Types of data to be considered for data analytics of eCommerce

Consumer-generated data: This data primarily consists of signals provided by consumers navigating mobile and online commerce sites, or shopping in the store. All the actions of these consumers are logged by the platform and are used to capture individual and global behaviors.

Consumer-provided data: In some instances, consumers provide explicit information that is important to the store. This can be a product or brand preference, a description of a customer segment they belong to, a style they prefer, etc. These signals are essentially information provided by the consumer to the retailer and should be picked up to influence personalization in all channels.

Retailer-provided data: The retailer’s marketing team will sometimes have additional information that is useful for personalization. This information can be provided to the personalization platform to enrich the customer profile and enhance the personalization across all channels. Examples of that information are customer relationship management (CRM) segments calculated by the marketing team, loyalty program tiers, preferred communication channels and any other information gathered by the retailer.

Third-party data: Occasionally, third-party data about consumers can be acquired by retailers and can be used to influence personalization. This is often consumer segmentation data provided by vendors who specialize in selling data. There are other third-party data sets that can be used to influence personalization. Examples include weather data or social network data. These data sets can also be uploaded in the platform to enhance the customer experience. Another example of a useful data set in this category is cross-device matching data.


Source

Popular posts from this blog

[Solved] Hive installation error: java.net.URISyntaxException: Relative path in absolute URI

Install Android SDK in centOs

Installing arules package (Association Rules) in R