Built a unified omnichannel retail analytics platform with ML demand forecasting that improved forecast accuracy from 62% to 91% and reduced stockouts 58% across 120 locations.
Retail in the Dark
Modern retail success depends on understanding customer behavior across every channel—in-store, online, mobile, and social—and using that understanding to stock the right products in the right locations at the right time. RetailIQ Fashion Group operated 120 stores across 28 states along with a growing e-commerce operation, but their data infrastructure reflected a history of incremental technology additions rather than strategic planning. The POS system, e-commerce platform, inventory management system, customer loyalty program, and marketing automation tool each maintained their own database with no systematic integration. Demand forecasting relied on spreadsheet-based models maintained by a single analyst, achieving only 62% accuracy at the SKU-store level—meaning nearly 4 in 10 forecasts were materially wrong, leading to both stockouts of popular items and markdowns on excess inventory. The e-commerce team could not see in-store purchase history for online customers, and store associates had no visibility into customers' online browsing behavior, making personalized service impossible. Inventory allocation to stores was based on historical sales averages that could not account for local events, weather patterns, demographic shifts, or competitive changes. Markdowns consumed 18% of gross margin as excess inventory accumulated from inaccurate forecasting and one-size-fits-all assortment decisions. Customer churn was increasing as competitors with better data capabilities delivered more personalized shopping experiences. Management recognized that their competitors were investing heavily in data infrastructure and that the gap would only widen without a comprehensive analytics transformation.
- POS, e-commerce, inventory, loyalty, and marketing data existed in 5 unconnected databases
- Demand forecasting at 62% accuracy caused both stockouts of popular items and excess inventory
- No unified customer view across online and in-store channels prevented personalization
- Store-level assortment decisions based on chain-wide averages ignored local market differences
- Markdowns consuming 18% of gross margin from accumulated excess inventory
- Single analyst maintaining spreadsheet forecasting models with no scalability
Unified Retail Intelligence
We architected RetailIQ as a modern data platform built on Snowflake with dbt for transformation, creating a single source of truth that unifies data from every retail channel. Apache Airflow orchestrates over 200 data pipelines that extract from POS systems, the e-commerce platform, inventory management, loyalty programs, foot traffic counters, weather APIs, and local event calendars, loading into Snowflake where dbt models transform raw data into analytics-ready datasets. The demand forecasting engine uses a TensorFlow-based ensemble model that incorporates historical sales, seasonal patterns, promotional calendars, weather forecasts, local events, social media trend signals, and competitive intelligence to predict demand at the SKU-store-week level with 91% accuracy. Store-level assortment optimization analyzes local customer demographics, purchase patterns, and competitive positioning to recommend product mix adjustments for each location, moving beyond one-size-fits-all chain averages. The customer analytics module creates unified profiles by linking online and in-store identities through loyalty cards, email addresses, and payment methods, enabling true omnichannel journey analysis. A customer segmentation engine identifies high-value customers, at-risk segments, and cross-channel shoppers, feeding into personalized marketing campaigns and store associate clienteling tools. Looker-based dashboards provide self-service analytics for merchandising, store operations, marketing, and executive teams, with automated alerts for stockout risks, markdown recommendations, and customer churn indicators. Real-time inventory visibility across all channels enables ship-from-store fulfillment and accurate online availability promises.
- Snowflake data warehouse unifying POS, e-commerce, inventory, loyalty, and external data sources
- TensorFlow ensemble demand forecasting at SKU-store-week level achieving 91% accuracy
- Store-level assortment optimization based on local demographics and purchase patterns
- Unified customer profiles linking online and in-store identities for omnichannel journey analysis
- Customer segmentation feeding personalized marketing and store associate clienteling tools
- Looker dashboards with self-service analytics and automated stockout and markdown alerts
- Real-time inventory visibility enabling ship-from-store and accurate online availability
Our Approach
Data-Driven Retail Transformation
RetailIQ's analytics platform delivered transformative results across every dimension of the retail operation. Demand forecast accuracy jumped from 62% to 91% at the SKU-store-week level, enabling merchandising teams to make confident buying and allocation decisions. Stockout incidents decreased by 58%, directly recovering lost sales that had been driving customers to competitors. Inventory carrying costs dropped 23% as improved forecasting reduced both overstock and safety stock requirements. The unified customer view revealed that omnichannel shoppers—those who bought both online and in-store—spent 2.8x more than single-channel customers, prompting a strategic shift to encourage cross-channel engagement. Marketing campaigns using the new segmentation engine achieved 45% higher conversion rates than the previous batch-and-blast approach. Store-level assortment optimization identified opportunities to reallocate $12M in inventory from underperforming categories to locally relevant assortments, improving same-store sales by 8%.
Return on Investment
Technologies Used
Integrations
RetailIQ gave us a complete picture of our business for the first time. The demand forecasting alone paid for the entire platform in the first season through reduced markdowns and fewer stockouts.
Project Gallery
Lessons Learned
- Data quality remediation in source systems should happen in parallel with warehouse construction, not sequentially
- Store managers become the strongest advocates when they see local assortment recommendations that reflect their market knowledge
- Phased rollout delivering value at each stage maintains executive sponsorship better than a big-bang approach
Summary
Advenno built a unified omnichannel retail analytics platform on Snowflake with ML demand forecasting, store-level assortment optimization, and customer journey analytics for a 120-location fashion retailer.
Key Takeaways
- ML demand forecasting improved accuracy from 62% to 91% at SKU-store-week level
- Stockout incidents reduced 58% through predictive inventory allocation
- Unified customer profiles revealed omnichannel shoppers spend 2.8x more than single-channel
- Store-level assortment optimization reallocated $12M in inventory to locally relevant categories
- Marketing segmentation improved campaign conversion rates by 45%
Frequently Asked Questions
Key Terms
- SKU-Store-Week Forecasting
- Demand prediction at the most granular actionable level—forecasting sales of each individual product (SKU) at each store location for each week in the planning horizon.
- Assortment Optimization
- The analytical process of determining which products to carry in each store location based on local customer preferences, competitive positioning, and space constraints to maximize sales and margin.
Facts & Statistics
Sources & Citations
Is this the kind of outcome you are aiming for?
AI projects are shaped by data quality, production architecture and the specific decisions you need to automate. Tell us about your use case and we will tell you what we would approach differently.
Share Your AI Goals
