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.
This image shows how Oracle Data Platform for retail can be used to support merchandising optimization and help retailers predict, sense, and shape demand. The platform includes the following five pillars:
Business record data comprises sales transactions, customer data, product data, returns transactions, suppliers, inventory, POS system data, revenue, and margin data.
Application data comes from ERP, SCM, CX, and WMS, Fusion SaaS, NetSuite, E-Business Suite, PeopleSoft, JD Edwards, SAP, Salesforce, and Workday applications.
Third-party data includes data from Oracle Data Cloud and social data.
Technical input data includes logs, web clicks, event streams, and beacons.
Batch ingestion uses OCI Data Integration, Oracle Data Integrator, and DB tools.
Bulk transfer uses OCI FastConnect, OCI Data Transfer, MFT, and OCI CLI.
Change data capture uses OCI GoldenGate.
Streaming ingest uses Kafka Connect.
All four capabilities connect unidirectionally into the serving data store and cloud storage within the Persist, Curate, Create pillar.
Additionally, streaming ingest is connected to stream processing within the Analyze, Learn, Predict pillar.
The serving data store uses Autonomous Data Warehouse and Exadata Cloud Service.
Managed Hadoop uses Oracle Big Data Service.
Cloud storage uses OCI Object Storage.
Batch processing uses OCI Data Flow.
Governance uses OCI Data Catalog.
These capabilities are connected within the pillar. Cloud storage is unidirectionally connected to the serving data store and managed Hadoop; it is also bidirectionally connected to batch processing.
Managed Hadoop is unidirectionally connected to the serving data store.
Two capabilities connect into the Analyze, Learn, Predict pillar: The serving data store connects to both the analytics and visualization capability and the data products, APIs capability, and the cloud storage capability connects to the machine learning capability.
Analytics and visualization uses Oracle Analytics Cloud, GraphStudio, and ISVs.
Data products, APIs uses OCI API Gateway and OCI Functions.
Machine learning uses OCI Data Science, Oracle ML, and Oracle ML Notebooks.
Streaming processing uses GoldenGate Stream Analytics and stream analytics from third parties.
People and partners comprise Historical Sales Analysis, Customer Segmentation, Promotion Impact Analysis, Pricing Impact Analysis.
Applications comprises Economic Indicators, Buying Behaviour, Real Time Demand Prediction, Inventory Prediction.
Models comprises Social Media Sentiment Analysis, Collaborative Forecasting and Demand Planning.
The three central pillars—Ingest, Transform; Persist, Curate, Create; and Analyze, Learn, Predict—are supported by infrastructure, network, security, and IAM.
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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