Research scope and research question

To define the scope of your research, it might help to consider which of the following options is applicable for the project.

Research planning

When planning your research, you should think of research methods that are most likely to give you fruitful answers for your proposed research questions. On the other hand, your research must fit within certain business constraints, as you always have to consider how to best allocate time, money, and people within a project.

A good starting point is to take a look at what is already out there so you are “standing on the shoulders of giants.” Take a look at previous research and existing data on the subject matter. Ask the Market Research or Innovation department if they have any relevant data, get statistics that might be useful for your research, and get an overview of what has been done in this field by other departments in the organization. Besides existing research within the organization, also invest some time into classic desk research: use (academic) research platforms to look for scholarly papers published on that topic. In many cases, you might invest an hour or so to screen the research landscape until you have a good idea whether it is worth spending more time reading published research. If you find interesting research, it will save some time and budget by focusing your research on the really useful parts.

Each loop should include data collection with various methods as well as at least a simple form of data synthesis and analysis.

Besides conducting some preparatory research, your research planning will include decisions regarding research loops, sample selection, research context, sample size, and method selection. ⁸

Data collection

There are a huge variety of research methods you can use to collect meaningful data in service design. We use quantitative methods like surveys (offline and online), any form of automated statistics (e.g., conversion rate analysis), and manually collected quantitative data (e.g., frequency of shop visitors through simple counting). However, we mostly use qualitative methods and particularly methods based on ethnography. Select and line up a sequence of research methods to collect and to visualize, synthesize, and analyze your research data. See this section and 5.1.4 for details.

Method triangulation

When we are selecting the “right” research methods – ones that yield a lot of useful data – we always face the question of how many research methods we should use. Given a limited budget, researchers often need to decide whether they should put their budget into one method and do this rather well, or distribute their budget to use a variety of research methods. We always suggest the latter, based on the concept of method triangulation. ¹² Triangulation is based on classic navigation and land surveying techniques. In simple terms, with triangulation you can estimate your own position by measuring the directions of at least two distinct landmarks. Based on basic principles of geometry, the more landmarks we measure, the more accurately the position can be calculated. Similarly, researchers can improve the accuracy and richness of their research by using different methods to collect data on the same phenomenon.

If different methods lead to the same outcome, you can be more confident about these findings. However, in design research, triangulation is not used as much to seek validation or verification of research results, but rather to ensure that insights are based on a rich and comprehensive dataset that is robust enough to provide a foundation for design decisions. In particular, when you do exploratory research to get inspiration for new ideas, richness of data and perspectives is key.

In a research context the fundamental idea of method triangulation is to cross-check findings with different methods.

Data triangulation

Different research methods generate different types of data as output, such as text (e.g., field notes or interview transcripts), photos, videos, artifacts (e.g., tickets or info flyers), as well as statistics. Some methods can generate different or even multiple types of data, and researchers should plan what kind of data they need. Again, following the principle of triangulation, you should strive to create different types of data in your research process.

Data triangulation enables researchers to support findings with different underpinnings and makes your dataset richer and more comprehensible. One advantage of using different types of data can be explained with a simple example. Imagine the situation of a contextual interview. If researchers only take field notes, they only write down what they regard as important. If they add photos of the situation, others can understand the situational context of the interview. If they do an audio recording of the interview and transcribe it afterwards, others have a chance to interpret the interview as well. If they take a video of the situation, others can also interpret body language and situational context. Different data types can help you to get a richer dataset and reduce the subjectivity of researchers.

Another differentiation is the distinction between primary and secondary data. Primary data is data collected by a researcher for a specific purpose. Secondary data is data that has been collected by someone else for other purposes, but is being used by a researcher for a new purpose. If your organization will be conducting several service design projects, it makes sense to integrate a data or knowledge management system. The outcomes from primary research in one project can serve as secondary data for another project and thereby save time and money. Researchers can build on the outcomes of prior research (i.e., secondary data) and so might be able to use a given research budget for more focused primary research, “standing on the shoulders of giants.” ¹³

Figure 5-2. —Use different types of triangulation to offset different forms of research bias:
—Researcher triangulation to avoid prejudices and predispositions.
—Method triangulation to get different perspectives on the same subject matter.
—Data triangulation to get a richer and more comprehensive dataset.

Expert Tip

“Providing sufficient raw data speeds up the decision-making process and helps avoid endless discussions within your team and with superiors. Instead of [questions] like ‘Is this really like this?’ our team accept a fact when they see it with their own eyes – or even better, when they experience it themselves. They then move on and work on actionable solutions instead of pointless discussions.”

— Anke Helmbrecht

The differentiation between first- and second-order concepts (not the same as primary and secondary data) is an important factor which affects how other researchers can work with the data. Basically, first-order concepts are the “raw data” (e.g., the direct transcript of an interview), while second-order concepts include interpretations by researchers. In practice, first-order concepts might be any original evidence of research that researchers gain from observations or interviews – for example, interview transcripts, but also photos or videos. Second-order concepts might be summarized field notes or any other form of data filtered or biased by a researcher striving to identify patterns. ¹⁴ While both are useful, it is important to collect enough raw data (first-order concepts) that does not include interpretations by researchers, as only this raw data can be interpreted by other researchers at a later stage. Recorded data like photos or videos preserves to a large extent the raw content, which is often more convincing when presenting your results than any second-order concept. When you can do only field notes, distinguish between an accurate description of a situation studied and your personal interpretation, so that you can always go back and refine your interpretation.

Data visualization, synthesis, and analysis

There are many ways to synthesize and analyze data (also known as sensemaking) in design research. In general, we can see two major ways to do this: the academic way and the practitioner’s way. Although we’ll focus mostly on the practitioner’s approach, we can learn a lot from academic approaches.

The academic way to work with qualitative data mostly includes codifying data and then searching for patterns within this codified data. This process is often referred to as content analysis – there are various methods and even software ¹⁶ to support researchers in doing this, but of course pen and paper will often also do the job. For example, a researcher could codify the data by going through transcribed interviews and tagging sentences with specific labels. In a second step, the researcher then counts how often specific labels occur – or rather a software tool does this automatically. ¹⁷ This process takes some time, but is particularly useful when you have to cope with a huge amount of data because it lets you split up a set of data and work your way through it step by step. This happens often when you plan research design with a classic project management approach: one period of data collection followed by one period of data analysis.

However, if you follow an iterative research design with a rather visual synthesis and analysis process, you won’t be drowning in data as you will have multiple iterations of data collection, synthesis, and analysis.

Codifying data

One major question when you’re codifying data ²⁰ is this: where do the categories for these codes come from? Mostly, qualitative research is understood to follow an inductive approach in which researchers “immerse” themselves in the data and generate categories and insights from the data itself. On the other hand, sometimes researchers follow a deductive approach and start with a defined set of categories derived from literature or other research, but then eliminate or add categories through their data synthesis. Deductive qualitative analysis strives to test specific assumptions or concepts and therefore often categorizes data in previously defined categories. While design research to identify customer experience gaps or people’s needs is mostly inductive, research to test prototypes is often rather deductive.

Figure 5-3. Indexing your dataset allows you to trace back insights (and later even ideas and prototypes) to their underlying data.

Using research outcomes

The outcome of service design research usually serves as input for other service design activities, such as ideation or prototyping. In some cases, research output can even be used directly in implementation activities. For example, research can sometimes reveal simple usability improvements for a piece of software that can be described as user stories and directly implemented during software development. ²¹ The usual case, however, is that topics identified during the research process are visualized with personas, journey maps, system maps, key insights, jobs to be done, user stories, or research reports. These can be used to identify problems or opportunities in subsequent ideation activities. ²² If you already have some immediate ideas of how to solve discovered issues, you might also directly try to prototype these in prototyping activities. ²³

Typically, during research activities, you explore in detail the existing problems of an experience, a process, or a system. You try to question what you see and challenge your assumptions. At least, this is what you should do. Unfortunately, one problem is that we as humans are trained to solve problems. When we see a problem, we immediately start thinking about potential solutions.

This is unfortunate, as these are probably not solutions for the root cause. To be able to work on that, you need to invest more effort into understanding the problem. You need to remain longer in the problem space, without jumping into the solution space too soon.

Sometimes it helps a design team if they clearly understand where they are. Are you in the problem space trying to understand someone’s problem in more detail and depth, exploring this from various perspectives and trying to dig deeper and discover the root cause of it? Or are you in the solution space striving to find ideas on how to solve a well-defined problem? You can make this clear using visual clues such as a poster on the wall wherever your design team works, by clearly articulating where you are in your meetings and workshops.

Of course, a design team will have many ideas during research activities and it would be a shame to lose them. If ideas come up during research, capture them on an idea wall ²⁴ and then let go.

Or, you might want to see the idea as a hypothesis and rephrase it into its underlying assumptions – this feeds into your next research iteration as a new research question. Also, you might want to brief your team before the next steps of the design process, such as ideation activities that might follow your research.

Desk Research Preparatory Research

Your own preparation before you start your actual research or fieldwork. ²⁶

Preparatory (or simply “prep”) research often includes digging deeper into an industry, an organization, competitors, or similar products, and also the client’s perspective of what the research problem is, their context, perceptions, internal conflicts or interplays, and so on. Prep research is less about finding answers, and more about finding the right questions to ask in your research. Prep research can result in a summary of text snippets and/or a collection of photos, screenshots, or videos, perhaps visualized as a mood board.

Preparation: Often prep research starts with very wide research questions or topics, from soft topics such as “How does home feel?” to rather specific topics such as “Where else is this technology used?”

Use: Prep research can include conducting internal interviews; screening social media; listening to podcasts, online videos, or conference talks; and reading industry-specific scientific or special-interest publications along with newspapers or general-interest magazines.

Expected output: Text, statistics, photos, and videos, as well as mind maps, mood boards, and the like

Desk Research Secondary Research

The collection, synthesis, and summary of existing research. ²⁷

In contrast to primary research, secondary research (often also simply called “desk research”) uses only existing secondary data – information collected for other projects or purposes. The main idea is to check whether research regarding a topic already exists. This helps us to formulate a research question more precisely and identify promising methods of data collection, visualization, and synthesis. Desk research should always be the starting point of a research process, simply to avoid reinventing the wheel and to stand on the shoulders of giants when you start your primary research.

Preparation: Collect a list of potentially promising internal and/or external sources such as academic papers, white papers, and reports, as well as experts on your research topic.

Use: Search for qualitative and quantitative secondary data regarding your research topic using online search engines, scientific databases and journals, libraries, conferences, and expert talks and interviews.

Expected output: Text, statistics, mind maps and the like

Self-Ethnographic Approach Autoethnography

Researchers explore a particular experience themselves and self-document this using field notes, audio recordings, videos, and photographs; ²⁸ also called self-ethnography/documentation.

Besides “real” (i.e., rather academic) autoethnographic research where researchers immerse themselves for months within an organization, service design often applies shorter versions of this: team members explore a particular experience themselves in the real situational context, mostly as customers or as employees. Variants of this include mystery shopping, mystery working, service safaris, explorative service safaris, or diary studies.

Preparation: Autoethnography is often one of the first research methods undertaken as it helps researchers to interpret behaviors when they conduct interviews or observations. Decide when and where you will conduct your research.

Use: Autoethnography can address one or more channels as well as actions with or without other people and/or machines. If you take field notes, write up first-level (“raw data”) and second-level concepts (“interpretations”) separately: for example, what you see and hear on the left page and what you interpret from this or how it feels on the right.

Expected output: Text (transcripts, field notes), audio recordings, photos, videos, artifacts

Self-Ethnographic Approach Online ethnography

An approach to investigate how people interact with one another in online communities, ²⁹ also known as virtual or cyber ethnography.

Online ethnography can be done as self-ethnographic research, non-participant ethnography, or participant ethnography – however, it always focuses on online experiences. It can look at many different aspects, such as social interactions within an online community or the differences between the self-perception of people when they are online and in real life.

Preparation: Based on your research question, define which online communities might be suitable for your research question. Decide how you will document your experiences; e.g., through screenshots or screencasts, system or journey maps, or simply field notes.

Use: Often online ethnographies include a mix of methods, such as observations, contextual interviews conducted online with screen sharing, or in-depth retrospective interviews with other community members.

Expected output: Text (quotes, transcripts, field notes), screenshots, recordings (screencasts or audio recordings)

Participant Approach Participant observation

Researchers immerse themselves in the lives of research participants.

Participant observation is an umbrella term for a variety of methods, such as shadowing, a day in the life, or work-along. ³⁰

Preparation: Based on your research question, select suitable interviewees and plan when and where you will conduct your research and how you will document it. How will you approach your participants? How will you start and end? How will you manage the “observer effect”? How much time will you plan for it?

Use: Observations might be at the participant’s workplace, in their home, or even following them throughout a process like a holiday trip. Use the situational context and ask participants to explain specific activities, artifacts, behavior, motivations, needs, pains, or gains. Sometimes contradictions between what people say and what people do can be very revealing if you mirror behavior back to participants. During participant observations it is important to observe not only what people are doing (by interpreting their body language and gestures) but also what they are not doing.

Expected output: Text (transcripts, field notes), audio recordings, photos, videos, artifacts

1. When researchers conduct participant observations, they often switch between more passively observing situations and actively asking questions to get a deeper understanding of user needs.

   

2. Contextual interviews help interviewees to articulate problems and needs as they are in the situational context, and they can simply show things right where they are.

   

3. During contextual interviews or observations, take audio or video recordings to enable data triangulation, if possible.

   

Participant Approach Contextual interview

Interviews conducted with customers, employees, or any other relevant stakeholders in a situational context relevant to the research question, ³¹ also known as contextual inquiry.

Contextual interviews are used to understand a certain group of people better (their needs, emotions, expectations, and environment – useful for personas), to reveal formal and informal networks and hidden agendas of specific actors (useful for system maps), or to understand particular experiences (useful for journey maps). Contextual interviews can be done, for example, with employees at their workplace or with customers during a specific moment of the customer experience.

Preparation: In contrast to studio interviews, contextual ones are conducted “in situational context,” so that researchers can observe the surroundings and interviewees can point to elements in the environment. Based on your research question, define who, when, and where you will interview and how you will document the situational context – including the interviewee’s mood, gestures, and body language.

Use: Try to ask your interviewees to demonstrate details of the concrete experience of interest. It is often easier for people to articulate their motivations and experience when they can refer to concrete examples.

Expected output: Text (transcripts, field notes), audio recordings, photos, videos, artifacts

1. Pay attention to your interviewees’ body language and gestures and write down interesting observations. This often leads to further questions.

   

2. Try to differentiate between concrete observations and your own interpretations (first-level/second-level concepts).

   

Participant Approach In-depth interview

A qualitative research technique of conducting intensive individual interviews.

In-depth interviews are often conducted with relevant stakeholders or external experts to understand different perspectives on a specific subject. These interviews can help researchers learn more about particular expectations, experiences, products, services, goods, operations, processes, and concerns, and also about a person’s attitude, problems, needs, ideas, or environment.

Preparation: In-depth interviews are mostly done in a semistructured way to collect useful data. For example, interview guidelines can be based on an empathy map. ³² In-depth interviews are mostly done face to face, allowing researchers to observe body language and create a more intimate atmosphere. They can also be conducted online or by telephone.

Use: These interviews can be supported by co-creating boundary objects, such as scribbles or mind maps, or using personas, journey maps, system maps, or other useful templates. They can also include tasks like card sorting to understand user needs or storytelling supported by tangible touchpoint cards to visualize experiences.

Expected output: Text (transcripts, field notes), audio recordings, photos, videos, artifacts

Participant Approach Focus groups

A classic qualitative interview research method in which a researcher invites a group of people and asks them questions on specific products, services, goods, concepts, problems, prototypes, advertisements, etc.

With a focus group, researchers strive to understand the perceptions, opinions, ideas, or attitudes toward a given topic. Although focus groups are often used in business, they have only limited applicability in service design. They typically lack the situational context and usually do not co-create boundary objects, such as personas or journey/system maps. This often leads to limited informative value as results depend solely on the moderated discussion and are biased by issues like observer effect, group think, social desirability bias, etc. ³³

Preparation: Focus groups are mostly carried out in a rather informal setting, like a meeting room or a special room where researchers observe the situation through a one-way mirror.

Use: Researchers often ask only an initial question and then observe the group discussion and dynamics. Sometimes a researcher acts as a moderator guiding the group through a set of questions.

Expected output: Text (transcripts, notes), audio recordings, photos, videos

Non-Participant Approach Non-participant observation

Researchers collect data by observing behavior without actively interacting with the participants.

In non-participant observation, researchers do not interact with research participants; they behave like a “fly on the wall.” Research subjects are often customers, employees, or other stakeholders, observed in situations that are relevant to the research question. Often, non-participant observation is used to level out researcher biases in other methods and to reveal differences between what people say and what they actually do.

Preparation: Plan who will do the research, when, where, and with whom. Sometimes when researchers do covert non-participant observation they pretend to be customers or passers-by, or even use one-way mirrors, minimizing the risk of the “observer effect.” ³⁴

Use: During non-participant observations, it is important to observe not only what people are doing (for example by interpreting their body language and gestures), but also what people are not doing (perhaps ignoring instructions or refraining from asking for help or assistance).

Expected output: Text (field notes), photos, videos, audio recordings, sketches, artifacts, statistics (e.g., counting customers per hour)

Non-Participant Approach Mobile ethnography

Aggregated multiple self-ethnographies, taking place in a guided research setting where data is collected with mobile devices such as smartphones. ³⁵

A mobile ethnography research project can include anywhere from a handful to thousands of participants. Usually users, customers, or employees are included as participants, self-documenting their own experiences on their own smartphones with text, photos, videos, or quantitative evaluations, as well as date, time, and location. Researchers can review, synthesize, analyze, and export the collected data in real time.

Preparation: Plan to offer incentives for your participants (recruiting is often the hardest part!). In your invitation, provide clear and short instructions on how to join the project and how to document. Define questions for your participant profile so that you can cluster participants into groups matching your personas.

Use: Mobile ethnography works well for longer research over one or a few days. Once you have started your data collection, you can start to synthesize and analyze.

Expected output: Text, photos, videos, audio recordings, date and time information, geolocation data, statistics of participant profiles

Non-Participant Approach Cultural probes

Selected research participants collect packages of information based on specific tasks given by researchers. ³⁶

In cultural probes, research participants are asked to self-document certain experiences with field notes and photos, and/or to collect relevant artifacts. Cultural probes are often also done virtually using online diary platforms or mobile ethnography apps.

Preparation: Prepare and send a package to participants, which might include a set of instructions, a notebook, and a single-use camera. You might want to prepare a simple script for participants to follow, or instruct them to take photos of how they use specific products in various contexts.

Use: The aim of cultural probes is to gain unbiased data that has been collected by participants themselves in context, without having a researcher present. They help researchers to understand and overcome cultural boundaries and bring diverse perspectives into a design process. Cultural probes are often a mix of various approaches and may be combined with in-depth interviews to review the collected data retrospectively. They can include diaries kept over a day, a week, or even several years.

Expected output: Text (self-documented notes, diaries), photos, videos, audio recordings, artifacts

1. Participants use a mobile ethnography app on their smartphones to report on and evaluate their experiences step by step. Researchers see the data in real time and can start analyzing it immediately. ³⁷

   

2. The content of a cultural probe (the observation package) to research flight travel experiences. ³⁸

   

3. Even though age and gender is always an easy start for a persona, demographics might be quite misleading. Instead, think of factors that differentiate the groups you would like to represent with your personas.

   

Co-Creative Workshop Co-creating personas

Using the know-how of a group of invited participants to create a set of personas.

The quality of results of a co-creative persona workshop depends on the research data you bring to the workshop and on how much participants know about the group of people you want to exemplify with personas – for example, a workshop with frontline employees is often quite useful to create personas of customers. For less biased results, avoid inviting only people with abstract knowledge of the subject matter. The results might look convincing, but often they are very biased.

Preparation: Invite and incentivize your workshop participants and describe the aim of the workshop. Select participants with in-depth knowledge of the stakeholder group you are creating the persona for. Write a facilitation agenda to create a safe space during the workshop. ³⁹

Use: These workshops often follow a structure similar to this: welcome and split into smaller groups, create initial personas, present and cluster, discuss and merge, visualize and validate, iterate.

Expected output: Drafts of personas (physical or digital), workshop photos, quotes of participants (audio or text), videos of workshop progress

Co-Creative Workshop Co-creating journey maps

Using the know-how of a group of invited participants to create one or more journey maps or service blueprints.

Invite participants who have solid knowledge about the experience you are mapping. If you want to create a journey map about customer experiences, this might be customers (yes, real ones!) and/or frontline employees. The outcomes of co-creative workshops are often assumption-based. The results might look convincing, but often they are biased. These outcomes should be understood as tools in development, as a common starting point to design the research process, or to evaluate and enhance collected data.

Preparation: Think about inviting workshop participants with either a shared perspective (such as customers of a particular target group) or from differing perspectives (such as customers of various target groups or customers and employees). Clearly communicate the scope of the journey map, such as a high-level journey map vs. a more detailed map of one specific situation within a high-level journey map.

Use: Define your main actor and journey scope, welcome and split into smaller groups, identify stages and steps, iterate and refine, add perspectives such as the emotional journey (optional), discuss and merge, iterate.

Expected output: Drafts of journey maps (physical or digital), workshop photos, quotes of participants (audio or text), videos of workshop progress

Co-Creative Workshop Co-creating system maps

Using the know-how of a group of invited participants to create system maps. ⁴⁰

For each system map, define a specific perspective and invite participants with a sound knowledge of this. With your decision on who to invite and who to leave out, you also decide which perspectives might be interesting enough to include. Constantly challenge your assumptions with solid research. Over time, assumption-based maps should develop into research-based ones.

Preparation: In addition to the know-how of the workshop participants, a second important factor is the qualitative research you do beforehand and bring to the workshop, for example through a research wall.

Use: Setting a clear scope and situational context helps workshop participants to get on the same page. Often these workshops follow a structure like this: welcome and split into smaller groups, create initial stakeholder maps (1. list stakeholders, 2. prioritize stakeholders, 3. visualize stakeholders on map, 4. illustrate relationships between stakeholders), present and compare, discuss and merge, iterate and validate, test different scenarios within the ecosystem (optional).

Expected output: Drafts of system maps (physical or digital), workshop photos, quotes of participants (audio or text), videos of workshop progress

Building a research wall

Synthesizing and analyzing research data through a visual arrangement of research data on a wall. ⁴¹

You can imagine a research wall as a more complex version of how detectives structure their crime scene data in many thrillers (think of any CSI episode). You’ll find many types of data on these walls (quotes, photos, screenshots of websites or videos, statistics, artifacts, etc.). This enables you to identify patterns within your data, while also providing a place to share your research with others as it develops.

Preparation: Prepare a wall space or large cardboard sheets to hang up your research data. Also, think about who should join you to create a research wall.

Use: Hang the material on the wall and start synthesizing data by clustering it according to specific topics, like certain customer segments, common problems, steps along the journey map, etc. Name these clusters and look for connections between clusters as well as connections between single materials (be aware of a potential confirmation bias). The various patterns you identify can then be further explored with tools like personas, journey maps, system maps, key insights, and so on – all of which also become part of the research wall.

Expected output: A visual arrangement of research data

Creating personas

Creating a rich description of a specific fictional person as an archetype exemplifying a group of people, such as a group of customers, users, or employees. ⁴²

Personas focus on particular types of customer motivations and behaviors, and help to achieve empathy with a group of people to create solutions that address real problems. You can create them for existing market segments or to challenge an existing segmentation.

Preparation: Persona templates or empathy maps are sometimes helpful when creating personas. You often mix different approaches – for example, starting with assumption-based personas developed during a co-creative workshop with frontline staff, then enriching and backing these with research.

Use: Create approximately three to seven core personas representing your main market segments. Following the principle of “design for the average – test with extremes,” create many more “edge-of-the-curve” personas to test ideas and prototypes with people from more extreme ends of your user spectrum.

Expected output: Personas

1. Using foam boards as research walls helps a research team to keep research data (such as quotes, photos, screenshots, artifacts, etc.) with them when they have to move between rooms.

   

2. Structure your research wall by clustering and adding headings to the different sections.

   

3. Starting personas with demographics, like age, gender, nationality, job, and so on, carries the risk of stereotyping. Instead, try to build your personas from your research, perhaps starting with behavioral patterns you find within your data.

   

Mapping journeys

Visualizing specific experiences of a main actor, often exemplified by a persona, over time.

Journey maps can visualize ⁴³ existing experiences (current-state journey maps) or planned experiences (future-state journey maps). The basic structure of a journey map consists of steps and stages defining the scale of the visualized experience, from a high-level journey map that shows an end-to-end experience to a detailed journey map showing only a few minutes.

Preparation: Even though assumption-based journey maps are relatively easy and fast to do, they can be very misleading. If you start with assumption-based journey maps, constantly challenge your assumptions. Over time, assumption-based journey maps should develop into research-based ones with a solid foundation on research data (beware of confirmation bias). ⁴⁴

Use: Often a process to create a journey map looks like this: prepare and print out data, choose a main actor (persona), define scale and scope, create steps, iterate and refine, add lanes.

Expected output: Journey maps

1. A journey map visualizing two different scales of daily and weekly user activities. The map includes a sketched storyboard, an emotional journey, and user needs. ⁴⁵

   

2. Journey mapping software helps you to quickly create professional journey maps with dispersed teams. ⁴⁶

   

3. System maps are often hard to understand for people outside of your core team. Reduce them to the most important facts when you use them for communication.

   

4. Using templates or a specific structure helps to develop key insights, but constantly ask yourself if every aspect of your insight is specific and clear enough and if it is backed by sufficient research data.

   

Mapping systems

Visualizing the ecosystem around services and physical or digital products.

“System maps” is an umbrella term for different visualizations, such as stakeholder maps, value network maps, or ecosystem maps. All of these can be created from various perspectives. A system looks different from a customer’s perspective compared with a business internal perspective. System maps have obvious relationships to other tools in service design, such as personas and journey maps. ⁴⁷

Preparation: As system maps can become very messy, it is important to define a clear focus for a map. Don’t try to visualize every stakeholder you can think of on the same stakeholder map; it’s more useful to make various maps for different purposes. System maps are an excellent tool to synthesize research data, so it is useful to prepare research data beforehand. Remember that research is iterative, and it makes sense to use these maps to find gaps in your research, which you can investigate in later research iterations.

Use: Often, creating system maps looks like this: prepare and print out data, collect stakeholders, prioritize stakeholders, visualize stakeholders on map, illustrate relationships between stakeholders (optional), find gaps and iterate.

Expected output: System maps

Developing key insights

Summarizing main findings in a concise and actionable format for communication within and across project teams. ⁴⁸

Key insights are built on research and supported by raw data. They often include a situational context, and an intended outcome, as well as a restriction, obstacle, or friction. First insights are often generated based on patterns you find while you are collecting data, building your research wall, or codifying your data. If you don’t have enough data to critically reflect on an assumption, collect more data. Design research is iterative!

Preparation: There are many ways (and templates) to formulate insights, such as: … [actor] wants to … [action] because … [motivation], but … [tension]. It helps to write down initial ideas for insights at any stage of the research process and then critically reflect on them using your research data.

Use: Often, key insights are developed from initial assumptions, hypotheses, and intermediate insights (try to avoid confirmation bias). The process often goes like this: prepare and print out data; write initial insights; cluster, merge, and prioritize; link key insights to data; find gaps and iterate. Key insights should be carefully phrased, as they will serve as points of reference for the further design process.

Expected output: Several key insights

Generating jobs-to-be-done insights

Summarizing the bigger picture of what customers want to achieve when they use certain services or physical/digital products.

Jobs to be done (JTBD) is a specific way to formulate insights based on a framework by Clayton Christensen. ⁴⁹ JTBD describes what a product helps the customer to achieve. It can be formulated for an entire physical/digital product or service (the main aim behind a journey map). Alternatively, formulate it for certain steps within a journey map by asking yourself what a customer or user wants to get done and adding your discoveries as an additional lane on a journey map. JTBD can help a team to break away from a current solution and discover new solutions based on what customers really want to achieve.

Preparation: Often, JTBD are developed based on this structure: When … [situation], I want to … [motivation or forces], so I can … [expected outcome]. To phrase JTBD, prepare and print out research data, personas, and journey maps.

Use: JTBD insights can be created iteratively together with data collection and can follow a process like this: write down initial JTBD insights; cluster, merge, and prioritize; link JTBD insights to data; find gaps and iterate.

Expected output: Jobs-to-be-done insights

Writing user stories

Summarizing what customers or users want to be able to do; used to bridge design research with defining requirements for software development. ⁵⁰

User stories are used in software development to define requirements from a user perspective, instead of more product-based requirement documents. User stories are often formulated like this: As a … [type of user/persona/role], I want … [action], so that … [outcome]. In service design, they are used to connect design research with actionable input for IT development to turn insights and/or ideas into productive software. User stories can also be used beyond software development to define the requirements of any physical/digital product or service.

Preparation: Just as journey maps have different zoom levels, software requirements also have different scales. While user stories typically describe detailed requirements, a set of user stories can be combined into an “epic,” a longer, less detailed description of the big picture of what software can do. It’s important to define this scale level beforehand.

Use: User stories should be formulated without IT-specific language, using simple, concise words, so that everyone can understand them. Writing user stories often follows a process like this: write initial user stories, cluster user stories into epics, link user stories to data, find gaps and iterate.

Expected output: User stories

Compiling research reports

Aggregate research process, methods, research data, data visualizations, and insights. Reports are often a required deliverable.

Research reports can have many forms, from written reports to more visual collections of photos and videos. Depending on the project a research report can serve various purposes, such as providing actionable guidelines to improve a physical/digital product or service, a “shock report” to get internal buy-in for a service design project, proof of work that justifies the budget spent on research, a compendium of research data that can be reused in other projects, and more.

Preparation: Have your research process and your research data, as well as different visualizations (personas, journey maps, system maps) and insights (key insights, JTBD, user stories), at hand. Think who you could invite to peer-review your report.

Use: Write a first draft of your research report. Ask yourself: who was involved, which methods and tools did you use, and how many iterations did you do? Add a summary of your key findings and key visualizations, add raw data as evidence, and use indices to show that there’s much more data these are based on. Then invite other researchers or participants of the research to peer-review your report and iterate.

Expected output: Research reports

1. Jobs-to-be-done integrated as an additional lane in a journey map.

   

2. Example of a backlog in software development for an “epic” (i.e., a new feature) consisting of three user stories.

   

Case: Applying Ethnography To Gain Actionable Insights

Zahlhilfe program: An intersectoral cooperation to prevent electricity cutoffs

AUTHORS

Nina Weschenfelder Senior Service Designer, minds&makers

Michael Wend Senior Customer Experience Manager, E.ON

The challenge

Every year, the electricity supply is turned off in approximately 350,000 households in Germany ⁵¹ because the electricity bill cannot be paid. For those affected, the effects are dramatic and often lead to further social problems. For energy suppliers, cutting off the supply incurs costs, and it negatively affects the company’s image. Energy-related debts are often just one aspect of a complex debt issue and must therefore be addressed holistically. In view of this, there is no viable alternative to a cooperative effort involving institutions from the public, private, and social sectors.

Project objectives

The project was intended to avoid electricity cutoffs, reduce energy debts, and prevent their recurrence. To reach these goals, the various perspectives of all stakeholders in the arena of energy poverty had to be taken into account. The project sought to establish and consolidate intersectoral cooperation between job centers, debt counseling charities, and the energy provider.

The goal is to develop services for customers, job centers, and counseling charities that have an immediate impact as well as long-term benefits, and to implement them throughout Germany.

Our project process: From briefing to evaluation in six phases

We started the Zahlhilfe project in 2014. It consists of six key stages. Following the Identification, Understanding, and Developing phases, we tested the service concepts with approximately 300 real customers, job centers, and counseling charities over a period of six months in the Testing phase.

The joint minds & makers and E.ON project team is currently developing the necessary skills and resources within E.ON to prepare processes and employees for the implementation of the service system. Additionally, we are steadily involving more job centers and debt counseling charities with the aim of rolling out the service system throughout Germany (Implementation phase). In a continuous monitoring process, qualitative and quantitative data from the company operation are being analyzed to make the social and economic effects of the services visible and communicable (Evaluation phase).

1. In contextual interviews, the dramatic situation of people is visible in the form of the demands and cutoff notices.

   

2. Based on service concepts that are linked to insights and opportunity areas, the project team develops first prototypes in a co-creation workshop.

   

3. The service blueprint, complemented by insights, opportunity areas, and prototypes of the individual touchpoints, makes the service tangible for all stakeholders.

   

Systematic approach: A guarantee for accountability, a basis for decision making

Our systematic approach is particularly useful in meeting the special challenges of the complex stakeholder cooperation and the long timescale of this project. We systematically integrate our research results into all stages of our innovation process. To this end, we use a precise coding system to be able to consistently trace every step of our work process to our research results at any time: from the original experiences and statements of our respondents to the insights derived from these, right through to the identified opportunity areas, subconcepts, and concepts and to final implementation.

Our stringent coding system is a vital prerequisite to keeping the context of the innovation and service concepts transparent and communicable. For example, we always link the code of an insight to that of the original quotes it is derived from. In concept descriptions, we always quote the insights and opportunity areas the concept is based upon. This high traceability does not apply only linearly to one single project phase and the previous step; thanks to our methodology we can trace back from any project phase to any other project phase.

Our carefully designed research system prevents adaptations of our innovation and service concepts that are not strictly based on our insights and thus contradict our consistent people-centered perspective. The entire content of a project is always transparent and comprehensible for any of the stakeholders so that decisions are made on the basis of verifiable research results instead of opinions or taste.

Results

Job centers, counseling charities, and energy suppliers are working together closely so that E.ON can offer its customers suitable aid at different points of the customer journey: through a separate hotline, counseling charities and job centers can directly reach out to E.ON contact staff who have the authority to take action and suspend cutoff orders immediately. Customers in emergency situations receive initial external debt counseling via telephone, helping them to work on solutions. Interest-free installment plans with realistic payment levels now reduce energy debts for the customers as well as the loss of revenue and other costs for the energy provider. These are just a few examples of our overarching, intersectoral service system that is now being applied throughout Germany and is in the process of consolidation.

The three stakeholders – job centers, counseling charities, and energy provider – are addressing the complex societal problem of energy poverty simultaneously at complementary levels. Finally, to ensure positive outcomes from the project, an accompanying social impact evaluation monitors the service system in the long term, so that potential for optimization can be recognized and implemented continuously.

Because of the Zahlhilfe project, the issues of energy poverty, payment problems, and cutoffs have been given a sustainably firm place within the company structure.

Key Takeaways

1. 01 A high degree of transparency: Using this approach ensures that solutions are very transparent and easy to understand. This is particularly important in more complex cooperations, such as those in which not all stakeholders are involved in all steps of the project.

2. 02 Ongoing verification of completeness and effectiveness: In this approach it is continuously verified that the interests of the various stakeholders are being taken into account and whether the services that are offered solve the identified problems.

3. 03 Flexible application: The original quotes of customers and other stakeholders’ views make their concerns tangible; in this approach findings serve to support decision-making processes undertaken by the responsible bodies.

4. 04 A foundation of research: By building upon the initially conducted research work, this approach makes concepts less vulnerable to ad hoc changes or process dilution.

5. 05 Ongoing effort: This approach is slightly more elaborate in the beginning, but especially in the case of complex, long-term projects it reduces the overall error rate, as well as the time and effort needed for subsequent phases.

Case: Using Qualitative and Quantitative Research in Service Design

Policy Lab Work & Health Project

AUTHORS

Cat Drew Senior Policy Designer, Policy Lab

Laura Malan Senior Consultant, Uscreates

The problem

Around 2.5 million people receive health-related benefits in the United Kingdom, which costs about £15 billion per year, ⁵² and the wider economic costs of sickness absence and worklessness associated with working-age people in ill health are estimated to be over £100 billion. ⁵³ The longer people are on these benefits, the less likely it is that they will return to work, and being out of work can have a big impact on people’s health and well-being. On the other hand, finding the right work can be actively good for people’s health.

The approach

The UK government’s Policy Lab and the joint Work & Health Unit (a joint unit sponsored by the Department of Health and Department for Work and Pensions) created a multidisciplinary team with the service design agency Uscreates, ethnography agency Keep Your Shoes Dirty, and data science organization Mastodon C, and involved around 70 service providers, users, and stakeholders to solve the problem. After a three-day sprint to properly diagnose the problem, we embarked on a discovery phase of ethnography and data science, and a develop phase where we co-designed and prototyped ideas which we are now taking to scale.

We conducted ethnography with 30 users and people that supported them: doctors, employers, Jobcentre staff, and community groups.

The insights

We used data science techniques (Sankey analysis and k-means clustering) to look at patterns of people surveyed through the Understanding Society survey. It validated the existing insight that once people move onto long-term sickness benefits, they tend to stay on them and that people on health-related benefits also have non-health-related needs. It also revealed fresh insights. For example, the clustering showed two groups of people on health-related benefits who reported comparatively good health, meaning non-health-related interventions must be more important for them, and these two groups were distinct (one high previous salary, the other low). Therefore, we need to personalize responses to support people in different ways.

We used a combination of techniques, including spending time with people in their homes or places of work, conducting interviews based on photos that participants had taken, and doing user-journey interviews.

Two key insights were:

• → People have to tell their stories multiple times to many different services that do not share information, meaning no one has a complete picture about someone’s needs.

• → Individual line managers and confidence are big factors in whether people stay in or get back to work.

Idea generation

We turned these insights into evidence-based challenges to which users, doctors, employers, and policymakers brought their different perspectives at a co-design workshop. Ideas formed around a Work & Health coach who could signpost people to different non-health services, liaise with their employers to make adjustments, and build their confidence. We knew we could not build a whole new service from scratch, so local Jobcentres and community groups prototyped elements of it to see how it could fit in with their existing services.

In Penzance, the Jobcentre tested a website and posters which would allow employers to refer their employees to its service. One employee said: “Something like this would have been useful while I was still in work. I wasn’t as quick as some other staff because of my condition but they didn’t understand that. It might have helped me talk to my manager better about my health and what help I needed from him.”

In Southend, the Jobcentre tested offering its services to the local doctors, so doctors could easily refer patients to them. In East London, the Jobcentre and a local community group tested how they could work together to provide Work & Health coaches with their combined knowledge of local services. In Bournemouth, the Jobcentre tested a Health & Work book for users to keep their information all in one place. One user said: “It helped me organize the situation and focus on what I need to do to get where I need to be.”

Scaling

The qualitative feedback from the prototyping showed that these prototypes had real value. We are now taking the ideas to scale, and the project has prompted wider systems change. The insights gained have informed a more positive and holistic conversation for new applicants for health-related benefits. We are creating a digital version for people who are still in work, preventing them from falling out of work. And the project has played an important part in the creation of the Work & Health Unit (set up halfway through the project) and the subsequent £40 million Work & Health Innovation Fund.

1. A photograph taken by one of the ethnographic research participants to show her daily experiences.

   

2. A prototype of what the digital Health & Work book could look like.

   

3. Participants at the co-design day exploring the evidence to inspire new ideas.

   

4. The k-means clustering technique used to segment those reporting to be on health-related benefits.

   

Lessons learned

A valuable lesson was how the insights from the data science informed the ethnography (e.g., revealing how mental and physical health are related), and how the ethnography informed the data science (e.g., highlighting the non-health needs of those on health-related benefits). There is huge power in using these two techniques together, with the data science giving the broad, large-scale “what” and the ethnography providing the deep, rich “why.”

KEY TAKEAWAYS

1. 01 Data science can inform ethnographic insights (and vice versa) through correlation of different events.

2. 02 Combine data science to understand the large-scale context with ethnography to determine the deeper meaning or “why” of your research.

3. 03 When conducting research, speak with people from all ages, levels, and perspectives.

Case: Developing and Using Valuable Personas

Met Office app: A goal-based persona case study

AUTHOR

Phillippa Rose Service Designer and Facilitator, current.works

I recently led user research for the new Met Office ⁵⁴ app, replacing its old weather app. I worked closely with The App Business design team and the Met Office over six months on a range of research interventions. The use of goal-directed personas proved to be the most consistent and impactful tool.

Traditional demographic metrics were not fit for purpose. It was far more critical to deepen understanding of user behavior patterns, and motivations were key.

What do we need to understand?

In terms of target audiences for the new app, we were asked to design for almost everyone. As ever, we needed to make sure we really understood the problem before we could design solutions.

The weather affects everyone to varying degrees, and increasingly more and more people have smartphones and use them to access weather information – so in designing an experience for almost everyone, we needed to understand people’s levels of interest in the weather and their motivations. We needed to make sense of user tasks and goals. We also needed to consider technical considerations, and of course human behaviors and skill levels, ensuring the app provided just enough of the right information, quickly and easily.

In order to answer some of these questions we chose to create goal-driven personas. This approach was key in identifying and meeting user needs, engaging a wider range of stakeholders, and informing design decisions.

Step 1: Use what you have – what do we know already?

We collated and analyzed existing Met Office desk research and reports, analytics of the old Met Office app, and websites, plus feedback from the Met Office Public Weather Desk. We reflected on the data to get a sense of any common goals, tasks, or correlations between behaviors and motivations.

Step 2: Distilling data into value curves

Next, we distilled evidence from this body of research with recent insights from our own user research – including over 500 interviews, online surveys, and field research – to produce a set of common user tasks and activities influenced by the weather. We then ranked them against key considerations to create a set of value curves of 11 common tasks/scenarios.

Step 3: Testing assumptions through card sorting

We also needed to test our assumptions about what information is most important to people, so we conducted an online card sorting exercise with 139 responses.

Step 4: Value curve trend analysis

We plotted the 11 value curves together and looked for commonalities and patterns between them. Three cluster groups emerged from the value curves one of the accompanying illustrations shows an example of four value curves forming one broad cluster group.

Step 5: Affinity mapping exercise

We then reviewed all our findings and did an affinity mapping exercise to drill down further into the various tasks, looking at behaviors and considerations for individual tasks/goals.

The basic premises of three goal-directed personas emerged from this process: Flexible planners: people adapting plans/timing/locations depending on the forecast conditions (prepared to wait for or seek out the best weather)

• — Be prepared: people preparing for and making provision for weather conditions in order to stick to their overall plans, or adapting their plans to incorporate forecast weather conditions

• — High stakes/high impact: high-risk outdoor activity/event planning requiring decisions based on long-term forecasting with others involved

Step 6: Persona development workshop

We ran a goal-driven development workshop to flesh out the personas further using a persona template by Lucy Kimbell as a starting point. We adapted Lucy’s template to focus less on individual characteristics and more on goals, environments, and issues.

Here’s the final list of what we found most useful to flesh out each of the personas in turn:

• — Goals

• — Situations and considerations (can relate to time frames, logistics, or mindset)

• — Environment and resources (can include people, information sources, and physical environment)

• — Ties and associations (including people and organizations/brands)

• — Issues/challenges (what’s stopping them?)

• — Workarounds/opportunities (possible solutions, problem solving)

We also used this session to further develop a set of user needs for each persona based on the guidelines from the Government Digital Service: ⁵⁵

1. As a … [who is the user?]

2. I need to … [what does the user want to do?]

3. So that … [why does the user want to do this?]

Step 7: Sharing

We then distilled the data down even further and wrote it up using a mix of text and images. We shared our refined personas with the wider Met Office teams and stakeholders, and stuck them on the wall in the project room.

Step 8: Ongoing iteration

During ongoing user research the personas are being continuously reviewed and adapted as necessary. Persona profile characteristics have directly informed recruitment of participants for week-long diary studies using Dscout and ExperienceFellow. In usability lab testing interviews we have asked questions to test assumptions and uncover examples of goals, tasks, and behaviors associated with the different personas.

We also ran a series of ongoing design ideation workshops to generate ideas for future app updates and features during 2016 based on the needs and goals of our three core personas, and created a dedicated Trello board to document the ideas with color-coded labels corresponding with the relevant personas.

Learnings and next steps

What is distinct about this approach to previous personas I’ve worked on is that we deliberately concentrated on goals and tasks at the expense of characterization. Each of these three personas is distinct and people may identify with one more than the others, but it is likely that each one of us has experienced user needs similar to at least two of these personas in different situations or at different times in our lives.

A second distinction is that these personas have evolved over a six-month period, whereas often I work with teams on persona development in shorter, sharper iterations. A significant amount of time was dedicated to developing them and evolving them, in a close collaboration between myself, Rob Jung, and Dima Shvedun from The App Business with support from Chris Frost and Jay Spanton at the Met Office.

Key Takeaways

1. 01 Consider focusing the approach on common goals, instead of solely personas, to help identify with user needs.

2. 02 Customer personas can evolve over a period of time as more insights are uncovered.

3. 03 Don’t forget to use existing data that can help provide information.

1. Insights were distilled into user stories and displayed in the office.

   

2. We utilized a persona development template to focus on goals, environments, and issues.

   

3. We conducted a series of design ideation workshops to generate ongoing ideas for future updates.

   

4. One of the three value curve clusters shows patterns between data.

   

Case: Illustrating Research Data With Journey Maps

Promoting youth mental health: The impact of mapping the journey

AUTHOR

Jamin Hegeman Design Director, Adaptive Path

The challenge

How can we help youth with mental health issues in a challenged community within San Francisco? What services might they need? How can we best deliver them? How can we inform policymakers to get the funding for such services? These are the questions that the Edgewood Center for Children and Families’ Organizational Consultation team faced in early 2014 when they contracted with Mo’ MAGIC, the Western Addition collaborative of youth-serving nonprofits.

The Edgewood team sought to bring a human-centered design approach to the challenge. They engaged 29 youths who attend Magic Zone, an after-school program located in the southeastern area of the Western Addition, and conducted 24 qualitative interviews with adult community stakeholders from 15 organizations from within the Western Addition and city-wide.

After a discovery and research phase, Edgewood reached out to Adaptive Path for service design expertise in creating a journey map and facilitating an ideation and prioritization session with community stakeholders. The Adaptive Path team, consisting of one service designer and one visual designer, designed two three-hour workshops: one focused on gathering data to create a journey map, another to generate ideas based on the journey framework.

Journey mapping workshop

For the first workshop, we gathered a small group of subject matter experts, people who worked with the youth in different capacities or on different programs and the Edgewood team that conducted primary research with the 29 youths who attend the Magic Zone after-school program.

The information we sought included the stages of the journey, actions, thoughts or expectations, feelings, people, services, and locations relevant to the stages. We also wanted to identify the high points and low points of each stage. To simplify data collection, we created large butcher paper templates (one for each stage) for the data we needed.

Journey map visualization

After the workshop, we synthesized the data both editorially and visually. We transferred the data into a spreadsheet, grouped like items, and applied an editorial lens to the information. We did this to focus the information and make it succinct and digestible.

In the workshop, we used custom worksheets to capture the essence of what needed to be communicated to aid the editorial process.

1. A rough-draft sketch of the customer journey helped us better understand the experience of a young person engaged in mental health programs.

   

2. Journey sketches showed the different layers of the story.

   

3. The Mo’ MAGIC journey map illustrates the complete journey with roadblocks that youths face on the path to empowerment.

   

4. The ideation session generated 140 new, broadly defined concepts that were then prioritized.

   

5. We gathered all the data points of the customer journey and analyzed them to tell a complete story.

   

Our visual design process started on paper to quickly explore ideas. We then moved to Illustrator and continued exploring visual constructs. Once the visual matched the story we wanted to tell, we layered in the content. Several rounds of internal critique and share-outs with our stakeholders for feedback followed.

Applying an editorial lens to the content of a journey map is a skill and art in itself.

The keys to a successful journey map include:

1. Gathering the right content (then editing that for clarity, conciseness, and priority)

2. Articulating a point of view through communication design

3. Establishing an effective information hierarchy to ensure key messages are communicated and readers can access information at different levels of zoom

In other words, follow the core principles of effective communication design.

Journey map outcome

The journey is broken into five stages, or phases. Each phase is signified by a one-word title: Unawareness, Sensing, Awareness, Connection, and Engagement. Within each phase, there is a quote that represents the overarching feeling or belief of the youth. This is followed by more of a matter-of-fact statement regarding the situation – for example, “Youth is not considering change.” Each roadblock includes dialogue boxes containing language the youth might use. Dark gray arrows reinforce that youths often drop off the path to empowerment at all phases of the journey.

For the ideation and prioritization workshop, we created an ideation framework modeled after the stages of the journey. It contained a matrix of journey stages and opportunities. A cross-organizational team drew solutions for each box in the ideation framework for a set period of time.

Impact

This project achieved impact in several ways. First, it provided a common tool and language for various organizations and exposed stakeholders to a new way to tackle old problems. Second, stakeholders found the map useful when engaging youths to discuss where they might be in the journey. Finally, the journey map provided a new way to see a complex issue and make the case for funding several initiatives.

“Issues have been studied to death,” says Mo’ MAGIC executive director Sheryl Davis. Creating a journey map to understand the complexity of servicing youths with mental health issues was “very different,” she said. It showed stakeholders where the gaps were and led to new ideas for action. Armed with our journey map and new service concepts, the organization received $200,000 in funding from the Office of the Mayor. That’s service design making an impact.

Key Takeaways

1. 01 A successful customer journey map includes gathering the right content (then editing that for clarity, conciseness, and priority).

2. 02 A customer journey map provides a common tool and language for various organizations and exposed stakeholders to solve problems.

3. 03 Applying an editorial lens to a journey map can help make a complex issue more approachable.

Case: Current-State (as-is) and Future-State (to-be) Journey Mapping

The bigger picture: Projects building up to more long-term and strategic value

AUTHORS

Geke van Dijk Strategy Director, STBY

Ozlem Dessauer-Siegers Sr. Service Experience Design Lead, Vodafone

Introduction

Over the past four years the Service Design Lead at Vodafone has developed and fine-tuned a Service Experience Design methodology that was used for all Vodafone’s customer journeys in the Netherlands, and later also in the rest of the Vodafone countries. STBY contributed to this by doing deep-dive design research on several of these journeys.

Each project focused on a particular set of customer journeys with the aim to better understand the experiences, behaviors, motivations, preferences, latent needs, and pain points of customers. At the same time, considerable extra value has been created across these projects through a structured way of working. The results of this approach have led to a company-wide change program. The key added value of this approach is that customer journeys mapped for specific projects can be linked up to customer life cycles with a more strategic scope. In this way, service design contributes to strategic value for a business on a larger scale than just the individual customer journey projects.

“Customer journeys linked up into a customer life cycle can deliver a more strategic overview with relevance to the wider organization.”

— Ozlem Dessauer-Siegers, Sr. Service Experience Design Lead, Vodafone

Customer journey maps illustrating as-is and to-be service experiences ⁵⁶

Customer journeys are one of the key tools in the service design approach. In many service design projects, customer journeys are mapped to explore the experiences of people and their interactions with service providers. These customer journey maps offer an important foundation to analyze existing situations and to identify recurring patterns, pain points, and opportunities. A similar format can also be used for a different purpose – to visualize how service experiences could be improved in future offerings.

During each project with various teams in Vodafone the typical dynamic and project flow would be as follows: during the fieldwork stage, customer journeys are mapped to investigate how they actually recently happened (as-is). This leads to insights on aspects that could be improved (low-hanging fruit) and also on opportunity areas for substantial service innovation (new propositions). After a strategic prioritization of the identified opportunities with stakeholders from several departments, the ideation stage in these projects then leads to ideas for new service concepts. These concept directions are expressed in new customer journeys (TO BE) that highlight where and how the customer experience can be improved for both new and existing customers, typically illustrated with sketches to indicate what these new service concepts would add to the customer experience. The concept ideas and sketches are then progressed in implementation projects that further specify and build the new service offerings.

Examples of focus points for the various projects STBY and Vodafone worked on include:

• — New contracts for consumers (choosing a new contract or renewing a contract, and choosing a new phone)

• — New contracts for businesses (complex process of stakeholder communication and decision making)

• — Expenditure (monitoring and managing usage and costs during the contract period)

• — Connectivity (experiences with the network during the contract period)

• — International usage (traveling abroad during the contract period)

• — Multiple contacts (complex interactions between customers and provider)

• — Multiple contracts (adding other people or other features to a contract)

“To enable comparison across several projects, it is important to use a systematic approach.”

— Geke van Dijk, Strategy Director, STBY London & Amsterdam

Systematic approach generating progressive deliverables

To enable cross-comparison between the results of various projects, it was important to use a systematic approach and to make sure that the project deliverables were created in a similar way that allowed for progressive understanding and follow-up. The activities across the various stages included quantitative analysis, qualitative analysis, co-creation, and design.

1. Customer Experience Pyramid © Ozlem Dessauer (2015).

   

2. McKinsey’s consumer decision journey, or customer life cycle, is projected from left to right on the customer journey map. The pain points and insights from the design research, channel analysis, and financial analysis are listed from top to bottom.

   

3. The systematic set of project deliverables allows for progressive understanding and follow-up.

   

4. Examples of some of the as-is and to-be customer journeys documented for the project. Printed posters (±5 meters long) were put up on the wall for the team to work with.

   

Discover stage:

• — Quantitative insight analysis: identify pain points and weak spots in the current offering

• — Qualitative data collection: conduct in-depth interviews with a sample of customers (mix of young and old, male and female, various types of phones and contracts)

• — Documentation: co-create customer journeys of recent and relevant service experiences, illustrated with photos, videos, and audio recordings

• — Design research analysis: perform content analysis to identify patterns in customers’ experiences

Define stage:

• — As-is outcome: aggregated customer journey poster, illustrated report with key insights and recommendations, and trail of evidence to original data

Develop stage:

• — Co-creation session with internal stakeholders

• — High-level conceptual design

• — To-be outcome: customer journey for future improved service experience

Deliver stage:

• — Detailed design for implementation

In the Define stage, the aggregated as-is customer journey is developed, based on the recurring patterns of customer behavior and pain points found in the individual customer journeys. Relevant insights are then drawn out that lead to both direct improvements for the existing service offering and concept directions for substantial new service offerings. Each to-be customer journey delivers three levels of service solution outcomes: Fix (operational fixes), Optimize (improvements), and Change (service innovations).

From customer journeys to customer life cycles

Although produced for specific projects and specific project teams, customer journeys can also deliver a more strategic overview of consumer–provider interactions and offer relevance for the wider organization. This is done by linking the various customer journey maps into a customer life cycle map. While working together on the subsequent focused service design projects, Ozlem ⁵⁷ came up with this method, and it turned out to work really well.

Customer life cycles are a key tool in the field of business strategy. The scope of a customer life cycle is wider than that of a customer journey, as it includes the entire relationship from the first day of formal interaction between a customer and an organization (e.g., a new customer requests a quote) until the last day (e.g., the customer stops being a client). In the case of Vodafone, the customer life cycle spans from the first contract offered to a customer to the day that a contract ends and is not renewed.

For customer journeys, the start and end points of the process are less well defined. They usually start when a customer has identified a specific need and engages in activities to fulfill this need, and go until the point that this need is sufficiently fulfilled (or the customer decides to abort the process). This means that there can be a few different customer journeys within a customer life cycle.

Mapping customer journeys according to the focus of what customers are trying to accomplish is very useful for empathizing with their perspective and improving specific service concepts, but from the point of view of the organization these journeys need to be added up to create a more tactical and strategic overview that can be matched with the overall operations and the way the business is run. This is what a customer life cycle offers.

The wider scope of the customer life cycle needs to be taken into account while developing specific project assets. To be able to link the outcomes from each project into an overall customer life cycle, it is important to use a basic similar structure across projects. While doing this, the service design team needs to not only focus on the deliverables for the project at hand, but also anticipate how these deliverables can later be linked up to be useful to a more long-term and strategic view. A firm understanding of the link between customer journeys and customer life cycle maps enables this.

Key Takeaways

1. 01 Several focused customer journeys can be combined into a more overarching and strategic customer life cycle.

2. 02 Customer journeys can be formatted to express both the current as-is state of customer experiences and the envisioned to-be state of future improved customer experiences.

3. 03 Customer journey–based analysis and ideation leads to both direct improvements for existing service offerings and concept directions for substantial new service offerings.

4. 04 A systematic approach to customer journey mapping is needed in order to be able to link up into a customer life cycle.