Digital Marketing Data Pipeline for Apparel Retailer

Organization

Our client is a renowned retail apparel chain headquartered in Texas, operating over 1,500 stores across the US and Canada. As a leader in the fashion retail industry, the company has strategically acquired several prominent market players as subsidiaries, further expanding its market presence. Backed by a robust supply chain and warehouse network, their team of approximately 22,500 employees provides a high-touch, personalized shopping experience, driving loyalty across all their brands.

Challenge

Our client sought to streamline data-sharing processes with multiple digital marketing vendors, including Cheetah Digital, Adnet, Starcom, Critio, Rakuten, ShopperTrak, and RTB Creative Stats, while receiving vendor responses to analyze campaign expenses and lead generation. Each vendor required data in different formats and specifications, necessitating the extraction of data from various sources, including sales, customer master, product data, and campaign details.

Additionally, the client required integration of Google Analytics data into their digital marketing data mart to enable deeper insights into customer behavior. This data was essential for:

• Performing in-depth analysis to better understand customer behavior and address gaps in the customer journey.
• Enhancing customer experience by resolving journey leakages.
• Assessing the impact of marketing efforts on website traffic and app performance.

Executive leadership also required the publication of key metrics to support informed decision-making and strategy development.

Techincal Solution

XTIVIA conducted a comprehensive analysis of data requirements, including attributes, formats, and frequencies, and profiled the data to identify quality issues. Collaborating with source teams, we addressed as many data quality issues as possible at the source level. ETL jobs were developed to ingest the data into the data lake. We also worked with vendors to establish agreements on response timelines for enriching and returning the data, ensuring that enriched data was seamlessly integrated into the marketing tables while avoiding duplicate customer data submissions to vendors.

Key Implementations:

• Google Analytics Integration: Defined the schema and configured options to export data from the Google Analytics account, storing it in GCP. Raw files from Google Analytics were loaded into Snowflake stages, where data flattening, transformations, and loading into the data mart were performed. Common dimensions, such as customer, product, store, campaign, and vendor, were managed using surrogate and reference keys. Each customer record now includes key metrics such as top purchases, page visits, RFM (Recency, Frequency, Monetary), loyalty points, transaction counts, and total discounts.
• Flexible Data Pipelines: Developed data pipelines for both vendor-bound and vendor-returned data using a generic framework, allowing easy onboarding of new vendors or discontinuation of existing ones.
• Automation and Alerts: Implemented automated processes to notify load statistics and alert teams if expected data was not sent or received.

This solution streamlined data management and marketing operations, enabling enriched customer insights and improving collaboration with vendors.

BUSINESS RESULT

• Adding new vendors and deleting existing ones is easier because of the automated, flexible, and generic pipeline.
• Informed decision-making due to the availability of KPIs for executives.
• Better understanding of customer behavior.

KEYWORDS
Digital Marketing, Customer Behavior Analysis

SOFTWARE
Snowflake, HVR, SQL, Python, Google Analytics, Tableau

HARDWARE
AWS, Unix, Windows, Snowflake

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