Oracle AI Apps for CX generates personalized product recommendations, either as a single product or in a carousel configuration, on a business's website. The data science models run on a combination of commerce data (not necessarily from a commerce platform), clickstream data, and trusted data from Oracle Data Cloud. This combination provides a more complete understanding of the customer and similar customers in that moment, to deliver unique, tailored experiences. Recommendations appear directly on the business commerce site for the most likely products of interest, associated promotions, and content, while factoring in the individual's purchase history, likelihood to purchase, affinity, and look-alike comparisons. Recommendations are calculated and surfaced in real time incorporating up-to-the-minute customer behavior data.

Multi-channel support allows for a seamless, consistent consumer experience across all digital channels of a business. As with all Oracle AI Apps, supervisory controls are a key design component that give users a way to interact with the data science and have some influence over the machine-generated outcomes. For Oracle AI Apps for CX, supervisory controls allow marketers and merchandisers to guide the data science algorithms based on specific business policies or objectives—for example, boosting the recommendations of products with excess inventory or enforcing brand exclusivity policies. Insights provide real-time understanding of how AI outcomes are benefiting the business.

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AI and data-driven contextual product recommendations appear in the body of an email at the time of opening to increase revenue and improve overall brand experience.

Offers and recommendations are calculated and shown in real time upon email opening, incorporating up-to-the-minute data regarding customer behavior. An embeddable code snippet allows Oracle AI Apps to work with Oracle Marketing Cloud as well as a variety of email service providers.

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Search results are tailored with personalized product recommendations to drive increased engagement, basket size, revenue, and repeat visits

The search experience includes AI-generated type-ahead entry, related search terms, and search engine optimization with results ranked according to potential relevancy for the individual customer. Blended product recommendations are shown on the search result page in ranked order at the top of the page.

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Data-driven, machine-learning algorithms help the digital marketer optimize and automate marketing program execution. The algorithms continuously adapt to changing customer interaction patterns to improve click-through and ultimately bring higher ROI and revenue from marketing spend.

Machine learning models automatically predict the best-performing mix of send-time, channel, and message for each customer based on their interaction history, profile, and content metadata to optimize for a defined business metric.

Supervisory controls allow marketers to influence the likelihood of specific decision outcomes as the programs are executing to meet specific business policies or objectives. For example, a marketer can boost the use of a specific channel or email template.

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Oracle AI Apps for CX taps into a customer's true interaction data to help hyper-target digital media campaigns. It links interactions from personalized product and promotion recommendations with Oracle BlueKai data attributes from Oracle Data Cloud. The resulting associations are automatically sent to Oracle Data Management Platform (DMP) for seamless retargeting and the identification of look-a-like consumers that match the client's evolving customer profile.

The connected audiences feature of Oracle AI Apps for CX helps digital marketers acquire and retarget consumers based on a customer's true interaction data, thereby improving the efficiency of customer acquisition strategies and the overall effectiveness of digital marketing campaigns.

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