Let us now discuss about few AI use cases in Data Cloud across different industries:
Financial use case
Financial service institutions can create custom cross-selling models that help recommend additional products or services to advisors based on real-time engagement data from clients. AI models can help in proactive fraudulent transaction detection and risk management. Using AI-powered customer service applications, chatbots, and virtual assistants powered by AI provide 24/7 customer support, answer queries, and assist with routine transactions, enhancing customer experiences while reducing operational costs.
Retail use case
A retailer can create a custom AI model to predict a customer’s likelihood to buy specific outfits based on engagement data, such as their purchase history or online activity. They can then deploy the model and use Einstein Studio to deliver hyper-personalized product recommendations to customers via the most appropriate channel, including email, a mobile app, or social media.
Automotive use case
An automotive brand can use predictive AI to determine when a car is likely to need maintenance and detect fraudulent insurance claims. Generative AI can then be used to personalize customer marketing campaigns based on individual needs and preferences.
Travel use case
A travel agency can connect CRM data, such as customer booking history and travel preferences, with non-CRM data like destination reviews and weather forecasts. They can then deploy predictive and generative AI models to predict which destinations appeal to specific customers and generate personalized email recommendations for them.
Field Service use case
Proactive service with Data Cloud combines a unified view of customers and their assets — including customer purchase details, service history, and IoT signals on machine health — all in real-time and at scale. With this unified data, Einstein can automatically schedule service appointments and job duration, the necessary parts or tools needed, and the right technician for the job.