Enabling Computer Vision Applications With the Data Lakehouse
The potential for computer vision applications to transform retail and manufacturing operations, as explored in the blog Tackle Unseen Quality, Operations and Safety Challenges with Lakehouse enabled Computer Vision, can not be overstated. That said, numerous technical challenges prevent organizations from realizing this potential. In this first introductory installment of our multi-part technical series on...
Tackle Unseen Quality, Operations and Safety Challenges With Lakehouse Enabled Computer Vision
Globally, out-of-stocks cost retailers an estimated $1T in lost sales. An estimated 20% of these losses are due to phantom inventory, the misreporting of product units actually on-hand. Despite technical advances in inventory management software and processes, the truth is that most retailers still struggle to report accurate unit counts without employees manually performing a...
Real-time Point-of-Sale Analytics With a Data Lakehouse
Disruptions in the supply chain – from reduced product supply and diminished warehouse capacity – coupled with rapidly shifting consumer expectations for seamless consumer demands in the new normal. In this blog, we'll address the need for real-time data in retail, and how to overcome the challenges of moving real-time streaming of point-of-sale data at...
Improving On-Shelf Availability for Items With AI Out of Stock Modeling
This post was written in collaboration with Databricks partner Tredence. We thank Rich Williams, Vice President Data Engineering, and Morgan Seybert, Chief Business Officer, of Tredence for their contributions. Retailers are missing out on nearly $1 trillion in global sales because they don’t have on-hand what customers want to buy in their stores. Adding...
How to Build a Scalable Wide and Deep Product Recommender
Download the notebooks referenced throughout this article. I have a favorite coffee shop I’ve been visiting for years. When I walk in, the barista knows me by name and asks if I’d like my usual drink. Most of the time, the answer is “yes”, but every now and then, I see they have some seasonal...
Jump Start Your Data Projects with Pre-Built Solution Accelerators
Deliver value faster. We hear this theme in nearly every executive discussion with customers. Data teams and data leaders need to deliver value in weeks, not months or years. The business climate is volatile, and they don’t have the luxury of long project timelines to deliver data and analytic capabilities designed to drive business value, such...
Machine Learning-based Item Matching for Retailers and Brands
Item matching is a core function in online marketplaces. To ensure an optimized customer experience, retailers compare new and updated product information against existing listings to ensure consistency and avoid duplication. Online retailers may also compare their listings with those of their competitors to identify differences in price and inventory. Suppliers making products available across...
Building Forward-Looking Intelligence With External Data
This post was written in collaboration with the Foursquare data team. We thank co-author Javier Soliz, sales engineer specializing in data engineering and geospatial analysis at Foursquare, for his contribution. “In an interlocked global economy, triggering events can quickly set off a chain reaction,” wrote Boston Consulting Group in early 2020 as the world...
Fine-Grained Time Series Forecasting at Scale With Facebook Prophet and Apache Spark: Updated for Spark 3
Advances in time series forecasting are enabling retailers to generate more reliable demand forecasts. The challenge now is to produce these forecasts in a timely manner and at a level of granularity that allows the business to make precise adjustments to product inventories. Leveraging Apache Spark™ and Facebook Prophet, more and more enterprises facing these...
Segmentation in the Age of Personalization
Quick link to notebooks referenced through this post. Personalization is heralded as the gold standard of customer engagement. Organizations successfully personalizing their digital experiences are cited as driving 5 to 15% higher revenues and 10 to 30% greater returns on their marketing spend. And now many customer experience leaders are beginning to extend personalization to...
