You work in the IT department for an online retailer. Your management team wants to understand and categorize decreased conversion rates.
The online sales application team reports that there has been an increase in the number of shoppers and in the number of shopping carts with large value items. However, the sales bottom line does not reflect that. In fact, the marketing team reports a decrease in conversion rates. These changes are happening fast, and the amount of data is daunting. The management team wants answers.
You have an upstream Java transformation web service that provides the shopping cart data in JSON, within a string field. However, you are unsure of the specific data structure. The sales team is able to provide a sample file with the JSON data.
The sales and marketing teams have some ideas about what might be causing these issues, but they want actual shopping data to confirm their theories. The teams do not have the resources or expertise to read and analyze raw JSON data.
With the sample JSON data, you are able to use CLAIRE®
Intelligent Structure Discovery
to parse the shopping cart data, creating a complex data type definition. Now that you know the data structure, you create a midstream mapping to parse the data, and categorize the shopping cart information. You can use the categorized data in downstream transformations, allowing you to further refine the information for the sales and marketing teams.
When the teams see the details you provide regarding the abandoned shopping carts, they realize what they need to do to reduce the number of lookers, and turn them into buyers.