TABLE OF CONTENTS
Business Challenge
A prominent healthy food snack brand, referred to as SoFake for confidentiality, faced a critical business challenge centered on optimizing revenue across its online channels.
Being a digital-native brand, 80% of their sales occurs through online platforms that inherently generate tons of data around sales, margin, product availability, pricing.
SoFake strongly recognized the potential of leveraging the available data to make informed business decisions. The objective was to figure out how primary sales varied across platforms and identify the underlying driving forces such as secondary sales or PO fill rates. They also wanted to deepdive into factors like availability, page visibility, and competition benchmarking data to get a better hang of their influence on sales.
However, the challenge was that this valuable data was scattered across different platforms and tools. SoFake struggled to get a unified perspective on both primary sales and secondary sales and was not able to comprehend the influence of different internal and external factors on overall revenue.
SoFake’s existing approach and challenges
SoFake heavily relied on internal teams manually collecting, cleaning, and assimilating data from different sources. This approach consumed significant time and resources, diverting attention from strategic decision-making to data manipulation.
Few challenges they grappled with on a daily basis:
- Data sources - SoFake collected data from multiple sources, such as primary sales data from internal invoicing tools like Unicommerce and AVC, secondary sales reports shared by marketplace platforms, and the POs raised by marketplaces. The data sharing methods varied across platforms and tools, including APIs, direct downloads, or email attachments.
- Data format - The disparate data sets presented inherent differences in structure, nomenclature, and granularity, making it difficult to extract meaningful insights.
- Benchmarking data - Platforms only shared information related to SoFake’s performance. However, benchmarking against competitors was critical for SoFake to have a proper understanding of their relative performance.
GobbleCube’s solution
SoFake partnered with GobbleCube to address these issues. GobbleCube’s key solutions comprised:
- Creating a data repo by building data-pipelines that streamline data collection from different sources
- Automating cleaning and mapping of data to integrate disparate data sources into a unified Single Source of Truth (SSOT)
- Triangulating actionable insights from SSOT for SoFake to act upon
High-level solution architecture
Phase 1: Collection, cleaning, and modeling of data from disparate sources to SSOT
GobbleCube developed connectors to automate data ingestion from diverse sources, both external (e.g., Blinkit, Zepto, Amazon) and internal (e.g., invoicing tools, finance tools, Google Sheets). These connectors were designed to adapt to source data changes, ensuring ongoing data accuracy and updates.
GobbleCube then implemented their pre-built playbook to standardize the disparate data, ensuring consistent structure, nomenclature, and granularity.As the next step, GobbleCube modeled the cleaned raw data into optimized bronze, silver and gold tables, creating a Single Source of Truth (SSOT) that served as a foundation for subsequent analysis and powered end-user views.
Phase 2: Enrichment of data
GobbleCube helped set up scraping processes (through third-party vendors) to collect location-level granular data on availability, price competitiveness for both internal SKUs and competitors.
Phase 3: Triangulating data into actionable insights
GobbleCube leveraged revenue, availability, and other framework playbooks to triangulate the modeled data. This helped to connect the dots between the metrics and their influencing factors. For example, how availability on a particular platform impacts the secondary sales, which in-turn affects primary sales.
Phase 4: User-friendly data visualization
GobbleCube’s visually-rich presentation layer, purpose-built for brands, provided a comprehensive view of key movements in metrics and their root-causes. This crisp flow of insights and byte-sized actionables enabled SoFake’s team to identify and act on anomalies faster.
Business outcomes
By implementing GobbleCube, SoFake was able to:
- Build a comprehensive SSOT of all their data spread across platforms and tools
- Measure their performance on both absolute and relative basis through the locality-level enriched data
- Visualize their entire business from a single vantage point
- Establish impact of various factors on their business metrics
As a result, SoFake was able to make informed business decisions that helped them optimize revenue while minimizing cost.
If this sounds interesting, then give us a shout. We’d be happy to chat!
