Stream: AI
Time: 10:00 - 10:45
Issue Detection, Proactive Alerting & Sentiment Analysis Step 1: Identify Multi-Customer Issues (AI Clustering) # Goal: Group similar issues and detect if they impact multiple customers. # Approach: # Use text embeddings (TF-IDF or Sentence Transformers) on Case Subject + Case Description. # Cluster using DBSCAN or KMeans. # Identify clusters involving tickets from more than one customer. Step 2: Proactive Alerts to Customers # Goal: For identified widespread issues, create suggested alert messages. # Approach: # For each cluster affecting multiple customers, summarize the issue. # Generate a notification with the fix from Case Root Cause or infer from common patterns. # Step 3: Customer Sentiment Analysis # Goal: Determine customer mood — Happy, Sad, Indifferent, or Passive. # Approach: # Use sentiment analysis on Case Description and Case Subject. # Use customer support data or quarterly or annual customer survey # Label as: #
There is currently no attachment for AI-Driven Enhanced Customer Support
Sr Product Manager - Intelligent Z Optimization and Transformation (IZOT) at BMC Software I own and manage DB2 Admin portfolio of products, make an honest attempt to maximize the value delivered sprint after sprint, build the product strategy and roadmap and build and maintain product backlog.
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