What are the dimensions to be taken in due consideration?
When we want to design a new digital model for our company, four elements are absolutely critical to success from our experience alongside the focus on the needs of people: the operating model, the adapted technology, the digital business model per se, and, increasingly, the business ecosystem.
We can outsource a sub-function, for example, from our core business to an actor in the ecosystem in order to benefit from an expanded customer access. Or we already have established partnerships that we can profitably use for testing an MVE (minimum viable ecosystem). In addition, some thought should be expended on how complementary advantages through the combination of established platforms and digital services might be realized (e.g., a combined offering through a centralized touch point with the customer). In the definition of the model, it is vital to keep an eye on the big picture and at the same time explore it with minimal functions (MVP and MVE).
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“4 wins” is not enough
We have talked a great deal about fitness and, in the end, we might be able to cross the mountain pass with customer orientation, the right skills, motivated employees, and a good implementation plan. Companies that have higher ambitions and want to be among those who come in first in their own particular Race Across America should bank on “8 wins” (see p. 290). The Race Across America is one of the toughest bicycle races in the world. For such a race, it is not enough to tend toward aimless action in individual forms. What is required is the perfect match of material, willingness, and vision, all the way to the ability to integrate new technologies from an extended ecosystem. For our own organization, we must reflect on the question of where we are in terms of the individual forms. In the end, we must decide for ourselves whether we want to go on a leisurely ride on our bicycle or whether we have the willingness and the vision to compete with the best in the world. In 2015, it was David Haase who optimized his Race Across America using sensors, weather data, artificial intelligence, and big data/analytics. With this, David’s performance reserves could be adapted to the conditions, and decisions were optimized.
KEY LEARNINGS
Digital transformation
Start the digital transformation with a design thinking workshop.
Take the needs of customers into account when developing digital products and services.
Accept the fact that new technologies will continue to bring forth great upheavals; at the same time, they give us the chance to tap new market opportunities.
Overcome the “digital divide” by developing new skills (e.g., to use network effects).
The greatest art in digital business models is to create business ecosystems and, as an entrepreneur, become a driver of digital transformation.
Think in two directions when considering strategic options: either secure or milk the existing business and develop new digital business models.
Digital transformation is also an organizational transition and demands agile and transversal collaboration on interdisciplinary teams.
Establish a new mindset and teams in the organization, which can meet these challenges.
3.7 How artificial intelligence creates a personalized customer experience
Designing a differentiating customer experience has become an integral part of the daily work in many customer-oriented companies. In Peter’s company, a solid basis for the “next level of customer experience” has been already created as part of the digital transformation. People no longer think in departments but holistically and transversally. Along with Sales, Customer Service, Marketing, and Operations, partners and resellers get involved.
The primary focus at Peter’s company is on differentiating the way the business interacts with its customers. Data plays an important part. After all, a multitude of it is collected with every interaction: from the visit in the brick-and-mortar shop to online purchasing all the way to interaction on the customer portal. A comprehensive customization can be carried out on this database for the individual customer. Because many companies today pursue a multichannel strategy, it is important to ensure that the individualization of the customer interaction is done across all channels and that each individual touch point makes a contribution to a differentiating customer experience. Thinking along the line of customer journeys is essential for the shaping of sustainable customer relationships.
In the customer interaction, what are the challenges entailed in the digital customer life cycle?
In the past, the customer life cycle was more sequential and limited to a few channels—from perceive, inform, and order to install, use, and pay all the way to support and termination. It was usually done through only a few traditional channels with a (sometimes accepted) break in the media and the experience between the individual steps. As Peter has learned from a panel of experts at his alma mater in Munich, the customer of today moves through a broad variety of channels, sometimes concurrently; skips steps; and continues them in other channels, frequently with other devices. Digitization leads to new forms of interaction between the company and the customers (and between customers) and makes it possible to design more holistic experiences. These challenges and opportunities must be addressed in the design of customer experiences.
Identifying the customer concern and thus the phase of the customer life cycle in an interaction as early as possible constitutes one of the challenges. This is why it is essential for Peter’s employer to collect the interaction data on in-house digital channels, classify the customer to the extent possible, and allow and use the data in the interaction.
The earlier the customer’s reason for contact can be identified, the better the customer can be guided in the channel universe and the better the experience can be shaped. Assuming that the customer contacts the company in the event of a complex fault, the issue is not to prioritize the customer chat but to choose a channel that is more appropriate for the processing of the concern, such as video telephony or a technician visiting the customer at home.
With the increasing digitization of business processes, there are also more and more problems, which the customer can easily and without great effort process on his own on the customer portal. In addition, the company itself today has long ceased to be the only contact point for the customer along the entire customer life cycle. In fact, companies need to involve external value-creation partners. Customers can turn to communities, for instance, to have their problems solved. The Swiss IT company Swisscom, for instance, integrates both the online customer forum and the tech-savvy offline community of “Swisscom Friends” in the design of the experience.
When designing customer interactions in the digital life cycle, it continues to be crucial not only to allow for switching channels but to design the switch as an integrated experience. This includes making customer processes independent of channels, so a seamless switch is possible without information and status loss and without the customer having to state his problem more than once. The customer effect score or “easy score” can be used as an indicator here. These metrics show from the point of view of the customer how easy the company has made it for the customer to resolve his problem. Ideally, this indicator should be taken into account in the iterative development of customer experiences as early as at the beginning of customer tests.
How can we raise the service experience to a new level by means of the technological transformation?
In saturated markets, the customer experience is a major point of differentiation in terms of customer loyalty. The aim is for the customer to remember the interaction between the company and customers as a positive point of differentiation and develop a preference for the brand.
The technological transformation creates new opportunities to shape this differentiating experience in a more targeted way. Companies today have access to a huge amount of data that is created in interactions with customers, in processes, and with objects equipped with sensors.
Big data analytics makes
it possible to process these large amounts of data and recognize patterns. The insights thus gained not only help us to understand the nature of the interaction between the company and the customer better and thus further develop the customer experience in general; they also enable us more and more to create an individual, differentiating service experience in the interaction with individual customers.
The developments in the area of machine learning allow us to penetrate areas in the service experience that were hitherto inaccessible on account of limiting factors such as finite human capital. Tasks previously performed by humans (e.g., in the customer dialog) can now (partially) be transferred to machines, which opens up the opportunity to scale new service models and implement them cost-efficiently.
The digital design thinking methods presented in this book help to design the “next-level service experience.”
What new opportunities arise from using artificial intelligence in customer interaction?
With artificial intelligence (AI), we can finally open up the sweet spot of customer interaction: for many customers, a unique and personalized experience. In the past, an extraordinary service experience could be offered only to a selected group of customers. The great masses were denied this service experience on account of the high costs. All we could do was to present a rather limited service experience to the great masses, which then rarely left a lasting or differentiating impression. With the use of artificial intelligence, we are able today to create a personalized, high-quality service experience—and for a large number of customer groups (sweet spot), to boot. Hence service orientation can penetrate areas now that were economically not viable before. Digitization allows for the realization of so-called artificial assisted service models, which were too expensive to be performed by people up to now. Companies that know how to take advantage of this competitive technology advantage can become new leaders in the service area.
For which customer interactions should we rely on artificial intelligence?
AI tries to imitate human behavior when solving tasks. This implies that AI is capable of learning from its own observations and the existing data (e.g., interactions) to solve future tasks with the knowledge gained. One property of AI is that unstructured data such as text, language, and images—in other words, human communication—can be understood. In addition, the intelligence is increasing over time, because AI considers feedback from past decisions for future decisions. Compared to humans, AI not only decides more quickly and precisely, it is also able to take into account much larger amounts of context information. For us, this means we can transfer activities that follow a specific pattern to the machine when designing customer interactions. We deploy people only where specialized, nonroutine, and emotionally demanding tasks require it.
What might a possible vision of the customer dialog with artificial intelligence look like?
We recommend taking the first steps in working with AI in an area in which a great deal of interaction data is available that is recognizable and, hence, comprehensible to the machine. Within this area, applications can then be found within which routine activities are identified by means of AI and then outsourced to it in the future. This way, the benefit that is to be achieved by using AI in the customer interaction can be assessed quite clearly right at the onset.
Based on this initial experience, not only can more use options be better assessed, but more fields of application based on them can be found. One good starting point can be the customer dialog via e-mail, because this type of customer interaction still shows a great deal of potential for boosting efficiency and a solid database is usually on hand.
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Use of Social CRM
With Social CRM (SCRM), we can expand conventional customer relationship management using elements that focus on the interaction in social media. In so doing, we integrate in our CRM customer data that is not directly company related and use it as additional information in order to get a better idea of our customer and his interests. Through the collection of customer data via social media channels, we optimize our own service by acting on a more needs-oriented and proactive basis. For this, the willingness of the customer to share his data is relevant. It depends a great deal on how valuable he sees the sharing of his information with the company. It is crucial here that—from the customer’s point of view—a fair exchange of (personal) information with the company takes place.
If, for example, we systematically collect the data traces of a customer’s open Facebook account, we recognize the themes he discusses on social media. We can use this information as a trigger and thus approach him proactively with a matching offer.
An alternative to SCRM are so-called data providers, whose business model is based on the collection and sale of customer data. They sell information such as place of residence, shopping and travel habits, number of children and pets, clothing size, and so forth to interested companies. This information offers valuable insights for designing offers that match even better. It greatly depends on the ethics of each individual company to what extent such data is purchased and used.
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The Marketing Manager as digitization champion
The technological transformation confronts the Marketing Manager with a new challenge. He is successful if he puts the customer at center stage and, at the same time, is able to use appropriate technologies in the company. Today, the analysis of big data in real time—with the use of AI—is possible, as well as pattern recognition and prediction. A successful Marketing Manager makes use of this in order to understand his customers better and anticipate their needs. He performs data-based customer experience management so he can address his customers better and shape experiences actively. He can offer the customer a real added value. For example, he uses the movements or periods in life of his customers—so-called moments of truth—to create a perfect shopping experience or make the use of his product more relevant. In his function as Marketing Manager, he will become an innovator in the company alongside the traditional R&D and the Digital Manager. Many companies appoint additional, or evolutionary, Innovation Managers, who expedite an even closer integration of technologies and platforms.
A Marketing Manager as innovator will ask himself:
Where does the customer stand in his period of life or even in his daily routine?
What does the customer do, when does he do it, and where?
What does the customer need at this moment?
How can we reach our customers?
Which data can I access?
These questions are intended to help the Marketing Manager enable a unique experience with the company for the customer.
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The Innovation Manager as inside outsider
After discussing the role of the Marketing Manager as an innovator, we want to take a look at the role of the Innovation Manager, not least because it is also a role that’s changing. The role of Innovation Manager is defined more and more by market requirements, changed business models, and the basic claim to understanding and using information technology (IT). In addition, the Innovation Manager has the tasks of bringing a new mindset into the organization on account of fundamental changes of market and business mechanisms, and of supporting the transition needed to adapt and to establish the right skills. He is also the link between the internal innovation systems and the external world, such as in partnerships with start-ups, accelerator programs, and universities. For this reason, it’s a good thing if the Innovation Manager has built his network in the company over the years while working in other positions and if he is given the leeway to act freely with the external system. He performs his function as an inside outsider. In his function, he is innovative on the level of business models and below—always with a holistic view of emerging technologies and market needs.
The following questions are increasingly important to the Innovation Manager:
Which pacemaker and key technologies do we need for new
market opportunities?
Which business models will be effective in our industry?
Which start-ups and strategic alliances will bring added value?
How do we increase the agility in the implementation of growth initiatives?
How do innovation efforts become noticeable in the future scenarios?
Which mindset suits us and how can it be spread transversally?
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The Digital Manager as an enabler of digital transformation
The Digital Manager is dedicated to the topics with top strategic priority in terms of the development of a digital offer. Today, he is usually the link between Marketing, Operations, IT, Innovation, and the Chief Executive Officer (CEO). The CEO has made the theme of digital transformation a core issue of the corporate strategy. The Digital Manager is tasked with providing the required skills, platforms, and technology components for a “seamless experience” and with implementing the digital initiatives. On account of a higher level of automation and maturity in the digital themes in many industries, Marketing will advance into an artificial inteligence department, hence changing the role of the Marketing Manager as a digitization champion. In many areas, the Digital Manager already takes on the tasks of customer interaction, communication, and their transition into digital experiences. In addition, he becomes an architect of digital ecosystems in which, on account of the new technologies, he also redefines the value streams and transforms the business models.
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