Business models

Core findings

  1. Flexibility, adjustability, and readiness are central competitive features required in all sectors in the next five years.
  2. To date, more than half of the companies we surveyed have not planned to transform their business models digitally yet.
  3. Even so, almost half of the companies had at least already begun to expand the digital services of their existing business model.
  4. Besides digitalizing the customer interface, analytics-driven and customer-driven business model innovations are foreseeable.
  5. Conventional ways of doing business could lead to the loss of end customers due to digital platforms being easily accessible.

Current competitive advantages,…

… will still be relevant in the future

Flexibility towards customer demands is seen as the most important factor. 44.4% of the logistics service providers in the survey regard the competitive advantage of flexibility as relevant for the future. Quality of products/services (32.2%) and adaptability (29.4%) are both seen as very important for logistics companies in the future.
Generally, there are only slight differences between the industry sectors. In relative terms, the aspect of data (end-to-end data transparency and ownership of end-customer data) is the most important, while manufacturers rank the ability to innovate as the most important.

Digital transformation of business models

Only 2.5% have already carried out a broad transformation.
35% have already undertaken a minor or partial transformation of their existing business model into a digital one. To date, 46.8% of all companies have not planned to transform their business model. Currently, the companies surveyed rather expand their business model through the inclusion of digital services.

Driving business model innovations in the future

Analytics-driven business model innovation

  • The starting point for this is the findings generated from data
  • An analytics-driven business model innovation can be based on the business analytics process* (see fig.).

Model example: Analytics for optimization

Here is an example of a business model pattern using analytics for optimization in logistics: Using this business model pattern, based on the business analytics process data (e.g. transport or traffic data) can be used by a logistics service provider to, for example, optimize route planning or improve workforce planning in the customer’s warehouse. In essence, the data is used to enhance the provision of a transportation service by means of a data-based service. What is changed in the process is the business model elements of the value proposition, the relationship with the customer, and the core activities.

Customer-driven business model innovations

  • In contrast to analytics-driven business model innovations, these are driven by customer needs.
  • The business analytics process can then be used to satisfy specific customer needs.

Model example 1: Object-Self-Service

The model of object self-service describes the possibility that physical objects trigger autonomous orders. For example, machinery can be automated and order replacement materials (e.g. filters). Hence, in logistics it would be possible for a logistics company to install a weight measuring technology in their customers’ storage facilities which would cause parts to be ordered automatically for restocking when the weight of any given part falls below a specific level. This changes the value proposition and thus also the relationship to the customer. The customer no longer needs to concern themselves with checking stock levels and ordering stock. Alongside the core service (delivery of the goods), this creates additional value for the customer, which in turn increases customer loyalty and creates change barriers.

Business practice example 2: Digitization of the customer interface (digital interfaces)

The digitization of the customer interface can also serve as another model. In the case of a logistics company, this enables web-based configuration of the transport service. The customer of the logistics service provider can select different components of the service online, e.g. transport and customs clearance. The online order placement and well-documented target processes enable automated scheduling and shipping by the logistics service provider. This changes the value proposition for the customer as ordering becomes simpler and tailored to their specific needs. This improved value proposition strengthens the customer relationship. Additionally, the cost structures for the company offering the services.

Payment driven

The starting point here is the data itself, which serves as the means of payment.
The business model is adapted in terms of the way in which income is generated (e.g. data as a payment). The data is used as means of payment and as such itself constitutes the added value without requiring further analysis. This principle can often be found in the B2C field. There, the data which Internet users disclose (e.g. from their use of social media) is used (also commercially) in particular to place individualized advertisements.

One possible example of this in the B2B field in logistics is that a shipper sends its own data (e.g. loading frequencies, loading times) to the logistics service provider, who can then use this data to optimize their own order planning as it improves their ability to predict and satisfy demand. In return, the shipper receives a price reduction. Primarily, this changes the way in which income is generated but it also affects the customer relationship, core activities and key resources.