Opportunities in data-driven servitization in an industrial B2B scenario

2018-04-16 08:17:00
When reviewing the global context that motivates servitization strategies, we have already talked about the differentiation of the value proposition as one of the main motivations in order to consolidate two important opportunities: differentiation from a competition that bases its positioning on lowering the cost of the product and focus on providing more value around the product, and customer loyalty and strengthening a relationship as an ally (thanks to this combination of product surrounded by services) generating an entry barrier to competitors and resulting in a greater recurrence of income. This new value proposition based on servitization is based on aspects such as greater transparency in the operation of the asset, a greater capacity to optimize the processes where it is used by being able to monitor how the asset is really being used and its different components, or a greater guarantee of availability thanks to analytical models that facilitate predictive maintenance to be able to act before failures occur.

Another fundamental aspect to highlight is how the deployment of data-based servitization facilitates a more efficient management of the digital intermediation with customers. Just as in contexts of online services outside of industrial scenarios, it is the web platforms themselves that make it possible to manage this intermediation and learn from what users demand, in an industrial B2B scenario it is this data-based servitization that facilitates a greater knowledge of customers (and how they use the product, which is very valuable feedback to optimize its design) and a closer relationship with them. In fact, and continuing with the analogy with other digitized scenarios, if we think about how the development of IoT technologies and their synergies with data processing facilitated the transfer of data analytics solutions from the world of online services to the world of physical assets, it is precisely this same type of translation that is also happening with the ability that servitization gives us to capture knowledge about how customers use a physical product.

If we think, for example, of the smartphone market, this is something that has already been done to identify what functionalities, components, etc. which ones are used the most (and how they are used), which ones are dispensable for future versions of the device (let’s think about how the infrared emitter, after years present in mobile phones, ended up disappearing from the main devices), etc. ., thanks to the fact that all this information is captured and centralized through the connectivity capabilities of the phone. This is the type of decision that a manufacturer of industrial assets can make about its product thanks to data-based servitization: analyze and learn more about the real needs of customers to fine-tune what is offered to them and that best fits what they are looking for. what they are really looking for. In other words, understanding which functions or components of your product are the ones that really add value to the customer (and how they do so) and thus optimize its design in future versions, doing without some things to enhance others.