Customer Facing Analytics

Customer Facing Analytics

With more data being collected on a day to day basis and data being treated as an asset, analytics is moving beyond internal use. This has resulted in a growing trend towards analytics being delivered to customers - both internal and external, in the form of customer facing analytics. While traditional BI enables ad hoc analysis and self service, customer facing analytics provides a packaged experience to the end user. Customer facing analytics provides an opportunity for the data producer (Enterprise that owns the data) and data consumer (end user or customer)  to interact through data. In customer facing analytics, data models and visualizations are defined and delivered to the end user and enable interactive analysis within the set boundaries of a packaged experience. Customer facing analytics can take various shapes and forms and can be delivered as interactive dashboards, web or mobile apps.  With this shift in analytics workloads, towards customer facing analytics, there are various aspects of analytics that become significant. 

Key Considerations for delivering Customer facing analytics:

  1. Define customer requirements in terms of specific insights and the data required to deliver these insights.
  2. Create flexible data models that are prescriptive, yet extensible to address current and future customer needs.
  3. Deliver analytics in various shapes and forms as needed by the customer.
  4. Define clear expectations on freshness of data, response times. Performance is a critical element of customer facing analytics.
  5. Ensure scalability, availability and provide secure access to insights.

Customer facing analytics might need service levels to be defined to ensure that expectations are set properly. Service levels can be used to agree on freshness of data, query response times, security of access etc. Examples of customer facing analytics vary depending on the end customer. A personalized dashboard in a gaming app or activity reporting in a fitness tracker or an order and inventory tracking application available to suppliers are all examples of customer facing analytics.

Join our virtual data engineering meetup where data pros from Canva, Bolt, PayU, and Sublytics will tell us all about their cloud data warehouses.