Introduction
It is relatively easy to find customer profiles that generate a disproportionately big part of your bottom line, i.e. high-value customers. The natural next thing would be to gather deep sociological insights about their needs and motivations, to keep them happy and possibly recreate your success. To improve services offered to high-value customers, combine retail data with other kinds of big data (banking, health, mobility, insurance, real-estate, social media and media use, etc.). Creative cross-industry data combinations are a key to building lucrative, customized and seamless service packages that could offer high-value customers considerable savings.
A high-value customer is someone who spends a lot and has no qualms about sharing their data. People who have less to spend, have issues with sharing data or have problematic behavioral patterns (think health data, grocery data and insurance services) could end up with second-class service experiences and would also have to pay relatively higher fees. Personalized services based on consumer rankings will add to the overall polarization of people and further differentiate their everyday experiences. Severe data breaches will likely make consumers rethink the benefits of allowing such deep cross-industrial analytics.
Next steps
Data rules, but e-commerce platforms need to create clear guidelines for getting customer consent, processing data and ranking customers. EU-level regulation might motivate to manage and serve customers fairly.
Opportunities
- Design data-driven products and services, with security and privacy in mind
- Rethink data ownership – how to share data in the new ecosystems and between partnerships and how can the customer be in control of their data.
