KEY LEARNINGS
New technologies used for next-level service experience
Use data, such as crowd-based data, for a better differentiation in the customer interaction.
Think in customer experience chains and ensure that, in a multichannel strategy, the customer gets the best experience on every channel.
Pay particular attention to the switch between channels and design such switches carefully so as to make the interaction as simple as possible for the customer.
Use technologies such as artificial intelligence (AI) to realize next-level service experience.
Create an affordable, very personalized, high-quality service experience for a great number of groups (sweet spot).
Only rely on people in the interaction with customers when specialized, nonroutine, and emotionally demanding tasks must be performed.
Use Social CRM to collect customer data in social media. Optimize with the data the service channels and act proactively and according to needs.
Determine a digitization champion in the company. This can be a tech-savvy Marketing or Digitization Manager, who is an innovator and relies on the use of big data analyses in real time (with the help of AI), on pattern recognition, and prediction.
Get the right skills and T-shaped employees into the company, who, for one, understand a technology and, secondly, can act and be innovative in ecosystems.
3.8 How to combine design thinking and data analytics to spur agility
The job profiles and roles in our companies are changing across the board. There is a multitude of new job profiles today. Until recently, Peter thought he had the coolest job in his company. After all, as the Co-Creation and Innovation Manager, he shaped the innovations of tomorrow. Then, some time ago, he read in the Harvard Business Review that being a data scientist is the “sexiest job in the 21st century.” In the future, data scientists will generate innovations, solve problems, satisfy customers, and get to know more about the customers’ needs through big data analytics. In his blog on digital transformation, the CEO of Peter’s company had also written about a data-driven business and that, nowadays, all business problems are solved with the new technologies.
How can we take advantage of this trend for our design challenges and integrate the faction of data scientists in the problem-solving process?
To benefit from big data analytics, we need a procedural model that combines design thinking with the tools of data scientists. The “hybrid model” (Lewrick and Link) is a suitable way to do so. This model has been developed based on the design thinking components. It promises to boost agility and ultimately result in better solutions. The hybrid approach gives companies the opportunity to position themselves as pioneers and become data-driven enterprises.
The model consists of four components: (1) the hybrid mindset; (2) a tool box filled with the existing design thinking and new big data analytics tools; and other key elements are the collaboration of (3) data scientists with design thinkers as well as a hybrid process (4) that can give orientation to all parties involved. Thus the hybrid model is another possibility for expanding the design thinking mindset and generating better solutions from the combination.
The advantage of the hybrid model: We create a mindset that gives us superior arguments when dealing with skeptics in traditional companies. One frequent point of criticism is that design thinking generates information on the needs only through ethnographic and sociological methods such as observation and surveys. With the hybrid approach, we can eliminate this vulnerability. Expanded by tools for the collection and analysis of big data, the quality of the design thinking process is heightened throughout.
HOW MIGHT WE...
go through the phases in the hybrid model?
Because the hybrid model is following the design thinking process, we primarily want to point out what is added. As in design thinking, the customer need and a problem statement (pain) to be solved mark the beginning. It can be a more rational or else an emotional problem. In the end, the solution may be a newly defined physical product, a digital solution in the form of a dashboard, or a combined solution that encompasses both elements.
The first phase is understand: we develop in common an understanding of the problem. It is important that data scientists and design thinkers already collaborate here. Some facts can be determined through the analysis of social media data, for instance, which has a broader base than data gathered from traditional user surveys.
The observe & data mining phase is dedicated to the collection of “deep insights” and “deep learnings.” “Deep insights” arise from our traditional observations of customers, users, extreme users, and the like. To obtain “deep learnings,” data must be collected, described, and analyzed, which allows us to identify initial patterns and visualize them. We recommend discussing the insights from both observations together and reviewing the next steps.
In the define phase, we combine the “deep insights” and “deep learnings.” A more exact point of view can be defined this way. The PoV describes the need a specific customer has and on what insights the need is based. The combination of both sides helps to get a better picture of the customer. The stumbling block again here is the definition of the PoV. We already talked about it in Chapter 1.6. The hybrid approach yields more “insights” that confirm the PoV but can also result in even bigger contradictions.
The aim of the ideate phase is to continue to generate as many ideas as possible, which are then summarized and evaluated by us. Several ideas are available at the end of this phase that are used in the next steps.
Then comes the prototype & modeling experiments phase. In this phase, we develop prototypes and carry out experiments with models. Prototypes make ideas palpable and easy to understand. As we know, a prototype can take many different forms; an algorithm, for instance, is also a simple prototype. The insights from the data experiments are best represented with models in the form of visualizations; in data science, this is the best solution to make something tangible.
In a test & proof of value phase, the prototypes are tested together with the potential user in order to learn from the feedback and adapt the solutions to the needs of the customer. This includes models, visualizations, and dashboards from data science, which constitute the basis for the prototype.
In the final phase, realize, we transfer an idea into an innovation! This includes integrating the models in operations. While data solutions usually evolve from data science projects and design thinking develops products or services, in the hybrid process, combined solutions from data science and design thinking can emerge. This can refer to a service-plus business model that presents added value as a result of the aggregation of various data sources; an example would be changes in the behavior of drivers to avoid traffic congestion in combination with an app.
EXPERT TIP
Live a hybrid mindset
For the successful combination of big data analytics and design thinking, a mindset should prevail that reflects the work in a hybrid model. Because we now have a group of data scientists on board in the projects, it is useful to add the corresponding components to design thinking. A possible mindset can be described as follows:
EXPERT TIP
Composition of the teams in the hybrid model
You need an interdisciplinary team to work with the hybrid model. It is made up of design thinkers, data scientists, and those responsible for implementation.
A facilitator who has the methodological knowledge continues to support the team. The team members can come from a wide variety of areas and contribute their differentiated background knowledge. Depending on the situation, the right specialist from data science can be used. The people responsible for implementation are part of the team.
EXPERT TIP
Toolbox for the hybrid model
We recommend having a combined toolbox ready that contains the usual methods of design thinking and the tools from data science. As in design thinking, the
critical point is: use the right method at the right point in time. There are many useful methods in design thinking that are easy to apply and quick to learn for everybody. In data science, things are a little more complicated because many tools require expert knowledge. But there is hope that more and more tools are being established that are user-friendly and can be used to perform data analysis without programming skills and expert knowledge. In addition, an increasing number of companies train their employees to acquire these skills. We have had very good experiences with Tableau. This is an easy-to-use tool. It also has a “back” functionality, if something goes wrong in the data experiments.
EXPERT TIP
Convince stakeholders of the approach
The hybrid approach compensates for the weaknesses of the unified approaches. Introducing a combined mindset has better chances of success than introducing one after the other sequentially.
In our experience, both top-down and bottom-up work.
With a bottom-up approach, the exchange between employees who deal with the subject of design thinking and those who are into data is promoted. In workshops, the two groups can present their approaches and challenges to each other. It quickly becomes apparent that the two approaches are complementary. The goal is to find a common pilot project in which the collaboration can initially be tested.
In a top-down approach, the advantages and disadvantages of both mindsets are presented to top management with the goal of carrying out an initial pilot project using the method of the hybrid model. After the pilot project is completed, the experienced gathered and the advantages are reported to top management and the stakeholders. In general, the hybrid approach reduces a number of risk factors; for example, it lowers the innovation risk of early experiments. In interdisciplinary teams, not only are new skills brought to the projects but also different ideas, which broaden the perspective. The same applies to a combination of systems thinking and design thinking and to projects that link strategic foresight to design thinking.
The hybrid approach—paradigm shift reduces risks
Paradigm shift
Focus on the overall picture (human being + data)
New mindset
New composition of the teams
New hybrid process
Risk factors that can be reduced
Innovation risk/risk entailed in search field for ideas
Cultural risk
Skills risk
Model risk
Implementation principles
Support, top management
Part of the transformation toward digitization and/or data-driven enterprise
Risk factors that can be reduced
Implementation risk
Strategy fit risk/management risk
EXPERT TIP
The supreme discipline—varying mindsets in the double diamond
The usefulness of hybrid models became quite clear to us early on. Along with heightened agility, we can generate more insights with the combined approach and mindset, which allows us to increase the number of possible solutions. Top innovators go one step further with their mindset and switch between design thinking, systems thinking, and data analytics across the entire development cycle. The quadruple diamond ensures that the optimum mindset is applied at each point in the cycle. Especially with far-reaching and complex problem statements, the respective design teams, squads, or experimental labs can optimize their work and apply the different skills sequentially or in a mixed form. The respective experts come from the corresponding chapters or guilds and help ensure that the necessary skills are available for each phase. As a facilitator or as the leader of a tribe, this also means having a higher level of methodological expertise at their disposal and having a sense for applying the right methods and tools in each phase.
We benefit from the fact that the three approaches go through similar steps. Thus the quadruple is purposefully built on the “double diamond” of design thinking, which is augmented by data analytics and systems thinking. Depending on the project, the mindsets can be mixed in the respective iterations.
When applying them sequentially, one single approach is executed; in the reflection at the end of the iteration, the further course and the method to be used in the next iteration are determined.
As described in Chapter 3.1, using design thinking and systems thinking in every project is recommended.
In a project that is largely driven by design thinking (example 1), systems thinking should be applied at least once in the end so as to depict and classify all the insights systematically. In a project that is driven by systems thinking and in which the system has already been improved iteratively two or three times, the critical assumptions should be checked in design thinking experiments, thus validating the system (example 2).
At the end of the day, the point is to understand each and every aspect of the problem from all perspectives by working on mixed teams with mixed methods. In the second part of the double diamond, the right solution is then also found with combined approaches. Example 3 shows the combination of design thinking and data analytics.
You can also combine all three approaches. This should only be done by experienced teams together with a facilitator, however. Combining all approaches naturally requires know-how in all of them (see example 4). As in the hybrid model, it is important that mindset, team, and tool sets are combined and not only the process be considered.
As in the hybrid model, it is important that mindset, team, and tool sets are combined and that not only the process be considered.
KEY LEARNINGS
Hybrid models—combination of data analytics and design thinking
Take advantage of the technological possibilities of big data analytics and machine learning in order to be innovative in a more agile way.
Define a problem statement in conjunction with the new contingent of data scientists.
Observe people and data and draw conclusions in common.
Accept that deep learnings can also come from data.
Establish a common design process and make clear what should be done in what phase.
Develop a common mindset to establish a hybrid model successfully.
Work on mixed teams. Take data analytics experts on the team.
Accept solutions and prototypes based on data.
Be open to innovations that are more data-centric than human-centric.
Win over top management for a hybrid model or start bottom-up with a pilot project.
Use alternating mindsets and combine design thinking, systems thinking, and data analytics as the supreme discipline.
Closing words
What have we learned on the journey?
There are quite a few things we have learned on the journey through the Playbook and in the interaction with potential users, our readers. The time has come to reflect on the factors of success before we move on and say goodbye to Lilly, Peter, Marc, Priya, Jonny, and Linda.
We have been confirmed in our claim that the traditional management paradigms must be challenged in order to detect future market opportunities and successfully implement them. The traditional mechanical- deductive approaches will make it difficult for companies to redefine entire value-creation chains and adapt their business models to the new customer requirements. Yet, unfortunately, the idea that innovation follows a defined stage-gate process with a clear sequence from search for ideas through implementation is still prevalent in many companies and in the minds of directors, department heads, and those responsible for innovation. These models have been outdated for at least a decade now.
There is a need for a fundamental change toward a systemic- evolutionary approach. Ideally, highly motivated interdisciplinary teams act in self-organizing network structures. Their work is based on customer needs and is geared to implementing new services, products, business models, and business ecosystems in a targeted way. When searching for the next big market opportunity, design thinking offers a strong mindset. This minds
et must be further developed and combined with other approaches. No one size fits everything—we have to find our own way and the appropriate mindset for our organization.
After interacting with and observing numerous employees in management positions of companies that already have initiated the transition, we noticed in the context of innovation, co-creation, inspiring teams, facilitation, and the like, that design thinking still has not been comprehensively integrated in most companies. Design thinking initiatives often run in specially dedicated organizational units, and a new mindset prevails sometimes more strongly, sometimes more weakly in the companies. Likewise, we could recognize little activity so far that aims toward a consistent application of a hybrid model, with the combination of big data analytics and design thinking. Agility could be boosted and the number of possible solutions increased with such a combination. The business ecosystem approach has so far been dominated by only a few players. Most product managers in large companies have so far neither learned the necessary skills nor received instructions to monetize new solutions in decentralized networks. In addition, a clear vision is lacking in many companies and the same goes for the use of strategic foresight or systems thinking, for instance, in combination with design thinking for an improved mapping of a possible future. Change takes time as well as strong personalities with a clear vision at the top of companies.
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