1. Data privacy-
Data privacy laws are already in place, in most countries. Firms need to be more transparent when it comes to what is tracked and where this data is used.
Support for third party cookies is being removed on most browsers. This poses another challenge to achieve hyper personalization.
3. Large volumes of data-
A lot of powerful Hyper-personalization implementations are based on machine learning models. These ML models require lot of data to generate valuable insights and accurate predictions.
4. Different sources of data-
Data can be structured, unstructured, streaming or batch. Working with different types of data, and extracting relevant information that can be used for hyper personalization is another challenge.
Examples of structured data is employee table which has information about employee name, id, the department they work and so on. Ex of unstructured data is media, iot sensor data, emails etc. Ex of streaming data is click stream data from apps, websites, iots and sensors and finally ex of batch data is payroll and billing system data, offline stores sales data and so on
5. Customer centric focus-
Moving from product centric focus to customer centric, is another challenge. Most of the organizations focus on selling their products rather than fulfilling customer needs. In general, smart devices, rapidly evolving customer experience, and real time processing of big data, has enabled hyper-personalization implementation. However businesses need to change their approach to make effective use of Hyper personalization.