The Agentforce platform relies on accurate, up-to-date customer data to make intelligent decisions, whether it’s automation-related or AI-related use cases. As of now, much of the customer information is scattered across systems — CRM, martech stack, customer service records, transaction records — with no unified record for activation and segmentation of that data. This leads to missed opportunities for insights, personalization, and AI effectiveness.
Salesforce Data Cloud is designed to solve this challenge—the unification of customer data—so Agentforce can work with unified customer profiles and connected data points.
Why we need to look at Data Cloud and Agentforce together:
1. Data fragmentation
Customer data may be in silos — service history in one system, purchase records in another, and customer interaction in CRM. Service agents generally toggle between tools.
2. Lack of unified profiles
Without a single customer record, a CSM or service agent might not have all the touch points connected to the customer, how the customer is segmented within the organization, and what KPIs impact the customer.
3. We need both Predictive AI and Autonomous AI agents
We still have use cases that require Predictive Analytics to work alongside an Autonomous agent. Additionally, not all Autonomous agent use cases are purely AI-related. Many of them are Automations. We need to examine our use cases for Predictive AI, Generative AI, and Autonomous agents.
Context & Content:
AI is very good at content, but we need to give it context.
Context ensures the right content is chosen for the right person at the right moment. Agentforce gets context from Salesforce Data Cloud, so AI answers are not generic — they’re personalized and grounded in customers’ data.
- Content: making a hotel booking leveraging their website for platinum members
- Context: the customer became a platinum member and tried to book travel.
Cassette: A real use case of Renewals, Churn, and Customer Success.
Client Profile:
- Large hotel & resort chain with loyalty membership program.
- Operates globally, with thousands of members at varying tiers.
- Uses multiple systems for reservations, CRM, loyalty points, customer feedback, and marketing.
The Problem
The Data Cloud + Agentforce Solution
1. Data Unification:
- Data Cloud ingests booking history, loyalty status, service feedback, and engagement data into a single profile.
- Identity resolution ensures multiple bookings, accounts, and emails are linked to the same customer.
2. Churn Model creation for Propensity to Churn:
- Data Cloud Einstein Studio creates a Churn Model, taking into account the following:
- Customer tenure
- Renewal date
- Case data
- Account health
- Activity data
- Engagement data
- The Churn Model creates a propensity model focusing on the following:
- Will the customer churn
- When will it churn (within 6 months, 6-12 months, after 12 months)
- Confidence score for each probability
3. Personalized Agent Outreach:
- Agentforce uses this real-time context to surface the right content for agents to make offers and provide next best conversation data, to win back loyalty.
- Agents see full context: last stay, preferred destinations, typical booking patterns, past service complaints.
4. Segment and Activate:
- Segment customers with the Churn model data.
- Activate these segments in CRM, Marketing, and Analytics.
- Continue enriching the model with new data, closing the feedback loop once the data is activated for action.
Want to see how your business can keep up with the pace of change and make AI work for your teams?
Contact OSF Digital to discover how we can help your business implement AI, autonomous agents, and next-gen technology solutions to boost performance and stay ahead of the curve.
