Inventory availability and promotions continue to influence customer decisions. As new customer journeys and shopping experiences emerge, execution is everything. Consumers won’t tolerate out-of-stock inventory—63% of consumers confirmed they will try another brand rather than wait for a restock. Retailers with more accurate forecasts and insight into their inventories will be able to quickly pivot to meet customer demand and shopping preferences, whether they’re shopping in store, buying online and picking up in store, opting for curbside pickup, or using other channels.
Consumers shop differently depending on what they’re buying. For example, they have different priorities when shopping for gifts than when shopping for seasonal items: Sometimes price is everything; sometimes it’s no object. Retailers can now understand how shoppers respond to every item at a localized level, enabling them to factor the effects of promotions, seasonality, and weather into their forecasts.
Additionally, retailers can increase personalization by targeting customers based on their real-time needs. Customers are more likely to accept offers that have been targeted to them based on an understanding of their behavior, how they shop, and the services they subscribe to, increasing offer acceptance rates and, ultimately, revenue. Conversely, pushing untargeted cross-sell or upsell offers can lead to customer fatigue and ignored or disabled notifications.
Having the right inventory available in the right place to quickly fulfill customer demand, no matter when and where a purchase is made, will help retailers overcome the challenges and exploit the opportunities described above.
Retailers can use advanced analytics and machine learning to identify products and services that are relevant to a given customer. They can then push these products and services to the customer through the appropriate channel at the appropriate time.
Let’s take a closer look at how Oracle Data Platform can help retailers improve forecast accuracy, simplify planning, and optimize inventory.
There are three main ways to inject data into an architecture to enable retailers to predict, sense, and shape demand.
Data persistence and processing is built on three (optionally four) components.
The analysis part is built on two technologies.
The forecast and corresponding pricing and promotional effects, paired with underlying costs and inventory availability, are the foundation for effective pricing and promotional decisions. Increase profitability and assortment flexibility with decreased inventory levels. Anticipate customer demand by maximizing the value of your data, applying analytics that draw from machine learning, artificial intelligence, and decision science disciplines to achieve the following:
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