rates, while all leads with

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Fig. 4 - Insurance: Education Level

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High School

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Associate’sDegree

Bachelor’sDegree

Master’sDegree

Lead Volume by Education Level

17%

22%

16%10%

28%

4%2%

an education level equal to or lower than an as-sociate’s degree convert at above average rates. This suggests that those that are highly educated might be doing more com-parison shopping when it comes to insurance or they might just be less willing to change insurance carri-ers. Figure 4 also includes a pie chart that shows the lead volume distribution by education level for those leads that did provide edu-cation information.


Credit Rating

Arguably, one of the most interesting criteria that can be used for lead scoring in insurance is a lead’s credit rating. Figure 5 shows that leads that indicate that they are unsure about their credit rating are the least likely to convert. When leads are hesitant about providing personal information or when they say they don’t know something, they are often less ready to make purchases, enroll, or convert. Interestingly enough, the figure also shows there is a highly significant negative correlation between credit rating and probability of conversion. The better a lead’s reported credit rating, the less likely they are to convert. It’s possible that those with high credit ratings are less price sensitive or somehow less willing or incentivized to switch insurance carriers, and that those with poor credit ratings possibly have more reason to benefit from switching carriers. The size of the bubbles in the figure is pro-portional to the number of leads that fell within that credit rating.

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Fig. 5 - Insurance - Credit Rating

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-38%Unsure

Excellent

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5%Poor

15%

79%Very Poor


Figure 7 shows the dis-tribution of current or prior insurance carri-ers by total volume for the leads that provided that information.

Current or prior insurance Carrier

Another extrinsic factor that seems to have an impact on an insurance lead’s probability of closing is the lead’s current or prior insurance carrier. Figure 6 highlights the eight insurance carriers whose leads are most likely to convert and the eight insurance carriers whose leads are least likely to convert.

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Fig. 6 - Insurance: Prior or Current Insurance Conversion Rate

Bottom 8

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Fig. 7 - Insurance: Prior or Current Insurance Volume

Other InsuranceCarriers 45%

GEICO10%

Travelers2% USAA

2%

State Farm10%

Unsure7%

Progressive7%

Allstate6%

Nationwide2%

Liberty Mutual3%

Farmers3%

AAA3%



Some of the intrinsic criteria shown in Figure 8 for the insurance industry are very similar to the criteria reported for the education industry, but in some cases the results are actually quite different. Entering a comment for an insur-ance lead seems to have a slightly more positive effect than it does in education. On the other hand, scheduling a call back, which is analogous to education’s Contact Follow Up, although still a positive, seems to make less of a difference in the insurance industry than it does in education.

Fig. 8 - Insurance: Impact of Intrinsic Criteria on Conversion

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

Performance relative to average conversion rate

223%

72%

108%

COMMENT

CONTACT CALL BACK

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REQUOTE


ARCHIVE

In insurance, when a lead is manually entered into Lead Manager or opts out of text messages, the effect is completely the opposite of what it was in education. While manually entered leads had higher enrollment rates in education, in insurance, they actually convert at less than half the average rate. And while those that opt out of texting in education enroll at lower rates than average, in insurance, they convert at about a 70% higher rate. The outcome for insurance leads that opt out of text messaging is counter intuitive, but what it actually reflects is that the relatively low percentage of leads that provide cell numbers in insurance convert at a very high rate. The only leads that can opt out of text messaging are those that have provided cell numbers. Leads that provide cell numbers and opt out of text messaging do convert at a lower rate than those that provide cell numbers and don’t opt out of text messaging, but since leads that provide cell phones convert at such a high rate in insurance, even when they opt out of texting, they still perform considerably better than the average lead. This goes to show that even when the data may not initially make total sense, we don’t always have to understand the true reasons behind the results to gain the potential benefits of measured criteria that result in higher conversion rates. Finally, we also see that leads that are re-quoted or put into an archive status are considerably less likely to convert than those that aren’t.

Just as companies in different industries collect different sets of data on their leads, companies within the same industry can also collect different information on their leads. Even within the same company, not all of the same in-


formation is collected for every lead, especially when certain actions or attributes don’t apply. Therefore, the data presented in Figure 8 includes different sample sizes depending on the number of leads that had this information across all insurance clients included in the study.

When a lead originally enters a lead management system, its lead score is usually exclusively determined by its extrinsic characteristics. After a lead’s status changes or after actions are taken with or by that lead, the lead’s po-tential for conversion can either increase or decrease (as illustrated in Figure 8). Therefore, the lead’s score should be adjusted after the status or action changes the lead’s disposition. Intrinsic characteristics not only illustrate the importance of re-scoring based on actions and statuses taken on leads, but perhaps more importantly, they also remind us that if we don’t have similar dispositions for our leads, maybe we should consider them. For example, if you don’t currently use or track comments maybe you need to make that option available to your staff or you need to remind them to use it more frequently, especially since it currently results in more than three times the conver-sion rate for leads that have comments entered.

Application for insurance

Velocify Lead Manager software allows companies to easily prioritize, distribute, and take actions on their leads based on each and 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 a lead. Based on the results of this research, companies should consider collecting some of this information about their leads if they’re not currently doing so. Using these results to make the most out of each lead 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 Lead Manager.

Fig. 9 - Insurance: Extrinsic Scoring

CreditRating

Prior or Current

InsuranceCarrier

EducationLevel

UT, CT, or NV +1

Comporium +2

Me or PTD +1

Petlovers, Hushmail, or Jabble -1

MD, KS, AK, or OR -1

Associate's Degreeor Less +1

Bachelor's Degree or Higher -1

Poor or Very Poor +1

Unsure or Excellent -1

Dairyland Insurance+1

Independent Agentor American Direct

Business Insurance-1

Home State Email


Nevertheless, for those interested in fully pursuing lead scoring, we highly recommend working with a reputable company that provides lead scoring services. Velocify’s Lead Manager is both highly configurable and ready for

Fig. 10 - Insurance: Sample Score

From Alaska

-1From Alaska

-1

Comporium Email

+2Comporium Email

+2

High SchoolGraduate

+1

High SchoolGraduate

+1

GEICO Insurance

0

GEICO Insurance

0

Poor Credit

+1Poor Credit

+1

Total Lead Score

+3Total Lead Score

+3

Fig. 11 - Insurance: Intrinsic Scoring

Comment +2

Contact Call Back +1

SMS Opt-Out +1


Requote -1

Manually Entered -1

Archive -2


For instance, using the criteria shown in Figure 9, a new insurance lead entering the system with the characteristics shown in Figure 10 would be scored a +3. That lead should be placed ahead of leads with lower scores and might be distrib-uted to an agent or rep that is better at closing high quality leads.

real-time integration with just about any lead scoring vendor or with proprietary lead scoring engines. To begin exploring lead scoring on your own, the table in Figure 9 should serve as an example of how select data presented in this report might be used by an in-surance agency to begin scoring leads.

Most intrinsic criteria would be added once an action has been taken on a lead. The leads initial score might change depending on the actions taken. For example, the lead previously described would re-ceive a new score of +5 if a com-ment (+2) were to be added to that lead’s record.


mortgageThe external attributes analyzed for the mortgage industry included the following:

Credit Rating Type of LoanLength of

EmploymentHome State Email

Home State

Figure 1 shows the top and bottom eight states of residence for mortgage leads in terms of quality based on con-version rates. For some reason, during the period studied, leads who resided in Vermont converted almost four times better than the average mortgage lead. Conversely, leads from North Dakota had the lowest conversion rates. Their conversion rate is less than a quarter of the average conversion rate. Although this state specific data can be very valuable, it is recommended that data such as this be recalibrated over time. Various state regulations or economic factors could have an effect on these types of results. Continuous updates for highly time sensitive data is recommended, but having a starting point and some initial data is highly advantageous and infinitely better than having no data at all.

VERM

ONT

TENN

ESSE

E

IDAH

O

HAW

AII

NEW

HAM

PSHI

RE

ALAS

KA

UTAH

KENT

UCKY

NEW

YOR

K

MIN

NESO

TA

CONN

ECTI

CUT

NEVA

DA

WIS

CONS

IN

WES

T VI

RGIN

IA

SOUT

H DA

KOTA

NORT

H DA

KOTA

-100%

100%

0%

-50%

-25%

25%

50%

75%

-75%

74%

Perf

orm

ance

rela

tive

to a

vera

ge c

onve

rsio

n ra

te

71%57% 55%

48%40% 39 %

-25% -27%-35% -35% -39% -42% -45%

-78%Bottom 8

Fig. 1 - Mortgage: Home State276%

Top 8


Email Address

Figure 2 presents the top and bottom eight email domain names used by mortgage leads according to their per-formance above or below the average close rate. The mortgage industry had five domain names that performed at twice the average close rate, more than the education and insurance industries. On the other end of the spec-trum, of the 100+ mortgage leads with snet.net email addresses, not a single one closed. It might also be of value to note that the Google leads shown, which had the second worst close rates, came from email addresses @comparisonads.google.com, which were automatically populated with an email address that included a temporary bridging number and weren’t real addresses. These were NOT actual Gmail or Google addresses, but it should tell mortgage banks that if a lead is received with that email domain, the probability of conversion is probably low.

WOW

WAY

CABL

EONE

CONS

OLID

ATED MIL

VA.G

OV

CITI

ZENS

WIL

DBLU

E

MYW

AY

PEOP

LEPC

MAI

L

NETS

CAPE

KNOL

OGY

OPTI

MUM

ADEL

PHIA

GOOG

LE

SNET

-100%

0%

50%

100%

150%

-50%

200%167%

Perf

orm

ance

rela

tive

to a

vera

ge c

onve

rsio

n ra

te

162%

124% 121% 120%

95% 87% 85%

-41% -46% -50% -60%-70%

-77%-92%

-100%

Fig. 2 - Mortgage: Email Address Conversion Rate

Bottom 8

Top 8


Figure 3 shows the volume distribution of the domain names most commonly used by mortgage leads. As you can see in the pie chart, only about half of mortgage leads provide email addresses, the least of all three industries studied. However, having an email address for a mortgage lead does not appear to be as advantageous as it might be in the other industries since leads without emails actually close at a slightly higher rate than average. Nonetheless, like with all data, the more consistently a piece of information is collected, the more mean-ingful and insightful the analysis and con-clusions that can be drawn from that data.

Fig. 3 - Mortgage: Email Address Volume

Comcast 3%Hotmail

4%

Blank 49%

Yahoo11%

Gmail 7%

AOL6% All Other Domains

20%

-100%

0%

100%

Fig. 4 - Mortgage: Length of Employment

0–5 Years

10–20 Years

81%

20%

40%

60%

80%

-20%

-40%

-60%

-80%Perf

orm

ance

rela

tive

to a

vera

ge c

onve

rsio

n ra

te

-47%

5–10 Years

-22%

Greater than20 Years

24%

Length of Employment

Over 30,000 leads in the mortgage industry were analyzed based on length of employment data, which generally indicated that those with longer employment histories tended to close at higher rates. As shown in Figure 4, the highest close rates actually came from leads with a length of employment between 10 and 20 years, which closed at a rate of over 80% above the average. While those with an employment history of more 20 years still closed at an above average rate, they might not have performed as well as those in the 10 to 20 year range because they might include leads with pension type labor jobs, which may not be quite as advantageous in the mortgage space. The size of the bubbles in the figure is proportional to the number of leads that fell within each range.


Credit Rating

A very important measure of lead quality in mortgage is a lead’s credit rating. Figure 5 shows, as would be ex-pected, that leads with higher credit ratings tend to have higher closing rates. This result is the opposite of what was observed in insurance. The only constant is leads that indicate that they are unsure about their credit rating convert at considerably lower rates in both industries. The size of the bubbles in the figure is proportional to the number of leads that fell within that credit rating.

-100%

0%

100%

Fig. 5 - Mortgage: Credit Rating

20%

40%

60%

80%

-20%

-40%

-60%

-80%Perf

orm

ance

rela

tive

to a

vera

ge c

onve

rsio

n ra

te

-70%Unsure

-40%

None

Very Good

36%

Excellent

24%

Good

-28%

Fair

-28%

Poor-96%


Loan type

Lastly, our study found that loan types that leads may be interested in can also make a difference in the probability of a lead’s closure. Figure 6 shows the remarkable increase in close rate for leads whose loan type designation has been set to conventional. Leads interested in conventional loans close at more than five times the rate of the average lead. Adjustable rate mortgage (ARM) loans and loans selected as “Other”, as a whole, close at less than half the average rate.

-100%

0%

100%

Fig. 6 - Mortgage: Loan Type

25%

50%

-25%

-50%

-75%

-75%

Perf

orm

ance

rela

tive

to a

vera

ge c

onve

rsio

n ra

te

CONV

ENTI

ONAL

BLAN

K

HOM

E EQ

UITY VA FHA

412%

18% 13%7%

2% FIXE

D

REFI

NANC

E

CONS

OLID

ATIO

N

REVE

RSE

CASH

NO C

ASH

PURC

HASE

ARM

OTHE

R-5%

-14%

-35%-39% -42% -46% -47%

-54%-68%

Lead Volume by Loan Type

All Others3%

Purchase13%

Blank57%

Re�nance27%


For mortgage, the intrinsic action that results in the highest boost in close rates for mortgage leads is pitching a loan to a lead. According to our research, leads that have had a loan pitched to them are almost three times more likely to close than those who have not. Figure 7 also shows that in terms of text messaging and manually entered leads, mortgage leads are more like insurance leads and less like education leads. Like in insurance, when a lead opts out of text messaging, it still closes at an above average rate because of the advantage gained from having


the lead’s cell number, and manually entered mortgage leads are less likely to close than imported leads, which are automatically imported from a lead provider or web inquiry form. The one constant across the board is the com-ment action. For all three industries, when a comment is entered in a lead’s record, that lead is always more likely to convert than a lead with no comment in its record. Lastly, we see that leads that have had a bankruptcy or that have been placed in manager review status also have a significantly lower probability of closing.

Fig. 7 - Mortgage: Impact of Intrinsic Criteria on Conversion

100% 150%-50% 50%-100% 0% 200%

Performance relative to average conversion rate

197 %

171%

PITCHED LOAN

SMS OPT-OUT

COMMENT

-40%

-66%

-85%


BANKRUPTCY

MANAGER REVIEW

88%

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

When a lead originally enters a lead management system, its lead score is usually exclusively determined by its extrinsic characteristics. After a lead’s status changes or after actions are taken with or by that lead, the lead’s closing potential can either increase or decrease (as illustrated in Figure 7). Therefore, the lead’s score should be adjusted after the status or action changes the lead’s disposition. Intrinsic characteristics not only illustrate the importance of re-scoring based on actions and statuses taken on leads, but perhaps more importantly, they also remind us that if we don’t have similar dispositions for our leads, maybe we should consider them. For example, if you don’t currently use or track whether a lead has had a loan pitched, maybe you need to make that option available to your staff or you need to remind them to do it more often, especially since that action currently results in almost three times the close rate.


Application for mortgage

Velocify Lead Manager software allows companies to easily prioritize, distribute, and take actions on their leads based on each and 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 a lead. Based on the results of this research, companies should consider collecting some of this information about their leads if they’re not currently doing so. Using these results to make the most out of each lead 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 Lead Manager.

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

Fig. 8 - Mortgage: Extrinsic Scoring

Credit Rating Type of LoanLength of

Employment

VT+2

TN,ID,HI,or NH

+1

ND-1

Excellent or Very Good

+1

Unsureor Poor

-1

Home State Email

Wowway or Cableone

+1

Google orSnet -1

10-20 years+1

Conventional+2


Fig. 9 - Mortgage: Sample Score

From Hawaii

+1From Hawaii

+1

Gmail Email

0Gmail Email

0

14 Years ofEmployment

+1

14 Years ofEmployment

+1

ConventionalLoan

+2

ConventionalLoan

+2

Poor Credit

-1Poor Credit

-1

Total Lead Score

+3Total Lead Score

+3

Fig. 10 - Mortgage: Intrinsic Scoring

Pitched Loan +2

Comment +1

SMS Opt-Out +1


Bankruptcy -1

Manually Entered -1

Manager Review -2


For instance, using the criteria shown in Figure 8, a new mortgage lead en-tering the system with the charac-teristics shown in Figure 9 would be scored a +3. That lead should be placed ahead of leads with lower scores and might be distributed to an agent or rep that is better at clos-ing high quality leads.

Most intrinsic criteria would be add-ed once an action has been taken on a lead. The leads initial score might change depending on the actions tak-en. For example, the lead previously described would receive a new score of +5 once a loan was pitched to this lead (+2).


ConclusionUnderstanding your leads and prospects and the attributes that make them more or less likely to convert or enroll is critical to running a successful operation. The data provided in this report should provide some insight as to how to begin adjusting your lead management practices in order to maximize the value of your leads and prospects. It is important to note that most of this information is not truly actionable without the right lead management tools in place. In order to truly benefit from this type of information, organizations should have a lead management system that effectively includes the following features:

• Automated lead importing• Optimal contact velocity• Automatic lead 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 performance insight• Comprehensive reporting and analytics (that help assess lead quality)• Full 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 leads or prospects according to scores• Dynamic scoring capabilities, as new data is added to a lead’s or prospect’s

record

The combined effect of all of the insight gained from this analysis (and much more that can be added from the analysis of each organization’s own data) is best captured through a lead score, as described in this report. Work-ing with a reputable lead scoring partner or developing your own lead scoring engine in conjunction with a top of the line lead management solution is the best way to capitalize on all of the benefits lead scoring offers. When implementing a full lead scoring process, in addition to the features previously listed, a lead management system should also include the following features:

Happy scoring!

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