From a business perspective, the most critical elements of the customer experience involve the choices that customers make: the choice to buy; the choice to recommend; the choice to continue as a customer. In practice, most organizations have insufficient insight into how customers consider their alternatives and make choices. Even worse, many organizations do things that complicate customers’ decisions and create barriers to profitable customer behavior. In this post, I will provide several frameworks that can help companies understand how their customers decide and inform how to design an experience that removes barriers to profitable customer behavior.
Rational economic theory makes the assumption that: the more choices customers have, the better. Certainly, there are several kinds of experiences where an extensive set of options increase the likelihood that customers will be satisfied. These include:
Preference Matching Experiences, in which customers initiate the experience knowing what they are looking for. For example, if they walk into a video store looking for a particular title, the greater the selection, the more likely they’ll walk out satisfied. If they go to dinner with a diverse group of friends, all wanting to order their favorite meal, the more items on the menu, the more satisfied people in the group will be.
- Exploratory Search Experiences, in which customers are “foraging” for novel alternatives that match their unique or even idiosyncratic interests. The ability to search for and find novel choices similar to other choices they’ve enjoyed in the past are a significant part of the appeal of “long tail” providers like Netflix, Amazon, Apple’s i-Tunes, etc…
However, not all experiences are Preference Matching or Exploratory Search Experiences. A growing amount of evidence suggests that, in most cases, people have a difficult time managing complex choices. As the attractiveness of product or service alternatives rises, people experience conflict and, as a result, may put off making a decision, choose the default option, or simply opt out. Research suggests that as the number of alternatives increases, people simplify their decision making processes by relying on heuristics, they tend to consider fewer alternatives, and process a smaller fraction of the available information regarding those alternatives.
Emerging neuroeconomic research supports a more information-processing approach based on bounded rationality. This research demonstrates that decision makers have limitations based on both their level of motivation and their capacity for processing information; including limited working memory and computational capabilities. The fact is, people today are overwhelmed with activities, information, and choices. The challenge has become how to manage this complexity and keep things simple. Evidence supports the conclusion that “choice overload” can be a barrier to profitable customer behavior.
In one representative experiment, conducted by Iyengar and Lepper, consumers shopping at an upscale grocery store were presented with a tasting booth that displayed either a limited selection (6) or an extensive (24) selection of different flavors of jam. The experimenters measured both customers’ initial attraction to the tasting booth and their subsequent purchase behavior. While the extensive choice booth attracted more customer attention, customers presented with the limited set of choices were 10 times more likely to make a purchase. Customers that sampled from the limited choice booth made a purchase 30% of the time versus only 3% of the time from the extensive choice booth. Leading companies are really starting to understand this. P&G, for example, reduced the number of versions of Head and Shoulders shampoo from 26 to 15, and, in turn, experienced a 10% increase in sales.
As the complexity of the information and choice environment increases, people tend to simplify their decision making by relying on more simple rules of thumb or heuristics that reduce that complexity. For instance, a study of the decision strategies of people presented with three, six, or nine alternatives revealed that 21% used an elimination strategy when presented with three options, 31% used an elimination strategy when presented with six options, and 77% used an elimination strategy when presented with nine options.
It’s helpful to consider a few basics regarding how people make decisions. One way to look at it is that decision making can be characterized at four levels, based on the complexity of an individual’s goals and the intensity of their involvement:
Level 1 – Recognition-Based Decisions. This includes many of the quick and largely automatic, unconscious, or habitual decisions people make every day. When these decisions are made, the person does not pay much attention to attractiveness; they simply “know” from earlier experience what decision to make in a particular situation.
Level 2 – Simple Attractiveness-Based Decisions. This includes decisions made with reference to one or a few attractiveness attributes favoring the chosen alternative. These decision problems do not involve conflicts between attributes and often the solution is quite obvious the customer. These decisions may still be driven by habit or affect (feeling). They also may be made as quickly as Level 1 decisions.
Level 3 – Alternative-Based Decisions. These decisions involve choices between alternatives with either goal conflicts. In most cases, some attributes favor one alternative while some attributes favor other alternatives. Repeated decisions at Level 3 may become transformed to Level 2 or even Level 1. Most of the existing research addresses problems at this level.
Level 4 -Innovative Problem Solving Decisions. In these situations, alternatives are not fixed, nor is there a set of attributes that characterize these alternatives. At this level, creative problem solving is an important sub-process that leads to the generation of alternatives to be considered.
In most cases, customers would prefer to make decisions at the lowest possible level that yields a solution that meets their needs. Customers can’t engage in alternative based evaluations or innovative problem solving as part of every experience. In some ways, this contradicts the “buzz” on customer co-creation. I’ve had smart people suggest that the whole idea of customer experience design is outdated because customers should just be able to design, configure, or co-create their own experiences. I believe customer co-creation is one of those management concepts that is in the irrational exuberance phase. There is certainly an important role for co-creation, however, across the wide range of experiences, the ability to navigate more of them on automatic pilot is the key to survival in the customers’ world of expanding complexity.
Researchers have compared customer expeiences that offer a limited, more psychologically manageable, versus an extensive, more psychologically excessive, number of alternatives and found that while experiences offering extensive alternatives may be initially perceived as desirable, they often create barriers to customers making choices and feeling satisfied with those choices after the fact.
Customers in extensive-choice situations tend to describe their decision-making process as being more enjoyable because of the wide-open opportunity it gives them but also more difficult and more frustrating. In addition, extensive-choice participants report being more dissatisfied and having more regret about the choices they’ve made than did limited-choice participants. This is particularly true in situations where customers feel they must make a choice for the objectively best option… rather than one that just reflects their unique personal preferences.
Understanding How Customers Make Decisions
Customers follow decision strategies that are dependent on the situation, their goals, and their persona. Rather than an invariant approach to solving choice problems, customers leverage a wide range of approaches, often developed on the spot. Since decision makers generally cannot process all of the available information in a particular situation, they develop problem representations by filtering or restructuring the available information. Hence, which information is selected for processing can have a major impact on their choices.
In general, it is not true that customers follow complex decision processes for complex, high involvement decisions. In fact, as the complexity of the decision increases, the complexity of the decision process often decreases. As decision situations become more complex, customers use rules of thumb or heuristics to reduce the complexity of the options and the information they must consider. For example, options that are superior on the most prominent of a small set of attributes are favored as the decision task becomes more complex.
The decision process that customers follow and the options they select, will depend on the extent to which customers’ goals are: minimizing the cognitive effort required for making a choice, maximizing the accuracy of the decision, minimizing the experience of negative emotion during decision making, and maximizing the ease of justifying the decision, or some combination of such goals.
The way customers make choices is also highly dependent on the way their choices are presented or framed. This creates a significant opportunity for providers that understand the psychology of their customers’ decision processes to present options in a way that improves the customer experience and drives additional revenue. One of the ways to do this has to do with understanding customer attention. There are two different types of attention: voluntary and involuntary. Voluntary attention is devoted to information perceived to relevant to the customer given their current goals. Individuals will devote effort to examining information they believe will help them attain whichever goals are more heavily weighted in that situation. However, attention also may be captured involuntarily by aspects of the environment that are surprising, novel, unexpected, potentially threatening, or extremely salient, thus exemplifying one aspect of accessibility. For example, changes and losses may be particularly salient and particular problem representations may make certain aspects stand out and gain involuntary attention. Thus, attention and selectivity can be influenced both by goal driven and more involuntary perceptual factors.
Level 3 decision strategies can be characterized based on the overall amount of information considered, the selectivity or comprehensiveness of attributes that are considered, whether the customer focuses on their top down alternatives or bottoms up attributes, and whether the customer makes cross-attribute tradeoffs. (See Bettman, James R., Mary Frances Luce, and John W. Payne, “Constructive Consumer Choice Processes“) Common Level 3 decision strategies include:
Weighted Adding. This consists of considering one alternative at a time, examining each of the attributes for that option, multiplying each attribute’s subjective value times its weighted importance and summing these products across all of the attributes to obtain an overall value for each option. The alternative with the highest value would be the one that is chosen. Because weighted adding involves extensive information processing and explicit tradeoffs, it is often considered to be more the most effective approach. However, customers don’t generally follow this approach because it places significant demands on their working memory and computational capabilities. Nevertheless, weighted adding is the decision model that underlies many of the techniques used by marketing researchers to assess preferences. Note: An Equal Weight Strategy is a variation of Weighted Adding, in which there is an implicit assumption that each attribute is of roughly equal importance.
Most Important Attribute Selection. This strategy provides a strong contrast to weighted adding: the alternative with the best value on the most important attribute is simply selected (assuming there were no ties on that attribute).
Satisficing. Involves finding the first “good enough” option. Options are considered sequentially, in the order in which they are encountered. The value of each important attribute for the current option is considered to see whether it meets some sort of cutoff level for that attribute. If the attribute fails to meet the cutoff level, the option is rejected, and the next option is considered. One implication of satisficing is that the option chosen can be significantly influenced by the order in which the options are encountered.
Elimination by Aspects. Combines elements of both the most important attribute and satisficing strategies. Elimination by aspects rejects options that do not meet a minimum cutoff value for just the most important attribute. This elimination process may be repeated for the second most important attribute, with processing continuing until a single option remains.
Majority of Confirming Dimensions. Involves alternatives that are processed as pairs, with the highest values of the two alternatives compared on each attribute, and the alternative with a majority of winning (better) attribute values is retained. The retained option is then compared with the next alternative from the consideration set, and this process of pairwise comparison continues until all the alternatives have been evaluated and one option remains.
Feature Voting is a simple approach that just counts the number of good and bad features characterizing each of the alternatives and then selects the alternative with the greatest number of good and/or the fewest number of bad features.
Choice Heuristics. Individuals often use a repertoire of rules of thumb or heuristics for solving decision problems. These heuristics are acquired through experience, imitation, or training. This could include customers that will “pick the second least inexpensive alternative (e.g., CD player, bottle or wine) from a brand whose name I recognize.”
Customers frequently use combinations of these strategies. A typical combined strategy might have an initial phase in which some alternatives were eliminated and a second phase where the remaining options are analyzed in more detail. One frequently observed strategy combination is an initial use of Elimination by Aspects to reduce the choice set to two or three options followed by a compensatory strategy such as weighted adding, to select among the remaining options.
The Search for Dominance Structure
One very interesting decision models that appears to fit well with what customers frequently do is called the Search for Dominance Structure. The key idea is that a decision maker will attempt to structure and restructure information about attributes in such a way that one of the alternatives becomes the obvious choice… because that alternative dominates the other alternatives on key attributes. The to-be-chosen alternative has one or more clear advantages and all the major disadvantages of that alternative have been either neutralized or deemphasized. (See: Montgomery, Henry, “Decision Making and Action: The Search for a Dominance Structure” in the book, “The Construction of Preference“) This search for dominance appears to go through four phases:
- Pre-editing typically occurs early in the decision process. In this phase, the decision maker attempts to simplify the decision problem by selecting those alternatives and attributes that should be included in the representation of the decision situation. There is ample evidence that decision makers attempt to simplify the decision by focusing on a limited subset of attributes, by rounding off information about attribute levels, and by screening out alternatives that fall short on important attributes.
- Finding-a-promising alternative. In this phase, the decision maker finds a candidate for his or her final choice. An alternative that appears to be more attractive than other alternatives on an important attribute may be selected as a promising alternative. When a promising alternative has been found the decision maker has formed a preference, albeit a temporary one, for a particular alternative. Increasingly preferential attention is paid to this alternative. The question is whether the decision maker can justify a decision to choose this alternative. The question is dealt with in subsequent phases of the decision making process.
- Dominance testing. This implies that the decision maker tests whether the promising alternative dominates the other alternatives. These tests could be more or less systematic or exhaustive. If the promising alternative is found to be dominant, it is chosen and the decision process ends. The fact that the decision maker manages to increase the support for the chosen alternative does not necessarily mean that the alternative dominates its rivals.
- Dominance structuring. If a violation of dominance if found, the decision maker continues to this phase, in which he or she attempts to neutralize or counterbalance the disadvantage(s) found for the promising alternative. These attempts are based on various operations. There are four dominance structuring operations:
- The decision maker may deemphasize a disadvantage by arguing that the probability of the disadvantage is very low and that it could be controlled or avoided in one way or another.
- Another possibility is to bolster the advantages of the promising alternative and in this way indirectly deemphasize the disadvantages.
- In the cancellation operation the decision maker attempts to find dominance by canceling a disadvantage by relating it to an advantage that has some natural connection to the disadvantage in question.
- Finally, the decision make may find a dominance structure by collapsing two or more attributes into a new, more comprehensive attribute.
It is also possible that the decision maker may restructure the problem in a way that reframes existing alternatives or creates new alternatives. If the decision maker fails to find a dominance structure he or she may go back to the previous phase and make a new start in the search for dominance or he or she may postpone the decision, if possible. Decision makers continue differentiating and structuring after the decision. This is called consolidation and is important to reduce dissonance, disappointment, regret, etc…
There are numerous examples of how the customer experience can be tuned to match the way that customers’ actually make decisions. For example, one of our health insurance clients was able to significantly increase client retention by reducing the number of renewal options offered to each client from over 100 to 4. Previously, renewal packages sent to existing clients might be over 40 pages long. In the new design, each client was offered four basic options: same benefits as the current plan at a new price, same price for a minimal adjustment in current plan benefits, and two new plans at a marginal discount or increase compared to the current price. For any given client, these four options represented the most likely renewal choices the client would consider. As a result, the client received a one-page renewal summary that fit with their decision logic.
In summary, from a business perspective, the most critical elements of the customer experience involve the choices that customers make: the choice to buy; the choice to recommend; the choice to continue as a customer. This post provided several frameworks that can help companies understand how customers decide. As always, comments and suggestions are appreciated.
Filed under: Cognitive Ergonomics, Customer Experience, Neuroeconomics | Tagged: bounded rationality, choice overload, customer choice, customer co-creation, customer decision making, Customer Experience, customer heuristics, Neuroeconomics, preference matching, satisficing, search for dominance structure |