Imagine you move to a new city and after some time you decide to have your hair cut or get a new hairstyle. For some people, this would be a frightening thought. Based on your knowledge of the consequences of not finding a new hairdresser who is "as good as" the one you used to have, what would your options be in such a situation? You would probably turn to your local acquaintances to hear about their suggestions and experiences. Then, you could personally visit some of the suggested hair salons to see if they meet your expectations in styles and budget. That's a start. But wouldn't it be great if you could present your previous experiences to this new hair dresser, showing images and ratings of those experiences? After all, all of that knowledge you created in your previous visits to the hairdresser is now the most useful asset you could wish for in order to guide you into this new experience.
But the truth is, that knowledge is not easily available. Most certainly you will have to start from scratch. You'll have to test the available options here and there, until you're confident of having re-established your "knowledge base" in this new city.
That's how it is, but definitely not how it needs to be. The currently available technologies make it very simple to create a platform that could collect data from your experiences going to a hair salon in a very easy and rewarding way. The development of a system (e.g. an app, a website, etc.) that could collect key points on the agreement between your previous hair dressers and you would be reasonably simple to implement nowadays. For example, instead of doing a simple "mirror walk" at the end of the haircut, there could be a system that would privately collect pictures or videos of your final haircut and upload it to a platform (e.g., using a tablet connected to the internet), along with your name and the one from the hair dresser, making future service provisions much easier.
After that, you could rate the whole experience attributing levels of satisfaction for each of the service phases based on the perceived benefits you think you got from them. The hairdresser(s) could also add the experience they had with you to their profile, classifying it based on the type of hair, your personality and the purpose of the specific hair style.
The possibilities to add all sorts of information are too broad to be presented here, but (to mention a few) even the chemical products applied to your hair could be made explicit and then related to the final perception of benefit, including allergies and other unwanted reactions, that was registered by you and the service provider.
All of those possibilities—or better yet, those potential service innovations—are made clear if you look at them throughout the lens of a new "logic" to the service provisions: the Service-Dominant Logic.
Time to change your perspective to the service logic.
It is known that thousands of corporate managers all over the planet are faced with the challenge of creating services that are innovative enough not only to please customers but also to increase revenue. Unfortunately, until recently, these managers had very few tools to help them to face such endeavor. And even today, after decades of research, both service and innovation concepts are still a very complicated (and sometimes deceptive) subject. These are concepts that entail dramatic debates among academics and practitioners. Dealing with the challenges of harnessing innovation with business results, be it in a research study or at retail store, is daunting.
Most of the contemporary service blueprinting techniques available are based on the "line of visibility" chart, and have "time" as their main driver (Shostack 1984; Bitner et al. 2008). Nowadays it is not possible to define service as a "timely sequence of events" anymore. This is not what a service fundamentally is about. Tools that are structured based on pre-service, service and post-service periods, such as the Customer Journey Canvas (Stickdorn & Schneider 2010), or ones that focus on interpreting the present with lenses that are based on the past—like the Business Model Generation Canvas (Osterwalder & Pigneur 2010), which builds upon the old business plan fundamentals—do not capture the possibilities of a dematerialized and big-da-ta-revolutionized world.
What we mean by that is that their most fundamental characteristic is the fact that they were created under the "goods-dominant logic" paradigm, meaning that they use lenses that are fit for a product-based, industrial economy. The foundations of those tools were created before the work of Professors Robert Lusch and Steve Vargo proposing the Service-Dominant Logic in 2004 (Vargo & Lusch 2004). Just to mention one of the several new concepts proposed by SDL, it defines service as the application of intangible assets for the benefit of oneself or others. This definition has equipped us with a new understanding of what a service is, and most importantly, what it can be. In an era where personal data is becoming ubiquitous, to be able to understand and document a service as an application of intangible assets is a revolutionary thing.
In the beginning of 2011, after an exchange of ideas with Professor Vargo about the interesting possibilities presented by the application of design and knowledge management techniques on the challenges posed by the management of services, we accepted the challenge of proposing a new framework based on the service-dominant logic (SDL). So, the main goal was to develop an academically-valid framework to create human-centered services with continuously innovative provision processes, starting with the service-dominant logic as the foundational premise. In order to so, we started to develop and test several combinations of concepts coming from the design and the knowledge management field. After two years of research (although it still cannot be considered a final version), it is already possible to see that the ideas we discussed back in 2011 are effective and consistent.
Through this two-year work, it was possible to research and present an interdisciplinary theoretical foundation to make sense of the service innovation process. As a result of this effort of combining different knowledge areas, we proposed several modifications to the "line of visibility" technique. In total, the proposed framework creates 20 different contexts in which service can be designed and evaluated for possible improvement. It was named as SECI+Design Logic 20/20 (SDL-20/20), because of its foundational premises and also due to the fact of offering 20 specific contexts through which a service can be designed and improved. As illustrated in Figure 1, it encompasses service design contexts ranging from (a) designing socialization opportunities for clients as to create knowledge in the discovery phase to (b) designing homogeneous support processes at the delivery phase.
Figure 1 - A human-centered service blueprint: A Hair Salon illustration
The purpose of this text, as illustrated in Figure 1, is to offer a simplified application of the framework. The context in which we've chosen to demonstrate this framework is a Hair Salon. This context is not as simple as it might appear. After all, when it comes to human vanity, there is nothing that can be said to be simple. The diagram above presents the main elements of the framework and serves to demonstrate the application of the resulting framework on the blueprint of an actual service, "as built." This figure mainly shows the "knowledge flows" that are present during a hair-cutting session in a hair salon. The following text summarizes the main components of the SDL-20/20 framework as it was applied on the aforementioned service context (a more detailed description of the framework can be found here).
The complexity of a service can hardly ever be detailed in its totality by a graphical representation. But relying on oversimplistic models can hinder designers' efforts to explore all the possible potential novelties. The SDL-20/20 framework offers a better balance between complexity and simplicity, helping organizations understand and commit to act towards service innovation.
As you can see from the proposed SDL-20/20 framework for service blueprinting shown in Figure 2 and illustrated in Figure 1, the vertical and horizontal axis from the traditional technique were modified and zones were created within the chart. By expanding the rubric from the five contexts proposed by the usual service blueprint techniques to 20 different ones, it more closely reflects the real service experience.
Figure 2 - SDL-20/20 chart: its layers and components
The application of this framework at a hairdressing service shows several opportunities for innovation, which become especially apparent at the Explicit Knowledge swim-lane (see the red box on Figure 1). Beyond merely highlighting the lack of solutions on providing support to the knowledge creation processes that happen during the service provision (see the blue and orange boxes on the Customer and the Provider Knowledge swim-lanes), it also points to opportunities for innovation on designing processes and systems to capture (Explicit Knowledge) the experience that both the client and the hair dresser have during the service provision.
Based on the proposed framework, designing processes and systems at the Explicit Knowledge swim-lane would configure a great opportunity for innovation (see the cited red box). Generally, clients choose a hair salon based on their previous experiences. It means that the discovery process happens during the customer previous social contacts, resulting in perceptions of fashion trends and preferred hair styles.
After a customer discovers how s/he would like to have their hair done and in which Hair Salon, s/he gets in touch with the provider and schedules an appointment or simply goes to the salon. Most of the time, very limited explicit knowledge is available to either the customer or the hairdresser about one another. The icons expressed on this part of the canvas represent this customer access to a limited amount of informational touch points like advertisements, the salon's website and, perhaps, prizes and institutional recognitions. On the other hand, the hairdresser usually also has little access to any explicit knowledge about the intended customer.
The definition phase starts when the customer has identified which hair dresser will provide the service and ends when they agree on how the hair will be dressed. During that time, and based on fashion magazines, personal pictures and hair style catalogues (all of them are forms of explicit knowledge), both discuss hair style preferences and the customer's hair possibilities to be dressed until they reach a common agreement. In a conventional situation, no knowledge will be made explicit about this agreement.
The development process of hair washing and cutting starts. Several discussions happen during that time, all aimed at reaffirming the agreements made up to that point or possibly defining new ones, based on the service evolution. Nothing will be recorded from this experience except for the memories of the people.
At the end, the hair dresser does the "mirror walk" around the customer's head to get the final agreement on the service that was provided. The customer, perceiving that the intended benefits were attained, proceeds to pay and leaves the salon. Once more, nothing will be made explicit in a way that it could serve as a starting point for the next hairdressing service.
SDL-20/20 and Explicit Knowledge
As it can be seen in the proposed chart (Figure 3), the Socialization and Internalization phases may take place in different locations, due to the fact that interactions between customer and provider can cross various kinds of boundaries, i.e. through mobiles, Internet, advertising, customer-customer interactions, etc. This is one of the reasons why there are no lines of "visibility," "interaction" or "internal interaction." So, the "physical evidence" of the previous blueprinting techniques was redefined and renamed Explicit Knowledge. This concept allows us to encompass a large range of service evidences that are not always "physical," in the sense that they have no tangible form, but can still be stored or transmitted, like a perception about the service experience for example.
The framework presented here, in a very simplified way, is based on an interdisciplinary theoretical foundation and is aimed at making sense of the service innovation process under the lights of the service-dominant logic and the new digital age. As a result of this effort of combining different knowledge-bases, several modifications were proposed to the old "line of visibility" technique, which is still the base for most of the service blueprinting techniques used by organizations at the present moment.
After the advent of the service-dominant logic, these previous frameworks (Shostack 1984; Bitner et al. 2008) can be considered outdated. A new model needed to be created to take advantage of the possibilities offered by the actual service-oriented economy. Thus, we believe that the SDL-20/20 framework has the potential to crystallize the radical innovative perspective proposed by the Service Dominant Logic into new service propositions, enabling service managers all around the world to tap into the incredible amount of innovative energy.
Figure 3 - SECI+Design Logic Framework
Back to the Hair Salon
What is left is that documenting the physical flows of people and things when dealing with service innovation is not enough. What SDL and the BIG DATA revolutions clearly show, is that we need also to be able to understand the flow of intangible assets, and especially the "knowledge flows" that happen in the background during the journey of the users throughout a service.
When you experience a haircut or any other service, the most important thing that both you and your provider can get at the end is the knowledge that both co-created during that experience. It is this knowledge that will or will not bring you back to that service provider. It is this knowledge that will support your decision to pay more or to pay less every time you use a particular service.
Being able not only to externalize this knowledge, but enable the most effective knowledge creation process (socialize, externalize, combine and internalize) needs to be the ultimate goal of innovative service organizations.
Still, today, whenever you leave a hair salon, no matter how happy you are, the real value—the knowledge that was co-created—is usually left hanging somewhere carelessly. What made you "look good" after a haircut, or what makes a service organization "looks good" after a service provision, is, precisely, what cannot be "seen." So visibility lines will be of no use to you on this matter.
The SDL-20/20 framework was developed exactly to enable organizations to make sense of those fundamental knowledge flows—to see and effectively act upon them.
This article was written by Tennyson Pinheiro and Mauricio Manhaes. Most of the concepts and ideas that were used to support these arguments are based on an extensive literature review based on the doctoral thesis research of Mauricio Manhaes on Engineering and Knowledge Management at the Federal University of Santa Catarina - Brazil in cooperation with the Köln International School of Design / University of Applied Sciences in Cologne - Germany. This research is supported by CAPES foundation, an agency under the Ministry of Education of Brazil, and SEDES - Center for Service Design Research, Cologne, Germany.
Special thanks to Andre Vicente, visual strategist at Livework Brazil.
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