A firm must be able to support customers in identifying their own problems and solutions, while minimizing complexity and burden of choice. When a customer is exposed to too many choices, the cognitive cost of evaluation can easily outweigh the increased utility from having more choices, creating the “paradox of choice”: too many choices, reduce customer value, instead of increasing it.
The resulting syndrome has been called the “paradox of choice,” in which too many options can actually reduce customer value instead of increasing it. In such situations, customers might postpone their buying decisions and, worse, classify the vendor as difficult and undesirable. Recent research in marketing has addressed this issue in more detail and has found that the perceived cognitive cost is one of the highest hurdles towards a larger adoption of mass customization from the consumer perspective.
To avoid this, companies have to provide the means of choice navigation to simplify the ways in which people explore their offerings. This capability also relates to the design parameters of toolkits themselves, another rather open field of research for mass customization and user innovation alike. Design parameters of toolkits that provide choice navigation help functionality, process orientation, recommendation systems, or visualization features, but also community functionality or design libraries by other users.
The core method for navigating the customer’s choice in a mass customization system has been product configuration systems, also referred to as choice boards, design systems, toolkits, or co-design platforms. They are responsible for guiding the user through the elicitation process. Whenever the term configurator or configuration system is quoted in the literature, for the most part, it is used in a technical sense, usually addressing a software tool. The success of such an interaction system, however, is by no means defined solely by its technological capabilities but also by its integration into the sales environment, its ability to allow for learning, its ability to provide experience and process satisfaction, and its integration into the brand concept.
In a toolkit, different variants are represented, visualized, assessed, and priced with an accompanying learning-by-doing process for the user.
Tools for user integration in a mass customization system contain much more than arithmetic algorithms for combining modular components. Taking up an expression from Eric von Hippel, the more generic term “toolkits for customer co-design” might better describe the diverse activities taking place. In a toolkit, different variants are represented, visualized, assessed, and priced with an accompanying learning-by-doing process for the user. The core idea is to engage customers into fast-cycle, trial-and-error learning processes. Thanks to this mechanism, customers can engage in multiple sequential experiments to test the match between the available options and their needs.
Choice navigation, however, does not just refer to preventing “complexity of choice” and the negative effects of variety from the customers’ perspective. Offering choice to customers in a meaningful way, on the contrary, can become a way for new profit opportunities. Recent research has shown that up to 50% of the additional willingness to pay for customized (consumer) products can be explained by the positive perception of the co-design process itself. Product co-designs by customers may also provide symbolic (intrinsic and social) benefits, resulting from the actual process of co-design rather than its outcome. There is, for example, a pride-of-authorship effect. Customers may co-create something by themselves, which may add value due to the sheer enthusiasm about the result. In addition to enjoyment, participating in a co-design process may be considered a highly creative problem-solving process by the individuals engaged in this task, thus becoming a motivator to purchase a mass customization product.
An important precondition for customer satisfaction derived from co-design is that the process itself should be felicitous and successful.
An important precondition for customer satisfaction derived from co-design is that the process itself should be felicitous and successful. The customer has to be capable of performing the task. This competency issue involves flow, a construct often used by researchers to explain how customer participation in a process increases satisfaction. Flow is the process of optimal experience achieved when motivated users perceive a balance between their skills and the challenge at hand during an interaction process. Interacting with a co-design toolkit may lead exactly to this state, as recent research in marketing indicates. Accordingly, recent research has recommended several design parameters of a configurator that should facilitate this effect of process satisfaction.
The interaction between the manufacturer and the customer that is underlying a co-design process further offers possibilities for building loyalty and lasting customer relationships. Once a customer has successfully purchased an individual item, the knowledge acquired by the manufacturer represents a considerable barrier against any potential switching to other suppliers. Reordering becomes much easier for the customers.
When Adidas enters a learning relationship with its customers, it increases the revenues from each customer…
Consider, as an example, the case of Adidas, the large manufacturer of sports goods. In 2001, the company introduced its mass customization program ‘mi adidas’, offering custom sport shoes with regard to fit, functionality, and aesthetic design. The process starts with a customer who wants to buy personalized running shoes for around $150. The more customers tell the vendor about their likes and dislikes during the integration process, the better is the chance of a product being created that meets the customers’ exact needs at the first try. After delivery of the customized product, feedback from the customer enhances Adidas’ knowledge of that customer. The manufacturer can draw on detailed information about the customer for the next sale, ensuring that the service provided becomes quicker, simpler, and more focused. The information status is increased and more finely tuned with each additional sale. This data is also used to propose subsequent purchases automatically, once the life of the training shoes is over (for Adidas customers who exercise intensively, this can, in fact, be the case every few months).
When Adidas enters a learning relationship with its customers, it increases the revenues from each customer, because, in addition to the actual product benefits, it simplifies the purchasing decision, so that the customer keeps coming back. Why would a customer switch to a competitor – even one who could deliver a comparable customized product – if Adidas already has all the information necessary for supplying the product? A new supplier would need to repeat the initial process of gathering data from the customer. Moreover, the customer has now learned how self-integration into the process can successfully result in the creation of a product. By aggregating information from a segment of individual customers, Adidas also gains valuable market research knowledge. As a result, new products for the mass market segment can be planned more efficiently, and market research is more effective, because of unfiltered access to data on market trends and customers’ needs. This is of special benefit to those companies that unite large-scale make-to-stock production with tailored services. Mass customization can thus become an enabling strategy for higher efficiency of a mass production system.
By Frank Piller
Frank Piller is a chair professor of management and the director of the Technology & Innovation Management Group at RWTH Aachen University. He also is a founding faculty member and the co-director of the MIT Smart Customization Group at the Massachusetts Institute of Technology, USA. Frequently quoted in The New York Times, The Economist, and Business Week, amongst others, Frank is regarded as one of the leading experts on mass customization, personalization, and open innovation. Frank’s recent research focuses on innovation interfaces: How can organizations increase innovation success by designing and managing better interfaces within their organization and with external actors.
Introduction: A special series of articles on mass customization and customer co-design
Part 1: Competing in the Age of Mass Customization
Part 2: The market for mass customization today
Part 3: Solution Space Development: Understanding where customers are different
Part 4: Robust Process Design: Fulfilling individual customer needs without compromising performance
→ Part 5: Choice Navigation: Turning burden of choice into an experience
Part 6: Choice Navigation in Reality: A closer look into the Customization500
Part 7: Overcoming the Challenges of Implementing Mass Customization
Part 8: A Balanced View: Conclusions and Key Learnings