Take fast, profitable action on connected data from web, mobile and stores with one view of customer, product and profit. Gain insights with data visualizations, and be alerted to opportunities that help drive decision making with each click and every brick. Below are a few sample use cases for retail analytics. See how retailers leverage DynamicAction in the digital channel, in their stores, and across their enterprises.
DynamicAction for eCommerce
Slow Selling Products: Determine which of your products are selling slowly relative to their peers, and be alerted to prioritized action candidates such as as increasing exposure, reviewing page content, replenishing stock, or adjusting the price.
Personalization and Product Recommendations: Reduce product recommendation and/or personalization exposure for products with low profit-per-view, high return rates, low SKU Availability, or low stock cover in an effort to generate higher profit from page real estate.
Efficient Marketing: Pause digital campaigns that promote products that are out of stock, highly fragmented, poorly reviewed, returned frequently or likely to sell through without additional exposure. Choose the most profitable products and product combinations to include in promotional emails.
Onsite search: Find high converting, high profit-per-view products without enough exposure. Adjust site search and sort order to provide additional views.
DynamicAction for Omnichannel
Customer Targeting: Leverage omnichannel view of customer activity to contact customers based on channel isolation, promo usage, repurchase risk, profit history, return history, web engagement and more.
Ship-from-Store Profitably: Adjust Ship from Store procedures and/or ship points given shipping profit and shipment timing.
Stock Allocation: Adjust allocation to stores given online demand signals (sales, views), conversion, reviews and returns. Adjust allocation to warehouses given store demand signals.
Sales Velocity/Pricing Disconnect: Quickly identify store-web price mismatches and sales challenges resulting from competitive pricing discrepancies.
Tech Stack Optimization: Export transformed data and new metrics to your technology platforms for semi-automated optimization. For example: send the list of negative lifetime value customers to your email platform to suppress them from your promotional campaigns.
DynamicAction for Stores
Stock Redistribution: Identify over and understocked SKUs by store and target destinations to which the product could be moved to sell faster. Establish program to move overstocked SKUs to higher selling velocity locations.
Optimize Merchandising through Affinities: Identify category, brand and product affinities from store and web purchases. Focus on strong support and high lift combinations, and test placing high cover products with high support and lift close to each other in stores.
Promotions: Test promotion type, creative and timing with a small subset online; then roll out to stores. Measure the effect on revenue and profit in the store channel.
Pricing: Identify slow selling, in-store products and compare price points to your web prices and those of competitors (including wholesale channels). Test in-store price decreases for items where in-store pricing is higher than web or competition.