Servitization: buzzword or game changer?

If you look at current analyses on the topics of digitalization and the platform economy, things do not look rosy for Europe and Germany at the moment. The regular survey of the 100 most valuable digital platforms by network economist Dr. Holger Schmidt shows thatEurope's share of the platform economy is modest at 5%. Nevertheless, Germany has moved up seven places in the Digital Quality of Life Index 2021 to 9th place. According to a recent study by the American data specialist Snowflake, two thirds of German companies still do not feel ready for the data economy.

Finally falling behind?

German industry has everything it needs to digitize faster and use data profitably: Its globally recognized engineering expertise, its position as a leading export nation and its strong reputation in the field of AI research.
Smart products and customer-centric services are the drivers for exploiting the available opportunities.According to a survey conducted by McKinsey in 2020, 88% of SMEs want to introduce new services and business models in order to improve interaction with their customers and achieve greater customer centricity. The term "servitization" is cropping up more and more frequently in this context.

McKinsey
Servitization, also known as service transformation, refers to a business model innovation: moving away from pure product sales towards offering product-service systems. The "power-by-the-hour" model for Rolls-Royce turbines is often cited as a popular example. Here, the turbines were rented out and the fault-free operating hours billed. However, this neglects the current relationship between servitization and the digital age.
We should therefore speak more precisely of the term "digital servitization", which describes more specifically that smart products are enriched by additional digital services and offered together with a corresponding IT ecosystem. The decisive factor for the success of such strategies always lies in the collection, processing and provision of data. This ensures monetary growth, competitive advantages and, in particular, direct access to end customers.

Finally using and selling data and knowledge

Data and knowledge are the decisive factors for scalable and sustainable digital business models, especially in the highly knowledge-intensive mechanical and plant engineering sector. When used optimally, customer-centric services, digital products and new data-driven business models can be created. On this basis, companies are transforming themselves into customer-centric platforms with additional digital offerings and building up a comprehensive digital service business, which promises higher margins, returns and growth opportunities than the actual core products. As tempting as the opportunities of digital servitization may seem, the domestic industry often struggles to implement it and is increasingly losing market share to so-called tech giants. The following are therefore the five most important steps that describe the path to digital business models.

The 5-step plan for data-driven business models

Step 1: Taking stock

The first step is to take stock and check the relevance to the corporate strategy. On the one hand, you should question the extent to which digital services can change the existing service landscape, particularly in terms of efficiency and scalability. In addition, it should be discussed whether existing products should be supplemented by digital services or whether completely new, digital product-service systems should be offered. You should also keep an eye on market trends and any external threats to contact with the end customer. If you see opportunities for the company's success, you should take a dedicated look at the potential.

Step 2: Identifying the potential

Digital servitization is in a constant field of tension between a "technology push" - what is possible due to new technologies - and a "market pull" - what are the customer needs in the market. Both issues should therefore be systematically analyzed in this step. Customer needs should be the starting point in order to prevent the development of complex products and services that fail to meet market requirements. The aftersales/aftermarket area is a good place to start, as close customer contact is already maintained here and sales can be increased quickly. Based on the defined strategic goals, a "product service portfolio roadmap" is drawn up. Potential partners in the customer environment, suppliers and competitors should also be identified.

Step 3: Design a dedicated organizational and structural setup

If the service business is an important pillar of the corporate strategy, this should also be visible in the organization with short reporting lines and anchoring at C-level. Dedicated resources should be set up for the sale of (digital) services and new innovative products.

Step 4: Think big!

Think big, but break down the strategy into verifiable and achievable minimum viable products to determine the product-market fit. In this way, the first products and services can be implemented as proof of value. A stage-gate innovation process should also be established in which the teams must demonstrate progress in order to validate the value of the products. It is very important that the service innovation portfolio is continuously reviewed and that non-functioning projects are terminated before "zombie projects" emerge that consume time and budgets. It is essential to collect the right data and transform existing knowledge into customer-centric products and services.

Step 5: Gradual expansion and transformation to a service business model

How can a rethink be initiated in the organization? First of all, the organization should be transformed and sales employees should then be gradually upskilled using suitable training concepts. This can change the sales DNA in the long term.

Such projects can often not be managed on their own and from day-to-day business. However, partners and holistic SaaS solutions are already available today that are dedicated to digitalization in service and are suitable as the basis for data-driven business models without undermining customer access. In order to ensure seamless data utilization and provision, it makes sense to rely on "Made in Germany" solutions, especially for reasons of data protection and data security.

How modern SaaS solutions are realizing the service of tomorrow today

Modular, AI-based SaaS products are already providing service technicians, support agents, sales staff and technical editors with the right product and service information at the right time and in the right place, making the service process more efficient from signal to action.
In knowledge portals, users can use the AI-based search to securely access all relevant information in a matter of seconds - regardless of where or in what format it is stored. They can also solve complex problems more quickly with the help of guided error diagnostics.
SaaS products also support active, structured knowledge transfer and situational learning with the help of team apps. Service knowledge is tapped where it arises: during service technician assignments in chat groups, during remote support calls, in countless service tickets or in the daily notes and feedback from service employees. This prevents the loss of knowledge in technical service resulting from employee turnover and demographic change.