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Use Case: Analyze Customer Returns For Retail Stores Chain

In a real business scenario, once the customers received their placed order, they have an option to return the order in the retail chain. This This use case demonstrates how you can use SnapLogic ELT Snap Pack to analyze customer returns for a retail stores chain. This example demonstrates the demonstrates the process to find customers who have returned items worth 20% more often than the average customer returns for a store in a given state for a given year.

Problem

For a given retail chain, we need data on customers who return more than 20% of the average customer merchandise returns for a given store per year. In the retail industry, the dataset is typically large, running into several terabytes. The The challenge in dealing with such large datasets is grouping the data.

Solution

This use case is based on the the TPC Benchmark DS^TM DSTM (TPC-DS) specification, a widely used decision support industry benchmark that evaluates the performance of big data processing engines. TPC-DS models the Data Science functions that retailers use to capture the longer-running complex queries of Data Science. TPC TPC-DS models the Data Science functions of a retailer. This This model captures the captures the longer-running complex queries of Data Science and represents and represents samples of data sets - customers, store, store_returns, and data_dim.

There is a fact dataset: STORE_RETURNS and 3 dimensions datasets: DATE_DIM, STORES, and CUSTOMERS.

ELT Solution Pipeline

To solve this big data problem, we have designed Pipelines that run on Snowflake and Microsoft Azure Databricks Lakehouse Platform

Example Pipelines - Snowflake

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This Pipeline does not require source tables as they are created on Snowflake on the fly from SQL. An output target tables are also created on Snowflake. The Pipeline writes from the Snowflake database to the Snowflake database. Users need not have Snowflake accounts. However, they require SQL require SQL experience.