Computing True Return on Investment using Customer Lifetime Value in the Continuing Education Industry: A First Glance

By Jacob Ensign, Business Analyst, JMH Consulting, Inc.

Return on Investment (ROI) is a powerful concept in any business or non-profit organization. Knowing the ROI of marketing efforts, process improvement, or project initiation facilitates your ability to improve your organization. However, truly understanding ROI depends on another, more elusive concept: customer lifetime value.

For example, by mailing a brochure to acquire a new registration, you can easily compute the ROI of that brochure mailing. If you track who received one of your brochures, you can count all of those individuals who end up in your registration database. Add up all the money you earned through additional registrations, divide by the amount you invested in your brochure (designing, printing, and mailing costs just to be thorough), and you have the ROI for your brochure, right? Not exactly.

There is a real difference between the ROI describe above and the true ROI for the mailing. Below are two scenarios showing two different ways of calculating ROI:

1. The ROI of a marketing effort that adds 200 new people to your registration database who take only one class
2. The ROI of a marketing effort that adds 200 new people to your registration database and a portion of those students return to take two, three, four, or more classes

The second scenario illustrates the only way to compute true ROI, and it depends on the rate at which your customers come back to take those extra classes. We can only calculate the true ROI of a mailing marketing effort by counting all individuals who entered your database because of the mailing and dividing the total amount of revenue generated through all of the classes taken in their lifetime by the amount spent on the marketing effort.

We have two choices when we want to compute the true ROI of our marketing efforts: either wait until everyone who received your brochure has passed away and count the money they have given you or create a best estimate using the concepts rate of return (ROR) and customer lifetime value (CLV).

The relationship between ROR, CLV, and ROI

A good definition for the rate of return (ROR) of a customer in the continuing education industry is the probability that a person sitting in one of your classes signs up for a class at a later date. That means that the ROR is always a number between 0% and 100%. An ROR of 0% means that no one ever takes another one of your classes. An ROR of 100% is perfect, and it means that everyone always returns all of the time.

The best definition for the customer lifetime value (CLV) is the total amount of money a customer gives you while s/he is your customer. Therefore, rate of return (ROR) is a probability or percent, and customer lifetime value (CLV) is a dollar-amount. We can only compute ROI if we know the customer lifetime value (CLV) of a new customer. The best way to compute customer lifetime value (CLV) is to use the rate of return (ROR).

Assuming that you know the ROR for one of your programs is 30% and that all of your classes in that program cost $100, we can easily compute the CLV for a new registrant. Let us say there is a group of 100 new students that sign up for a class.

1. The 100 students take your class, paying you $100 each, $10,000 in total.
2. Since the ROR is 30%, 30% of the students (30 of them) sign up for a second class, each paying you another $100, another $3,000 in total.
3. Since the ROR is 30%, 30% of the 30 (or nine of them) return to sign up for a third class, each giving you yet another $100, or $900 in total. The nine represent 30% of 30% of the original 100, or
   
4. Of the nine that took a third class, 30% of them will return for a fourth and pay you another $100. That number is equal to
   
5. This continues until no more students return. The total amount we collected is somewhere around $14,000, or
   
   Therefore, the CLV is around $140 for each of these customers.

In general, we write the CLV as follows:

A cleaner way to write this is as follows:

So in our example above, we have that

If you know your ROR, you know your CLV. If you know the CLV, then you can compute the true ROI of all marketing engagements.

Factors that influence the rate of return

Representatives from adult education organizations have claimed that there is a “typical” ROR.  Unfortunately, there is no “typical” customer or “typical” offering in adult education because of the diversity in programs. ROR will always be different for different groups of customers. For example, personal enrichment programs can expect to have a higher ROR, so can non-cohort certificate programs. However, a long cohort certificate program will probably have a very low ROR. In general, ROR can differ dramatically between different types of programs (certificate versus open enrollment, cohort versus non-cohort).

So what drives ROR? The list below is not exhaustive, but each item describes characteristics that may affect your ROR.

• More dynamic course offerings will increase your ROR. The more often your course offerings change, the more likely former participants will return for other classes.
• More diverse course offerings will increase your ROR. The broader the range of subjects covered by your course offering, the more likely former participants will find something else that interests them.
• Creating clear, logical groupings of courses will increase your ROR. For example, certificate programs with many electives, or series of courses (level 1, level 2, etc.).
• Customer experience during the registration process can influence ROR. The smoother the process, the more likely the customer will return. This includes the online registration process and the professionalism of staff speaking to customers on the phone.
• Customer experience in the classroom can also affect ROR.
  • Did the participant get what was expected?
  • Did the participant get more?
  • Did the participant leave the class with the sense that even more knowledge would further benefit them?
  • Is there value in taking the “same” class more than once? For example, theatergoer classes change seasonally.
• The convenience and accessibility of your classes will influence ROR.
  • Classes scheduled at times that are convenient for likely participants will improve ROR.
  • Classes conveniently located near the bulk of your existing customers will have a greater ROR.
• Demographic characteristics of your target student population can affect ROR. For example, if you have a program that caters to retired persons, your ROR is probably higher because competition for your student’s free time is less of a factor. If you offer programs that help participants change careers, your ROR is probably below 10% since most students are only changing careers every few years (one should hope).

What others say

The Online Education Database compares the quality of colleges by using retention rate, which is just another way of saying rate of return. They suggest that the rate of return is a critical statistic to use in comparing one education program from another. Higher retention rates imply a more attractive curriculum, more competent administration, and overall higher quality. While this is probably true, the methodology used by the College Navigator database to compute these statistics cannot be applied to the continuing education and adult education industries. I’ll explain.

According to the Online Education Database website, the College Navigator defines retention rate as the percentage of first-time students from the fall of one year that enroll again in fall of the next year. However, the continuing education industry has a shorter “educational lifecycle” than most academic programs. In our industry, we have to consider that some customers will take more than one class in the same term and that students can often gain the knowledge they need in less than a year. Comparing student repeat rate from one year to the next is simply impractical in our industry. To compute ROR, we need to think about student behavior more like a business than a college.

Learning Resources Network (LERN), a leader in the adult education industry, which supports continuing and adult education programs, suggests that you should aim for a 30–50% annual repeat rate. In a set of performance criteria used by LERN to evaluate a program’s positioning for future success, LERN recommends that the repeat rate should be 50% or more. When I tried to compare some real registration data to these recommendations, I struggled with two questions.

First, how do we account for the various natures of the different programs in a department, i.e. cohort certificate programs versus open enrollment? Cohort certificate programs that help people change careers hope to achieve a lower repeat rate. Ideally, a career-changer will come to the program, earn the certificate, and then get and keep a new job. A high repeat rate for this type of program (i.e. students returning to retake that program or take another certificate program) is a likely sign of a low quality program. In contrast, if you run leisure programs for retired persons, you should expect an ROR much higher than 50%. Such programs generally depend upon continued revenue from the same individuals over many years.

Second, how should we account for the value of getting students to repeat within the same year? Should we value the fact that student X took a class in fall 2007 and one in fall 2008 more than student Y who took three classes in fall 2007? LERN’s model is ultimately similar to that used by the College Navigator. Neither provides an accurate measure of ROR in our industry.

Best practices in computing CLV and ROR

Neither of the above definitions of ROR seems to work for the continuing education industry, especially if the goal is to determine ROI. Comparing two disparate periods confuses the purpose of computing CLV, determining the true ROI of your marketing efforts.

So, how do you compute ROR and CLV? Below, we outline some general rules you can follow when computing CLV and ROR.

Rule #1: If the rate of return should be close to zero, it is not important to track

If you only run certificate programs that do not have any after-certification electives, you will waste time by trying to compute ROR. It is simply not compelling information to track or quote.

Rule #2: If the rate of return should be close to perfect, obsess about it

Programs for retired professionals that sell memberships, versus individual classes or groups of classes, should aim for a very high ROR. ROR significantly influences your profitability. Return customers represent revenue that you can acquire without spending money on marketing. This means that tracking ROR and maintaining ROR at high levels are both critical tasks.

Rule #3: Consider special marketing adjustments

Above we described how to compute CLV using the ROR; however, our model above was simplistic. It effectively demonstrates the relationship between ROR and CLV, but many pieces to the puzzle are still missing. A better model will also include each of the following factors:

• The money you spend marketing to an existing customer between first and second class decreases your CLV.
• Special promotions or discounts provided to your customers, particularly loyalty rewards, will likely increase your ROR but decrease your revenue collected for that engagement. This may increase or decrease the CLV.
• The rate at which you acquire new customers through word-of-mouth engagements will increase your CLV.

Rule #4: Consider the total number of classes taken when computing customer lifetime value

I mentioned this above, but it is important and ignored often enough to warrant reiteration. The CLV of a customer that takes two classes in fall 2007 and one in fall 2008 should be higher than the CLV of a customer that takes one class in fall 2007 and fall 2008.

Rule #5: Your business model should drive how you compute rate of return

The ultimate question for computing the ROR is, “How long of a period must a customer not return, that you no longer consider him/her an active customer?” In other words, when is it safe to say that a customer has defected?

You can potentially answer this question based on your current business practices. How long do you continue mailing brochures or catalogs to a previous customer if they do not return to take another class? If you only market to customers from the previous two years, then someone who has not taken a class with you in more than two years is no longer an active customer.

You can also determine the answer to this question based on historical registration trends. By studying how long it takes typical customers to return, you can determine what the upper bound is. This approach is more mathematically sound than the previous; however, it often requires a specialized understanding of statistics to implement.

Final thoughts

As we have seen, the all-important marketing concept of ROI depends heavily on two other elusive concepts, customer lifetime value and rate of return. In particular, finding the rate of return of your customers proves to be a challenging task even if you have a deep understanding of the concept and the mathematics behind it.

While computing ROR is complex, one of the rules, specifically the fifth, provides great insight on how to compute the best simple ROR. However, making use of this rule entirely depends on your ability to understand the customer lifecycle and customer defection of your customer base.

In my next article, I will focus on a data-driven approach that answers the question of customer defection, i.e. when can you consider a customer defected? Then, we will explore how to use that information to compute a realistic rate of return, customer lifetime value, and return on investment for any department in the adult education industry.