By David Dorf-Oracle on Jul 20, 2015
Buzzwords abound in the tech world, and mobile, social, big data, and cloud are the four horsemen. Of those I think big data is least understood, but possibly the most powerful as exemplified by my favorite quote:
“Data is the new oil; analytics the new refinery.” -- unknown
The retail industry has always been dealing with large volumes of data; it's just that now the velocity and variety have dramatically increased. So the problem for retailers is two fold: first, how do they convert all that data into meaningful information? And second, how do they make the information relevant and actionable to employees?
Oracle Retail's answer comes in two families of cloud services: Oracle Retail Advanced Science and Oracle Retail Insights. Continuing our momentum in delivering cloud services, these products simplify implementations allowing retailers to realize value faster and with less IT effort.
Oracle Retail Advanced Science Engine
We have a long history of employing data scientists that cull through donated data to find useful insights that can be productized in algorithms. Over the years we've led the industry in areas like demand forecasting, markdown optimization, and localized assortments. To make it easier to bring new algorithms to market, we've built our Advanced Science Engine, a platform tuned for analyzing data at scale and exporting the results. Over the past six months, we've adjusted the architecture to provide this in a cloud deployment. The specific science being offered is packaged into three separate cloud services:
This solution provides insight into how store clustering best benefits the business. It helps to answer questions such as:
- What categories or merchandise classifications benefit most from clustering?
- At what level of product or location hierarchy should clusters be created?
- What product/location attributes should be leveraged?
The solution automatically selects the best clustering method depending on the clustering approach selected, and allows for what-if scenarios that help explore the data. Is scores clustering approaches for comparison, and recommend the optimal number of clusters. This fosters consumer-centric assortments that ultimately increase sales.
Customer Decision Trees & Demand Transference
CDTs map the decision process made by shoppers to purchase items. They help a retailer understand if they have the right variety of sizes, flavors, colors, etc. in their assortment.
Suppliers often provide consumer decision trees that help retailers understand the impact to assortment decisions based on "generic" consumers. But when a consumer is identified, they become a customer. Our solution focuses on customer decision trees, which reflect the actual customers in your stores. The solution even allows a side-by-side comparison of imported consumer decision trees alongside the calculated customer decision trees.
Using this science helps to reduce duplication in assortments, and prevent dropping unique items to which customers are attached. Demand transference helps forecast if customers will switch to alternatives so that the number of variety of items offered can be optimized. This solution often works closely with Category Management.
Assortment & Space Optimization
This solution helps to identify the optimal targeted assortment withing the space constraints. It understands shelves, pegboards, and freezers and incorporates various business rules and visual merchandising standards. The what-if analysis is very helpful in exploring options while seeking to maximize profits for any given space within the store or store cluster.
These cloud services can be used with existing planning products, or with Oracle Retail planning solutions such as Category Management Planning & Optimization, Macro Space Optimization, Retail Demand Forecasting, and Advanced Inventory Planning.