Retailers face mounting challenges as they work to maintain their market position while maximizing profitability. Pricing has long been one of the key levers available to retailers to drive sales, attract new customers, retain existing customers, and increase market share. However, over the last decade, it has gained even more significance as the rise of ecommerce and omnichannel shopping has made it easier than ever for consumers to buy—and compare prices across multiple retailers before they do.
Inaccurate, fragmented, or, sometimes, too much information can make it challenging for retailers to make the right pricing decisions. And while strategic pricing can be a key way retailers can differentiate themselves from their competitors, uninformed pricing decisions can have a significant negative impact on revenue, profitability, and customer satisfaction. Given the thin margins most retailers work with, determining the best price to maximize a product’s sales, profitability, and market share is critical. However, due to data challenges, many retailers must make pricing decisions without understanding the demand or the impact price changes will have on sales and margin, and they must make tens of thousands of these underinformed pricing decisions across their assortment.
The ability to bring diverse datasets together and apply advanced analytics and machine learning at scale enables retailers to broaden their pricing strategies to include competitive pricing, psychological pricing, promotional pricing, price bundling, and increasingly dynamic pricing; identify the correct pricing strategy (or combination of strategies); and optimize their pricing. They can then push the right products and services at the right price to the right customer through the appropriate channel at the appropriate time.
Let’s look at how Oracle Data Platform is built to help retailers identify the correct pricing for individual products, optimize that pricing throughout the product lifecycle, and understand the price-volume-market-time relationship.
There are three main ways to inject data into an architecture to enable retailers to optimize prices.
Data persistence and processing is built on three components.
The ability to analyze, learn, and predict is built on two technologies.
The right pricing strategies can increase revenue, profitability, market share, and customer satisfaction, but to develop them, retailers require real-time access to inventory levels, orders, demand, current pricing and promotions, and 360-degree customer views. By using a data platform that integrates data from multiple sources and supports advanced analytics, retailers can easily tailor their pricing strategies at the product level while aligning prices with corporate and category goals across all selling channels. This flexibility allows retailers to propose regular prices based on their target margins, competitive price alignment, or their preferred pricing relationship between different markets and maximize the value of their promotion and markdown strategies—all while providing a superior omnichannel customer journey by ensuring individual consumers experience pricing consistency at every touchpoint.
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