Retail

Real-time omnichannel ecommerce recommendations

Personalize product offerings: Recommend products based on a customer’s browsing patterns, past purchases, and preferences, ensuring relevance during their shopping journey.
Optimize promotional strategies: Deliver timely discounts and special offers tailored to individual customer behavior, increasing conversion rates and engagement.
Enhance product discovery: Suggest product categories or complementary items based on real-time browsing activity and purchase history, encouraging customers to explore more offerings.

Use case summary

Retailers can leverage Data Cloud Sub-Second Real-Time to deliver personalized product recommendations, optimize promotions, and enhance product discovery, significantly boosting sales and customer satisfaction.

Industry

Retail

Salesforce products used

Data Sources Used

Browsing History

Customer Profiles

Wishlist Data

Email Engagement Data

Social Media Interaction Data

Review and Feedback Data

Point of Sale (POS) Data

Apply Insights and Predictions

By bringing together the data sources referenced in this use case, teams can build calculated insights or run predictive models with Data Cloud that will allow them to make smarter decisions or power new automations.

Personalization Insight	Data Cloud uses browsing, purchase, and search history to build a dynamic profile of customer preferences, allowing it to recommend the most relevant products during each session. For example, if a customer has frequently browsed shoes but not purchased, the system might highlight related shoe brands or accessories like socks and shoe care products to enhance the chances of conversion.
Promotional Optimization Insight	Based on the customer’s past engagement with offers and seasonal trends, Data Cloud suggests targeted promotions to increase the likelihood of purchase. For instance, if a customer typically purchases during end-of-season sales, the system can present early access to such promotions, incentivizing repeat behavior.
Enhanced Product Discovery Insight	Data Cloud analyzes customers' in-session behavior, such as time spent on particular product categories, and dynamically adjusts the content displayed. For instance, if a customer is exploring a new category like electronics, the system might recommend popular or trending items in that category, thereby enhancing the product discovery experience and increasing basket size.

What’s the impact?

Improve Conversion Rates

Improve Average Order Value (AOV)

Improved Customer Satisfaction (CSAT)

Improve Engagement

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