Proactive Customer Risk Management in Salesforce
Industry: Legal Technology
15–25%
reduction in churn risk by identifying negative sentiment 30–60 days earlier
20–30%
improvement in account prioritization efficiency
25–40%
reduction in manual analysis effort across teams
Background
A leading AI-powered legal technology company serves enterprise law firms, corporate legal departments and public-sector organizations. Customer relationships are long-term and high-stakes, where early signs of risk often emerge gradually across usage behavior, support interactions and commercial activity.
The company relied on BI dashboards built on Snowflake to track support activity, product usage, account engagement and commercial metrics. These dashboards provided high-level visibility but were not designed to explain why changes were occurring. Teams could see scores move, but lacked the context needed to interpret signals and act early.
Industry Challenge
In legal technology, customer risk appears through subtle shifts in usage patterns, case activity, responsiveness and communication tone, spread across multiple systems. Without a unified and explainable view of these signals, risk is often identified too late, after engagement has already declined.
Goals
- Detect customer risk earlier, before renewals are impacted
- Provide clear, explainable insight into why customer sentiment is changing
- Align sales, customer success and leadership around a shared view of account health
- Enable proactive, confident customer conversations
Implementation
The company partnered with Infoglen to embed an AI-driven customer intelligence layer directly within Salesforce.
- Customer data from Salesforce and Snowflake was unified using Salesforce Data Cloud, creating a complete and contextual account view.
- More than 220 indicators were built to track changes in financial behavior, product usage and operational activity over time.
- Customer emails, support conversations and interactions were analyzed to understand tone and intent.
- A flexible weighting model allowed business teams to control which signals influenced sentiment the most.
- Explainable AI logic was orchestrated using Agentforce, clearly outlining what drove sentiment changes.
- Insights were delivered natively in Salesforce through custom Lightning components, embedded into daily workflows.
See the Solution in Action
Impact
Measurable Outcomes
- 15–25% reduction in churn risk by identifying negative sentiment trends 30–60 days earlier
- 20–30% improvement in account prioritization efficiency by focusing on sentiment-declining accounts
- 25–40% reduction in manual analysis effort through automated, explainable scoring
- 10–15% uplift in renewal confidence through timely, context-aware customer conversations
Qualitative Outcomes
- A shared, trusted view of customer health across teams
- Faster, more confident customer and leadership conversations
- Shift from reactive firefighting to proactive account management
- A scalable foundation for ongoing AI-driven customer intelligence
Technology Stack
- Salesforce Data Cloud
- Agentforce
- Force.com
What the Customer Says
The development work was technically successful and the Infoglen team was knowledgeable, efficient and eager to help throughout the project. The team was responsive and demonstrated strong Salesforce expertise and problem-solving, including providing valuable technical input beyond the original scope.
— Jeannie Hawksworth, Program Manager, Careers Next
Goals
• Detect customer risk earlier, before renewals are impacted
• Create a shared, explainable view of account health
• Enable proactive, confident customer engagement
Implementation
• Salesforce as the unified intelligence layer
• AI-driven sentiment and risk analysis across accounts
• Explainable insights embedded directly in Salesforce workflows
• Clear governance aligned to responsibilities, not exceptions
Results
• 15–25% reduction in churn risk through earlier detection of negative sentiment
• 20–30% improvement in account prioritization efficiency
• 25–40% reduction in manual analysis effort
• 10–15% uplift in renewal confidence
