Client Overview
Industry
Software Development
Region
USA
Company Size
201-500 Employees
Featured Solution
End-to-End Case Automation Using Einstein
About the Client
A recognized leader in Financial Planning & Analysis (FP&A) cloud solutions, the company empowers organizations to navigate complex financial landscapes with precision and agility. Their cutting-edge platform, trusted by 1,000+ enterprises worldwide, streamlines planning, budgeting, forecasting, and reporting—driving faster, data-backed decision-making.
Support Ops Were Functional, But Far from Scalable
Despite having Salesforce Service Cloud, Experience Cloud, Voice, Data, and Sales Cloud in place, the customer’s support function was hitting a ceiling. Key workflows were still heavily manual and placed too much reliance on individual agent effort.
- Agents had to manually search for answers during live chats, slowing down resolution.
- Every case required manual summaries and documentation, increasing average handling time.
- Post-resolution knowledge wasn’t captured fast enough, leading to repetitive issue handling and limited knowledge reuse.
- The absence of proactive insights meant high-risk cases often escalated unnoticed.
In short, the tools were there, but the experience was not intelligent, consistent, or scalable.
High Time-to-Resolution, Overworked Agents, and Bottlenecks to Scale
Without automation in place, the customer faced several challenges as their end-to-end flow could take over 30 minutes per interaction, resulting in an inability to scale support with growing customer demands.
Lower Agent Efficiency
Agents spent over 50% of their time on non-resolution tasks like summarizing chats and searching internal documentation.
Limited Scalability
With each interaction requiring manual effort, support volumes couldn’t grow without additional hiring.
Delayed Customer Responses
Manual KB searches slowed chat responses, leading to longer wait times and lower CSAT.
Underutilized Knowledge Base
Valuable insights were lost or delayed due to the time it took to write and publish post-resolution articles.
Intelligent Automation with Minimal Disruption
With deep Salesforce AI expertise and a focus on operational scale, we reimagined the customer’s support workflows, without requiring a disruptive overhaul of their existing Salesforce stack. Our goal was simple: turn their manual support processes into an intelligent, AI-assisted engine that could scale.
We implemented a tightly integrated set of Einstein AI capabilities that tackled inefficiencies across the support lifecycle:
- Einstein Service Replies (for Chat):During live chats, Einstein surfaces context-aware responses sourced from the knowledge base. Agents simply review and send, enabling faster, more consistent replies.
- Einstein Work Summaries:After every interaction, Einstein automatically populates the case subject, description, and closure summary. It also generates email summaries and voice call notes, eliminating manual wrap-up and ensuring quality documentation.
- Case Timeline:We enabled a chronological view of every case interaction, giving agents a clear, timestamped summary of events, improving handoffs and reducing rework.
- Draft Knowledge Base Articles:Post-resolution, Einstein drafts a KBA directly from the case transcript. Agents only need to review and publish, dramatically improving knowledge reuse and reducing article creation lag.
- Similar Cases (via Salesforce Data Cloud):Using Salesforce Data Cloud, we reduced turnaround on recurring issues, so agents could instantly view similar past cases and resolutions, improving first-contact resolution.
- Escalation Prediction (Prompt Builder):Using customer sentiment analysis across emails and comments, we created a predictive model to flag cases likely to escalate.
Key Outcomes: Time Savings, Scale, and Higher-Value Support
Agents now save 10–15 minutes per case across the full support lifecycle, from chat response to case closure and knowledge article creation. With ~650 chats handled last year, this translates to 6,500–9,750 minutes saved, equivalent to 108–162 hours annually. That’s nearly a month of support capacity gained without increasing headcount. With routine tasks automated, agents focus more on complex, high-priority cases, boosting service quality and job satisfaction.
Highlights
10–15 Minutes Saved Per Case
650 Chats Handled Annually, Resulting in 108–162 Hours Saved
100% Automation of Draft KBAs and Wrap-Up Summaries
5% Increase in CSAT
Conclusion
By strategically embedding Einstein AI into the existing Salesforce environment, we delivered intelligent automation with minimal disruption. The customer now enjoys a streamlined support lifecycle that saves time, boosts agent productivity, scales effortlessly, and enhances knowledge reuse.
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