With the rise of Linked Data technologies, there come several new approaches into play for the improvement of customer experience across all digital channels of a company. All of these methodologies can be subsumed under the term “the connected customer”.
These are interesting not only for retailers operating a web shop, but also for enterprises seeking for new ways to develop tailor-made customer services and to increase customer retention.
Linked Data methodologies can help to improve several measurements alongside a typical customer experience lifecycle.
- Personalized access to information, e.g. to technical documentation
- Cross-selling through a better contextualization of product information
- Semantically enhanced help desk, user forums and self service platforms
- Better ways to understand and interpret a customer intention by use of enterprise vocabularies
- More dynamic management of complex multi-channel websites through a better cost-effectiveness
- More precise methods for data analytics, e.g. to allow marketers to better target campaigns and content to the user’s preferences
- Enhanced search experience at aggregators like Google through the use of microdata and schema.org
In the center of this approach, knowledge graphs work like a ‘linking machine’. Based on standards-based semantic models, business entities are getting linked in a most dynamic way. Those graphs go beyond the power of social graphs. While social graphs are focused on people only, are knowledge graphs connecting all kinds of relevant business objects to each other.
When customers and their behaviours are represented in a knowledge model, Linked data technologies try to preserve as much semantics as possible. By these means they are able to complement other approaches for big data analytics, which rather tend to flatten out the data model behind business entities.