Implications for Higher Education of the Generalized Differentiation Strategy Model

by Dr. Scott Moore | April 30, 2020

Estimated time to read: minutes

The previous essay focused on defining the Generalized Differentiation (GD) Strategy Model. It was detailed and required the reader to do a lot of thinking and research in order to gather the information needed to use it. In this essay, my goal is to show the reader that all of the effort was warranted. I describe four important takeaways from the model, and then highlight multiple ways an educational leader can use GD-focused insights to increase enrollment and increase profits.

Higher Education Strategy Series: This is entry #6 of this series. This page describes the whole series and provides links to all of the articles.

1. Important takeaways

The most important high-level takeaways from this model are that it emphasizes the importance of the following:

  • A deep understanding of target prospects, 
  • A deep understanding of the relative strengths and weaknesses of your program in comparison with competitor programs, 
  • Differentiation-related choices,
  • The alignment of marketing efforts, feature development and investment, and an understanding of what prospects know about and have insight into. 

The following sections discuss each of these in turn.

Target prospects

A program must have deep insights into its target prospects. It must understand as thoroughly as possible what features they value deeply and what features they simply want to be good enough. This knowledge would help the institution know where to focus their efforts at improving the program. It also helps the institution know which prospects to target and what message to convey to those prospects.

A word of warning: It can be seductively easy to use current students or alumni as a proxy for an institution’s target prospects. This would significantly bias the data collection, since only prospects who elected to enroll would be included in the analysis. It would be quite useful to understand those prospects for whom the institution was a finalist in his/her decision process but who decided to go elsewhere:

  • What factors were important to them?
  • Which did they merely satisfice on?
  • What institutions were they also considering in addition to the one that they decided to attend?

Gaining this knowledge would throw further light on which features the institution should be focusing on (for both investment and marketing efforts) since improvements would likely bring additional student enrollments.

Relative strengths

A program must understand its own strengths relative to its competitive set given its target customers. This understanding must penetrate to the feature level at which prospects decide on one program over another. This knowledge guides leadership to determine where to invest (to improve those features that need to be improved) and how to market (to emphasize those features that are strong relative to other programs). Note that the competitive set is not determined by the institution; it is determined by the target prospects. They are the ones who determine which programs they are considering.


The program must ensure that it is better on some feature than its less-expensive competitors or it risks irrelevance; further, if it wants to charge more, then it should choose at least one feature on which it will be markedly better than its competitive set. The most likely candidates on which to differentiate are those features that prospects within the target cluster value highly. By improving the quality of the feature and by simultaneously increasing marketing efforts so that knowledge of the feature also increases, the overall value that prospects place on the program would increase, thus leading more prospects to enroll in the program.


As described earlier, a program must simultaneously coordinate three major processes: Marketing efforts must align with the choices made during program definition, and both of these must align with program investment. A high-quality feature of a program only raises the overall value for a prospect if that prospect has deep and accurate knowledge of it. If the institution is not going to market around a feature or if target prospects don’t care about that feature, then it shouldn’t invest in that feature. Similarly, if a program does not have a high-quality feature, then the institution should not work at marketing it. Again, all of this depends on the institution understanding who its target prospects are and what features they value.

2. Increase enrollment

There are multiple ways to increase the number of prospects who choose to attend a program: 1) Increase the quality of highly valued features, 2) improve satisficed features up to the minimum level, 3) improve the knowledge of target prospects related to highly valued maximized features, 4) change the feature profiles of prospects, and 5) avoid dominant competitors. Let’s look at each of these in turn.

Increase the scores on maximized features that are highly valued by target customers: This will pay off by increasing the overall value for the program, thereby increasing the chance that prospects will choose the program. Further, a program should spend on those features in the most effective ways; that is, for every dollar spent on a feature, the quality of that feature would rise more quickly than for other features for those people in its target cluster. The implication here is that if an institution cannot efficiently raise the quality of a feature, then it should consider hiring an outside firm who can or else work on raising the quality of another feature.

Increase the scores on satisficed features to the minimum level: This will increase the number of acceptable programs, thereby increasing the number of prospects who include the program in their consideration set. This, of course, requires that the institution knows both which features are satisficed and the minimum level required for such. An extensive knowledge of the features of competitor programs would help with setting the minimum level, but deep insight into target prospects is really what is needed here. The institution must ask prospects which features are their satisficing features (and, of course, which are their maximizing features) as well as the minimum required level for those features.

Increase target prospect knowledge of highly valued features: This will yield return by also increasing the overall value for the program, especially if some features are less well-known. This is a relatively easy way both to increase the number of prospects who choose to enroll in the program and to improve the prospect-program match by increasing the accuracy of the prospect’s knowledge.

Manipulate prospect feature profiles: A program can try to change a prospect’s value profile so that a particular prospect places more value on those features that the program scores well on and less value on those features that the program scores lower on. For the marketing team, this is both a more difficult and a longer-term play.

Stay away from dominant competitors: A program should, if possible, not choose to compete for a target cluster when the arena is dominated by another program (i.e., the other program is as good or better on every dimension). If a program feels it has to win by outmarketing another program — simply because it isn’t actually as good as its competitor — this can be a losing and expensive proposition.

3. Increase profit

There are multiple ways to increase the total profit at a specific program: 1) Spend less on features that prospects do not value highly, 2) spend more on the highest-valued features and less on middle-valued features, and either or both of 3) increase tuition, and 4) increase enrollment:

Spend less on low-valued features: While it might make sense for other organizational reasons to spend money on particular features, when the majority of prospects place a low value on a feature, spending on it can be a money-losing gambit.

Match value profile: More generally, match the spending profile on features so that the institution works to increase the quality of highly valued features and spends less on the quality of low-value features.

Increase the program’s tuition rate: This choice is usually not on the table, as the university sets something of a standard rate that can’t be changed much. It might be possible to create a noncredit certificate, the price of which can more accurately reflect market demand while also using resources that have been created for the tuition-based program.

Increase enrollment: This is a hugely complex decision when the variance from a particular expected enrollment is considered. Increasing enrollment can be done in several ways: 1) Increase the number of students per section, 2) increase the number of sections, or 3) increase the number of entry cohorts per year. The first choice has implications for the types of pedagogies that can be deployed. The second choice has implications for faculty staffing: Does the institution have sufficient staff to run the number of sections needed? The third choice also has faculty staffing implications — not only is the institution running the section multiple times (as in the second choice) but those are occurring at different times during the year, which would likely be less efficient for the faculty and support staff to carry off.


For this series, I am posing activities for an educational leader to complete. The unifying project for these activities is to define a medium- and long-term plan for competing and winning online.

  1. Does your marketing and/or admissions department do follow-up interviews with applicants who chose to go elsewhere? Do you see the advantage of conducting these interviews? If you do but these are not conducted, who can you speak with about starting the practice?
  2. Does your program have satisficed features that are simply not good enough? What can be done to improve them?
  3. After the previous article in the series, you gathered rough data needed by the GD framework. During your first pass through the data, you should sort the features from large to small by value points. Look at the knowledge points for the features at the top of the list. Which features do you see have low knowledge points even though they are high on your value list? This tells you that you need to invest in increasing prospect knowledge for those features. 
  4. In competing in an online world (which the COVID-19 pandemic has rushed into occurrence), the importance of an alignment of marketing efforts, feature development and investment, and an understanding of what prospects know about and have insight into cannot be overstated. Go back to your list of features that you developed after the previous article. Go through each one of those features and note what is being done to improve each one of them.
    1. Are there important ones that are being ignored? Why?
    2. Are there less important ones that are receiving apparently too much attention or investment? Why?
    3. Are there important ones that prospects do not know well? Why is that? What might be done to improve their knowledge?

Feel free to reach out to me if you have any questions or comments. 

Keep Learning

Define and Act on Your Institution’s Strategy

Dr. Scott Moore has written a 15-part series on defining and acting on a higher education strategy to guide leaders during these difficult times. It is targeted at educational leaders who are participating in shaping their school's actions during and after the COVID-19 pandemic.


Dr. Scott Moore

Dr. Scott Moore is a former Principal Learning Strategist at Extension Engine. In this role, he led the global Custom Learning Experience practice. He worked with dozens of nonprofit, higher education, and learning business organizations as they considered using online learning to support their mission and margin, using his deep understanding of organizational dynamics, online learning, strategic differentiation, decision-making, and more. Prior to joining Extension Engine, he was a faculty member, administrator, and dean at Michigan Ross and Babson College for 20+ years. He holds an M.B.A. from Georgia Institute of Technology and a Ph.D. in Decision Sciences from the Wharton School of the University of Pennsylvania.

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