Engineering Data Solutions for Retail Customer Behavior Analysis
DOI:
https://doi.org/10.15680/IJCTECE.2022.0506019Keywords:
Retail Big Data Analytics, Customer Data Engineering, Retail Data Governance, Data Quality Management, Omnichannel Retail Data, E-Commerce Transaction Logs, Supply Chain Data Integration, Customer Service Analytics, Marketing Data Pipelines, Retail Data Lifecycle, Data Ingestion And Processing, Retail Data Modeling, Analytics-Oriented Data Storage, Pipeline Orchestration, Feature Engineering In Retail, Customer Identity Data, Customer Behavioral Data, Customer Journey Analytics, Personalization And Segmentation, Data-Driven Retail DecisionsAbstract
The retail sector is one of the most data-intensive industries in the world. Retailers collect a wide variety of data from various sources across the enterprise, from the e-commerce transaction logs to supply chain activities, customer service conversations, and marketing campaigns. The availability of big data in retail is a double-edged sword—while the amount of data presents opportunities, the effectiveness of customer analytics initiatives also relies heavily on data quality and governance. Reliable, governed, and proactive customer data engineering is essential for operational and analytical workloads across the retail ecosystem since analytics-based decisions undertaken by marketers, customer service, and product development teams impact customer experience and ultimately the bottom line. This requires an engineering approach to customer data and a focus on how it is governed, processed, segmented, transformed into features, and served to data scientists, visualization tools, and digital platforms
The data landscape within retail can be categorized based on the stages of the data life cycle starting from data sources and ingest to data quality and governance, data modeling for Analytics, data storage, pipeline orchestration, and finally feature engineering of customer behavior catered for modeling and analysis. Customer-related data used for data sciences use cases can be broadly divided into two categories: identity data responsible for a single customer’s identity and behavioral data that enables analysis of the customers’ actions, including visits, purchases, interactions, and conversations, over time and in response to various marketing push messages
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