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Proactive Agent Assist Copilot

AI-powered real-time recommendations, next-best-actions, and contextual intelligence surfaced to agents during live customer interactions.

Priority: P2 — Strategic Value
Time to Value: 8-10 weeks
Category: Agent Augmentation

Business Problem

Customer operations agents handle complex interactions where the right information at the right moment determines the outcome — yet agents operate with limited visibility into the customer's full picture. Without proactive AI assistance:

  • Missed upsell/cross-sell — agents resolve issues without recognizing revenue opportunities; 70-80% of expansion moments go undetected
  • Reactive-only service — agents wait for customers to report problems instead of preemptively addressing known issues
  • Knowledge overload — 500+ knowledge articles, 50+ SOPs, and constantly changing policies mean agents often guess instead of searching
  • Inconsistent quality — top-performing agents outperform average agents by 3-4x on CSAT and resolution speed, but their tribal knowledge is not captured
  • Churn blindness — high-value customers showing churn signals are treated the same as routine callers until they leave

Capabilities

Real-Time Next-Best-Action

During a live interaction, surface the optimal action based on customer context, issue type, and business objectives: "Recommend: Waive late fee ($45) — customer is Platinum tier with CLV $48K, 3 years tenure, first late payment. Retention ROI: 107x."

Churn Intervention Cards

For customers flagged with elevated churn risk, automatically surface retention context and pre-approved offers: "CHURN ALERT: Account ACCT-2205 has 0.78 churn probability. Triggers: 2 unresolved cases in 30 days, NPS dropped from 72 to 31, usage down 40%. Pre-approved: 3-month 20% discount OR free premium feature trial."

Contextual Knowledge Delivery

Instead of agents searching through knowledge bases, AI pushes the relevant article or resolution template as the customer describes their issue — ranked by relevance, success rate, and recency.

Sentiment-Aware Coaching

Real-time voice/text sentiment analysis with coaching prompts: "Customer sentiment turning negative (0.3 → -0.4 in last 2 minutes). Suggested: acknowledge frustration, offer direct escalation path, reference previous positive interaction on Jan 15."

Upsell & Cross-Sell Intelligence

Detect expansion signals during service interactions: "Customer asking about feature X — they're on Basic plan. Similar accounts who upgraded to Pro after this inquiry: 68% conversion rate. Suggest: offer 30-day Pro trial."

Data Sources & Ontology Mapping

flowchart LR
    subgraph Data Plane
        SF["Salesforce CRM"]
        OE["Oracle ERP"]
        WXCC["Webex Contact Center"]
        SL["Social Listening"]
        GD["Google Drive"]
    end

    subgraph Ontology Entities
        CUST["Customer Profile"]
        INTERACT["Live Interaction"]
        BILLING["Billing Context"]
        KNOW["Knowledge Base"]
        SENTIMENT["Sentiment & NPS"]
        HISTORY["Resolution History"]
    end

    subgraph AI Workflow
        NBA["Next-Best-Action Engine"]
        CHURN["Churn Intervener"]
        COACH["Sentiment Coach"]
        UPSELL["Upsell Detector"]
    end

    SF --> CUST
    SF --> HISTORY
    OE --> BILLING
    WXCC --> INTERACT
    SL --> SENTIMENT
    GD --> KNOW

    CUST --> NBA
    INTERACT --> NBA
    BILLING --> NBA
    KNOW --> NBA
    SENTIMENT --> CHURN
    HISTORY --> NBA

    NBA --> COACH
    NBA --> UPSELL
    NBA --> CHURN
Ontology Entity Source System Key Fields
Customer Profile Salesforce Account + Oracle AR_Customer Account_ID, Tier, CLV, Churn_Risk, Tenure, Product_Holdings, Preferences
Live Interaction WXCC Call_Record + Chat_Transcript Interaction_ID, Channel, Real_Time_Sentiment, Topic_Detected, Duration, Agent_ID
Billing Context Oracle AR_Invoice + AR_Payment + Billing_Dispute AR_Balance, Last_Payment, Open_Disputes, Payment_Behavior_Score
Knowledge Base Google Drive SOP_Document + SF Knowledge_Article Article_ID, Category, Resolution_Steps, Success_Rate, Recency_Score
Sentiment & NPS Social Listening + CSAT_Survey + NPS_Response NPS_Score, CSAT_Score, Sentiment_Trend, Last_Survey_Date, Detractor_Flag
Resolution History Salesforce Case + Case_Comment Past_Cases, Resolution_Patterns, Avg_Resolution_Time, Preferred_Channel

AI Workflow

  1. Interaction Detection — WXCC webhook signals new customer interaction (call/chat/email); trigger context assembly
  2. Customer Profile Hydration — Pull full profile: Salesforce account, Oracle billing, interaction history, NPS, churn risk, product holdings
  3. Real-Time Intent Detection — NLP on live speech/text to classify intent (issue report, billing inquiry, upgrade interest, cancellation signal)
  4. Knowledge Matching — Match detected intent to top-3 knowledge articles/resolution templates ranked by relevance and success rate
  5. Action Scoring — Score candidate actions (resolve, escalate, offer, waive, upsell) against customer context, business rules, and policy constraints
  6. Recommendation Delivery — Push ranked recommendations to agent desktop in real-time with confidence scores and one-click action buttons
  7. Outcome Capture — Record which recommendations were accepted/rejected; feed back into model for continuous learning

Dashboard & Alerts

Key Metrics

KPI Description Target
Recommendation Acceptance Rate % of agent-facing recommendations acted upon > 40%
First Call Resolution (FCR) % of interactions resolved on first contact > 80%
CSAT Score Customer satisfaction score (1-5 scale) > 4.2
Upsell Conversion Rate % of surfaced upsell recommendations converted > 12%
Churn Save Rate % of at-risk customers retained after intervention > 55%
Knowledge Article Relevance % of pushed articles rated helpful by agent > 75%
Agent Assist Latency Time from interaction start to first recommendation < 8 seconds

Alert Rules

Alert Trigger Severity Action
High-value churn signal Platinum/Gold customer with churn risk >0.7 calls in Critical Immediate routing to retention specialist with pre-built offer card
Sentiment collapse Real-time sentiment drops below -0.6 during interaction High Push de-escalation guidance + supervisor notification
Revenue opportunity Upsell signal detected for customer with >$50K CLV Medium Push offer card with pre-approved terms to agent
Knowledge gap No relevant article found for detected intent (confidence <0.4) Medium Flag to knowledge management team; log for content creation
Model drift Recommendation acceptance rate drops >15% over 7-day window Medium Alert ML ops; trigger model retraining pipeline

ROI Model

Metric Before After Impact
First call resolution 65% 82% 26% improvement → $1.4M cost avoidance
CSAT score 3.6 4.3 19% improvement → reduced churn
Upsell revenue per agent/month $2,800 $5,400 93% increase → $3.1M incremental annual revenue
Churn save rate (at-risk accounts) 28% 56% 100% improvement → $2.4M retained revenue
Agent knowledge search time 3.2 min/case 0.4 min/case 87% reduction → 15% handle time savings
New agent time-to-competency 8 weeks 4 weeks 50% faster ramp

Estimated Annual ROI

$4.0M - $7.0M annually from upsell revenue, churn prevention, FCR improvement, and agent productivity — across a mid-size enterprise with 200 agents handling 500K interactions/year.

Implementation Notes

  • Real-time recommendation delivery requires <8 second latency from interaction start; WXCC webhook + pre-cached customer profiles critical
  • Churn risk model requires minimum 18 months of labeled churn data (accounts that left + accounts retained after intervention)
  • Upsell models should be trained on historical upgrade patterns with minimum 6 months of conversion data
  • Sentiment analysis on voice requires real-time speech-to-text pipeline with WXCC call recording integration
  • Recommendation acceptance/rejection feedback loop is essential for model improvement; agent desktop must capture this signal

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