Have you already searched for something on Google today or wished a friend a happy birthday on Facebook? Perhaps you have already made provisions for the weekend and ordered something quickly from Amazon. It's all so easy these days on your Apple smartphone or Microsoft laptop. Google, Facebook, Amazon, Apple, Microsoft. They all belong to the top ten most valuable companies in the world. Ranks two to eight are occupied by corporations that no longer see their business model in the analogue world. But what is the recipe for success for these companies? How were Google and Facebook able to become the most successful companies of our time without us having to pay for them at all? The answer is: data.
Today, data-driven business models are in many cases real success models. However, they are by no means a trend that only emerged in the 21st century. For a long time now, companies have been building on the ability to evaluate, analyse and present data. Examples of this are credit agencies whose business model focuses on evaluating company data from other companies and thus, for example, classifying creditworthiness. This is worth cash to many companies. However, digital platforms have taken such business models to a new level.
It has never been so easy to convert data into information. In this way, very specific insights can be gained that can lead to highly individualised products.
Did you know, for example, that a study found that Instagram images whose background is mainly blue receive 24 percent more likes than those whose background is red? This is a very simple example, but you can find as many more as you like to prove the usefulness of data analysis.
Now the question can be asked: What does a mechanical engineering company in the Ruhr area care whether Amazon knows which customers order which product in which colour? The answer is quite simple and can be answered by looking back over the past decades. No company operating in the manufacturing sector simply lets its production processes run. It is analysed and optimised. Deficits are identified and potentials are revealed. Where can supply chains still be improved? What time frame can still be extended? Can the production line be further accelerated by rearranging the machines?
Process optimisation has always been pursued. Now that many processes have been digitised, this optimisation is increasingly taking place digitally. However, in many cases the data that is generated in the process is still largely ignored. Sales employees write countless e-mails every day. Some of them end up in a successful business deal, others do not. Is it not possible to analyse which e-mails were particularly successful and which were not? And could this knowledge not be passed on to other companies.
Every company has countless pieces of information. For this, meaningful evaluation methods are needed. In the digital world these are usually only a few clicks away. And the larger the basic quantity to be evaluated, the better the results. In this way, not only direct but also indirect effects can be determined perspectively.
A keyword that comes up again and again is "Big Data". Especially in the manufacturing sector, however, there are also various other success factors that must be taken into account along the supply chain in order to design processes optimally. Problems can be of different nature - whether strategic, functional or operational. Whether suppliers or customers, production or warehouse - there is potential for optimisation in many places. With our white paper "Supply Chain Excellence" we support companies in gaining an overview of this and in analysing and evaluating processes in a meaningful way. Data play a major role, especially with regard to the success factors transparency and transformation. Through many years of experience in big-data projects in industry and logistics and the view from the outside, extensive insights can be gained.
On all these points, the aim is often also to better understand the other person and his or her decisions and to anticipate how he or she will act. Facebook can tell companies which advertisements are particularly successful with which target group, and that is what makes it so attractive. Resources can be used sensibly with greater certainty. Scattering losses are minimised, risks are reduced. In comparison, which company would rely on a platform that does not offer such potential for analysis? Wouldn't you also like to make more information-based decisions?
Often such business models have yet another added value. Due to their strict recipient orientation, they are more innovative and closer to the pulse of the times. Current products and processes are not perceived as unchangeable, but are in a constant state of change, depending on the behaviour of the customers. A company like Spotify has revolutionised the entire music market. Very few people today still listen to music on CDs, not only on the road but also at home. The situation is similar with Netflix or YouTube. Here too, linear television programmes have been replaced. This works because these companies have closely analysed their users. Netflix, for example, uses different preview images for films and series for different users because they have realised that this can increase activity. In addition, a questionnaire about their own preferences is included in the registration process. The suggestions we receive are then in most cases a very good fit and we click directly on the next video. This way we stay on the platform. And before the next YouTube video, an advertisement will be placed which, in the best case, will address us in an equally personalised way. Why not ask the customer about his preferences and use this data to adapt our own (proactively offered) products?
You see: Such business models can be enormously successful without - like YouTube, for example - charging a single cent. Certainly not every company needs to shift its business centre to the internet, especially since not every business model can be thought of as a platform. But it proves how extensive the possibilities of such data-driven business models are. And, as always, the learning should come from adapting those components for one's own work which fit into one's own model and which promise added value for it. In many cases this is possible.
And if you have already googled new winter boots this morning, just take a look at what other advertising you will see during the day. Perhaps this will then provide the impulse purchase. Thanks to data-driven business models.