Time to Treat Learners as Consumers - marketing to adult learners

Take a page from market research and create better learning.

Main Points

* How adults learn has received the most attention, but how they want to learn could potentially add more value to the learning experience.

* Why not treat learners as consumers with distinct preferences for what, how, and when they learn? And why not assess those preferences prior to creating learning solutions and training interventions?

* Adaptive conjoint analysis, a powerful statistical technique that deconstructs a product or service into a number of discrete elements, can provide insight on what, how, where, and when people want to learn and how likely they are to choose your training programs over others.

One arena that has provided a significant contribution to the training field over the past several years has been that of learning styles. Not only does there appear to be decent evidence that there are a number of distinct learning styles, but also that they dramatically affect the way people go about learning.

Yet, there doesn't seem to be much evidence for how that knowledge is applied in a classroom, other than trying to create a learning environment that caters to all styles. Anyone who has either created or instructed learning programs realizes that this objective, more often than not, falls short of succeeding. At best, the learning experience is compromised by trying to please all of the people all of the time.

But perhaps catering to learning styles is the wrong approach. Is it important to know how learners learn, or is it important to know how they want to learn? It's a subtle, but important, distinction.

Wanting versus doing

Understanding how adults want to learn, not just how they learn, involves different issues. Though how adults learn has received the most attention, how they want to learn could potentially add more value to the learning experience. A lot is known about people's learning styles and approaches, but does that really help create a learning process that answers to a myriad of individual differences in styles and approaches?

That's why learner-driven solutions--whether delivered electronically or otherwise--are becoming so popular. In theory, they let learners determine when, and to some extent how, they want to learn. But, generally, learning styles appear only to provide some information on how people approach learning. They don't necessarily identify people's preferences for learning--not just the approach they take but the context in which they want to take it.

With continuously changing learner demographics and a new generation-type label per decade, it makes good business sense to check in relatively frequently on how learners prefer to learn. Rarely, however, are learners asked about their preferences. Instead, the training community often makes assumptions about what might work, what might be fun, and what might enhance the learning experience, and they hope that in addition to it working, participants will also like it. It's usually more of a "choose your seat when you come" rather than "choose your seat before you decide to come" approach.

In the product world, where consumers have choices and make purchase decisions, research on people's preferences is conducted as a matter of course. Companies need to know the particular attributes to build into a product if it is going to be attractive to their target market. Makes sense.

The question is, Why has that kind of thinking escaped the radar screens of the training industry? Why not treat learners as consumers with distinct preferences for what, how, and when they learn? And why not assess those preferences prior to creating learning solutions and training interventions, instead of alpha or beta tests that evaluate what has already been created?

Just like marketers of consumer products, the goal is to get trial-and-repeat purchase behavior. But just what gets people to purchase in the first place? Taste, feel, price, packaging, convenience, availability--probably some combination of all of those factors and more. However, learners are often not treated in the same way. Assumptions are made about what they will like, based on what they have experienced and how well that has worked.

What if the process were reversed? What if learners were asked for their preferences prior to engaging them in a learning experience? Not ask them what skills and information they need to do their jobs (albeit important), but focus instead on how they want to go about accessing that knowledge in the first place.

A consumer-in approach

The approach that most people take to training is more product-out than consumer-in. That is, assessments and conclusions are made concerning the best way to deliver various skills and information learning, without the benefit of insight from consumers--the prospective learners. In the consumer products industry, consumer insight is critical, and few companies in that industry go to market without that type of assessment. There are, however, tried-and-true consumer research methodologies that can not only be readily applied to the training field, but also that can alter the entire mindset of how a learner is viewed as a potential buyer of learning programs and services.

What's more, learning initiatives are often sold through intermediaries such as HR departments, managers of the learners, senior executives, and so forth--all of whom mean well but impose their own perspectives and values on the buying behaviors of others.

For example, cereal companies don't rely entirely on what moms and dads say about the cereals their kids prefer; the cereal companies ask the kids directly. Car companies don't ask teenagers what cars their parents like most; they ask the parents. In the end, it's the end user--whether cereal eater, car purchaser, or trainee--who decides the fate of a particular product or service.

A page from market research

So, how can learning preferences be identified? There are many applicable techniques used by consumer product companies. One is called adaptive conjoint analysis. ACA is a powerful statistical technique that provides a means of deconstructing a product or service into a number of discrete elements, each with its own value. In doing so, ACA provides insight on the tradeoffs people make when choosing between various products and services. It measures the utility that consumers derive from various features, through the application of advanced fractional factorial experimental designs.

In its simplest form, ACA uses a forced-comparison methodology of preferences for any number of key factors--such as those that will influence the final purchase decision. For ice cream, that may mean brand, flavor, ingredients, fat content, package size, price, or other factor. For cars, it might include make or model (brand), performance characteristics, transmission options, gas consumption, and warranty.

As consumers, we make choices--whether for ice cream or cars--in the form of packages (or bundles) of attributes. If you want the performance and road-handling characteristics of a BMW, you'll have to make a tradeoff with other attributes, such as fuel economy or price. Similarly, if you want Ben & Jerry's ice cream, you have to make a tradeoff in package size; it typically comes only in pints. By asking people to choose from among various packages, each with a bundle of attributes, you can assign relative values (utility in conjoint terminology) to each attribute with remarkable validity.

The notion of adaptive conjoint makes this process even more powerful in that the choices presented to each consumer are different--generated incrementally by computer and based on prior responses or selections. In addition to easing the data-collection process, ACA drives to statistical validity with much smaller samples.

To use that methodology in the training field, some general assumptions must be made. First and foremost is the belief that employees are no different from buyers when it comes to the learning options they choose. They have their own preferences, interests, wants, and needs for learning. And though there are undoubtedly clusters of employees with similar learning preferences, there are also plenty of relatively unique combinations.

People also have deep-seated preferences for how they like to approach their personal development. Inherent in their preferences is a relatively universal attraction to event-based instruction that serves the natural human need for social interaction. It's what we have been most used to throughout our lives.

In fact, most of us were schooled in often-overcrowded classrooms. In other words, most adults are conditioned to learn at a place rather than in a space. There should be little surprise, then, that getting adults to recognize the potential benefits of online learning, for instance, will be difficult to achieve--particularly at any organization where the classroom tradition is well entrenched in the management development culture.

The steps of ACA

The methodology for conducting ACA is fairly straightforward. The following steps outline how ACA would be applied to assessing learner preferences:

1. Define the product attributes to be tested. That is, define the critical factors that would be relevant to assessing learning preferences. Usually, that involves conducting a series of interviews with key internal stakeholders and experts in adult-learning principles to

* develop a robust list of consumer-learner decision factors and identify the specific product attributes used by the "buyers" when they choose a learning product. For learning purposes, the attributes might include such items as the subject matter or topic, where the learning takes place, how it's delivered, the time required for completing the learning experience, and so forth.

* organize and map out key decision factors into independent dimensions or components, which are often referred to as attributes

* prioritize by distributing 100 points across those attributes

* gather precise wording and vocabulary used to describe various differences in service offerings, which are often referred to as levels.

2. Develop conjoint analysis survey.

You do that by entering the information and definitions gathered in step 1 and constructing a set of product attributes and levels to be tested and by

* adding any segmentation questions or opinion questions that may be useful in interpreting the results

* creating a survey using special computer software and testing it with the subject experts and stakeholders

* refining and pilot testing the survey.

3. Administer the survey to the target audience. That involves

* using individual disks that contain the interviewing software and questions

* compiling the surveys and analyzing the results

* presenting the results to a subset of the respondents to ensure that the results were interpreted appropriately.

4. Document the survey results.

You also should share the findings with the program managers. Conjoint analysis can provide data that can be used to identify what the optimal bundle of training looks like from the training customers' perspective: How long should it last? Where should it be done? What medium should be used?

ACA is a statistical technique that measures the utility that customers derive from various product and service features. ACA also provides insight into the tradeoffs that customers make when choosing a product or service.

In the field

Following is some actual data collected from a group of more than 100 new supervisors asked to provide input on how they would most prefer to learn job-relevant information and skills for their new roles.

In this situation, the learning experience was deconstructed into five actionable attributes. Each attribute had a number of levels (or options) that were combined to create potential product combinations or learning offerings. Respondents were presented with 13 pairs of hypothetical product bundles and asked to indicate their preferences.

By repeating that process, it's possible to quantify which features a respondent likes and dislikes. It's also possible to determine the strength of someone's preferences. The result is a thorough mapping of a decision maker's utility or usefulness of each product attribute. The assumption is that utility values are additive. Therefore, it's possible to examine attribute tradeoffs and identify the impact on total package utility.

The results provide a quantitative basis for strategic product or, in this case, training decisions around the desired features and needs of targeted markets. Which target segments value which product bundles the most? The conjoint analysis can be used to examine the tradeoffs made by learners with respect to important dimensions identified by the management development team.

For example, the box shows the various attributes and respective levels used with these first-line supervisors to assess their learning preferences.

Conjoint analysis will also provide information about the relative importance of the attributes to learners. As shown in the bar graphs, for these supervisors the research identified that the learning medium and the subject area were most important. It also identified that the method used and the time required to complete the training were relatively less important.

Conjoint analysis can also permit a tradeoff analysis that will indicate that learners would be willing to study a certain subject area under certain conditions but not under others. An example of that is illustrated on the next page. These learners seem to value--that is, they are more interested in--studying effective leadership rather than administrative forms. Furthermore, when compared without reference to subject area, they also seem more interested in acquiring information in a learning center than in a home environment.

The value of this type of analysis is that it offers the opportunity to combine attributes to create "most-preferred" or "desirable" learning scenarios, taking into account a number of tradeoffs that learners might be willing to make.

The example in the top box suggests that learners, given the choice, would more likely prefer studying effective leadership at home (note the additive utility points value of 38 + 9 or 47) more than learning administrative forms in a learning center (with an additive utility points value of only 36 + 5 or 41). The concept of utility points being additive makes it possible to see that some topics can be learned individually at home, while others would be more desirably taught in a group setting at a learning center.

Conjoint analysis also lets managers estimate how attractive various possible program structures would be to their staff. That can be done by combining various attribute levels into bundles and then summing the utility points to estimate which bundles would be more attractive.

The example below shows that from a product-offering preference perspective, conjoint analysis indicates that learners would derive more value (and thus be more interested in) training program bundle B than training program bundle A. Learners would see almost six times more value in bundle B (172 utility points) than in bundle A (30 utility points).

In addition, it's important to ask traditional attitudinal and behavioral descriptor questions about the way people like to learn, the amount of time they spend in formal and informal training, and so forth. Last, demographic information is collected regarding their jobs, geographic locale, length of service, and so on to permit various cuts of the data in the analysis. Based on that information, you can create a paired comparison questionnaire and deliver it electronically to assess the overall preferences of a representative sample of learners.

So, a widely used market research tool can be helpful in the design and development of learning experiences. The ACA methodology enables a better understanding of just what learners would be willing to trade off for an optimal learning experience. It also provides a comparison of various learner program configurations and which ones learners prefer most.

What conjoint results are able to show is that learners have distinct preferences for what subjects they want to learn, how they want to learn, in what timeframe, and under what conditions.

A potential limitation to this approach, as might be expected, is the fact that in the absence of real long-term experience with certain types of learning experiences, most people would find it difficult to judge the actual value of a particular learning experience accurately. That is, it would be difficult to assess their preference for something they haven't truly experienced. Put another way, people don't know what they don't know. At best, they can only guess what that experience might feel like. As such, some conjoint results must be taken with a grain of salt until learners have had the experience.

On the other hand, this approach does offer the opportunity to assess exactly what the factors are that might prevent someone from evaluating a yet-to-be-experienced learning event accurately.

The value in treating learners as consumers by applying a technique used in consumer research is that it can offer valuable insight on configuring the most appropriate learning experiences for learners. If you believe that learners do indeed have points of view, then it would be prudent to conduct this or similar analyses if you want to know the best way to attract people to your training and learning experiences. Indeed, this type of information is invaluable for charting the learning strategies that an organization takes on to meet its performance improvement agenda.

Stephen L. Cohen is managing director of Dove Consulting's People Performance Group, which creates custom learning systems and performance improvement interventions for corporations worldwide; scohen@consultdove.com

David W. Dove is chairman, CEO, and founder of Dove Consulting, an international strategy and organizational effectiveness consulting firm; ddove@consultdove.com.

Edward L. Bachelder is director of research for Dove Consulting, where he leads research and analytical projects; ebachelder@consultdove.com.

Edward L. Bachelder realized he was a grown-up (per our questionnaire) when he figured out why Pampers boxes are all the same size regardless of the quantity inside. (Can you figure it out?)

Former cabana boy 'a la the movie Flamingo Kid Steve Cohen says that he's anxiously looking forward to the moment when he realizes he's a grown-up.

Eight Days a Week is David Dove's philosophy bumper sticker.

                      Learning Attributes and Levels:
                          First-Line Supervisors
                   Attribute                Levels
Subject Area       [] Problem solving       [] Effective Leadership
                   [] HR policy             [] Corporate culture
                   [] Interpersonal skills  [] Admin, Forms, and info
Where Learning Can [] Office                [] Corporate training
Be Accessed        [] Home                     center
Medium             [] Paper-based           [] Group interaction
                   [] Computer-based        [] Audio/visual tapes
                   [] Face-to-face
Learning Method    [] Simulation            [] Case studies
                   [] Role play             [] Hear experienced
                   [] Exercises                managers
                   [] Self-assessment       [] Traditional classroom
Time Required      [] 1 hour                [] 16 hours over 3 months
to Complete        [] 2 hours               [] 1 whole day
                   [] half-day              [] 2 whole days
                   [] 8 hours over 3 months [] 5 whole days
                          Relative Importance of
                              Each Attribute
Medium           26.1%
Subject Area     23.2%
Where Accessed   19.0%
Time to Complete 16.2%
Method           15.5%
                     Example of One Possible Tradeoff
Attribute       Levels               Utility Points Delta
Subject Area    Effective Leadership       38       +33 points
                Administrative Forms       5
Where Learning  Learning Center            36       -25 points
Can Be Accessed Home                       9
                      Utility Points for Two Possible
                             Training Bundles
Attribute Bundle A             Utility Bundle B             Utility
                               Points                       Points
Subject   Administrative forms 5       Effective Leadership 38
Where     Home                 9       Learning Center      36
Medium    Paper-based          9       Group Interaction    46
Method    Self-assessment      5       Experienced Manager  27
Time      1 hour               2       2 days               25
TOTAL                          30                           172

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