Composite Score Logic and Resources

• Normalized NPS from different departments
• Normalized CSAT from different departments

• NPS: Standardized to a 0–100 range.
• CSAT: Normalized directly to a 0–100 scale.

If a department's NPS or CSAT, or KPI returns null, it’s treated as 0 in the sum (handled via IFNULL). The weights are automatically adjusted to balance the score.

The score sums all available normalized NPS and CSAT and scales the total by using the CCSI_VoC_Score_Equal_Weight_prm parameter. This allows for uniform weighting where each available signal contributes incrementally to the final score.

This strategy ensures a holistic view of the customer relationship. By aggregating signals from every department, the score prevents uncertainties where high satisfaction in one area might mask poor experiences in another. Every valid signal incrementally builds the total health score.

• Positive Sentiment Normalized
• Negative Sentiment Normalized (Inverted)
• Neutral Sentiment Normalized
• Mixed Sentiment Normalized

• Data Pre-processing: Uses a batch data transform (BDT) in Data 360 to explode records from the Engagement Analysis Text Insight DMO. This makes sure that complex text responses containing multiple sentiment values are split into individual rows such as contact reason, product, and key phrase before aggregation.

  The Engagement Analysis Text Insight DMO is provided with Agentforce Service's Customer Signals Intelligence. If you haven't set up Customer Signals Intelligence, then the BDT isn't installed.

• Sentiment Ratios: Calculated as a percentage of the total sentiment count.
• Inversion: Negative sentiment is explicitly inverted (100 - Negative Score) so that lower negativity adds points to the final score.
• Capping: The final score uses MIN(100, ...) logic to make sure that it never exceeds 100.

• Sentiment Ratios: Normalized to a 0–100 percentage scale.
• Negative Inversion: A negative sentiment score of 0 becomes a contribution of 100 points (before weighting).

• Campaign-to-Opportunity Contribution Normalized
• Lead to Opportunity Ratio Normalized
• Won-Closed Opportunities % Normalized
• Average Pipeline Velocity Normalized
• Case Resolution Speed Normalized (Inverted)
• FTFR Normalized

• Pipeline Velocity: Calculated by using Bookings, Win Rate, and Deal Length.
• Case Resolution: Inverted (100 - Case Resolution Speed) so faster times yield a higher score.
• FTFR: Standard calculation of First-Time Fix Rate.

• Ratios & Rates: KPIs like Lead Conversion and FTFR are scaled to a 0–100 range.
• Time Inversion: Case Resolution Speed is normalized and then inverted (100 - Speed) so that faster resolution times result in a higher score contribution.

The score sums all normalized journey components and scales the total by using the CCSI_Journey_Experience_Score_Equal_Weight_prm parameter.

This strategy treats every stage of the lifecycle from marketing engagement to service resolution as equally critical. It acknowledges that a journey requires excellence at every handoff, preventing a strong sales experience from being undermined by poor service delivery.

• Mobile Engagement Activities Normalized
• Total Opportunities Win Rate Normalized
• Ratio for Total Cases Closed/Created Normalized
• Ratio For Total Work Orders Closed and Created Normalized

• Mobile Activity: Sum of SMS and WhatsApp interactions.
• Win Rate: Percentage of opportunities won vs. closed.
• Closure Ratios: Ratio of closed cases/work orders to total created.

• Volume Normalization: Mobile activity counts are normalized against organizational baselines to fit a 0–100 scale.
• Ratios: Win rates and closure ratios are standard percentages scaled to 0–100.

The score sums the normalized engagement components and scales the total by using the CCSI_Engagement_Effectiveness_Score_Equal_Weight_prm parameter.

By weighing mobile interactions, sales wins, and service closures equally, this score validates true engagement. It filters out noise by requiring that high activity levels are matched by actual outcomes, making sure to measure meaningful commitment rather than busy work.

• Upsell Cross-Sell Identified During Visits Normalized
• Campaign-to-Revenue Contribution Normalized
• Monthly Recurring Revenue Normalized
• Revenue at Risk Normalized (Inverted)

• Revenue Logic: Sums MRR and Upsell opportunities.
• Risk Logic: Revenue at Risk is inverted (100 - RAR) to make sure that high risk negatively impacts the score.

• Financial Normalization: MRR and Upsell values are normalized to a 0–100 scale based on defined ranges.
• Risk Inversion: Revenue at Risk is inverted (100 - Normalized Risk) so that 0 risk results in a maximum score contribution.

The score sums the revenue components and scales the total by using the CCSI_Revenue_Health_Score_Equal_Weight_prm parameter.

This strategy balances growth potential with stability. It makes sure that a high-growth account isn’t rated as healthy if it carries significant revenue risk, aligning the score with sustainable financial performance.

• Ratio For Total Work Orders Closed and Created Normalized
• FTFR Normalized
• Case Resolution Speed Normalized (Inverted)
• Ratio for Total Cases Closed/Created Normalized
• Mobile Engagement Bounce Events Normalized (Inverted)

• Efficiency: Resolution Speed and Bounce Events are inverted (100 - KPI) so that lower times and fewer bounces increase the score.
• Success Rates: FTFR and Closure Ratios are added directly.

The score sums the 5 operational components and scales the total by using the CCSI_Operations_Score_Equal_Weight_prm parameter.

This approach penalizes inefficiencies while rewarding success. It makes sure that operational speed doesn’t come at the cost of quality, providing a clear metric for service delivery effectiveness.

• Open Support Cases Normalized (Inverted)
• Positive Sentiment Normalized
• Ratio for Total Cases Closed/Created Normalized
• FTFR Normalized
• Work Order Volume Normalized (Inverted)
• Revenue at Risk Normalized (Inverted)
• Total Complaints Raised By Users Normalized (Inverted)

• Positive Indicators: Sentiment, Closure Ratio, and FTFR are added directly.
• Risk Indicators: Open Cases, Work Order Volume, Revenue at Risk, and Complaints are explicitly inverted (100 - KPI) in the formula to reward low volumes of negative signals.

The score sums all health indicators and scales the total by using the CCSI_Customer_Health_Score_Equal_Weight_prm parameter.

By inverting negative sentiment and weighing it alongside positive signals, this strategy acts as an early warning system. It makes sure that a rise in negative interactions immediately drags down the score, flagging emotional disconnects before they turn into formal complaints or churn events.

• Total Complaints Raised By Users Normalized
• Mobile Engagement Negative Interactions Normalized
• Revenue at Risk Normalized
• Negative Sentiment Normalized

The score sums all risk components and scales the total by using the CCSI_Churn_Risk_Index_Score_Equal_Weight_prm parameter.

Unlike other scores, this is a risk accumulator. By summing complaints, disengagement, and sentiment drops, it makes sure that any single red flag contributes to the alert level. This creates a highly sensitive metric designed to trigger retention interventions immediately.

• Total Revenue Normalized
• Total Orders Created Normalized
• Total Renewal Orders Normalized
• Positive Sentiment Normalized

• Summation: Adds normalized values for Revenue, Orders, Renewals, and Sentiment.
• Capping: The final score is explicitly capped at 100 by using the MIN() function to handle potential outliers.

The score sums the value KPIs and scales the total by using the CCSI_Customer_Lifetime_Value_Score_Equal_Weight_prm parameter.

This strategy connects hard values such as revenue and orders with soft values such as sentiment and loyalty. It identifies customers who aren’t engaged today, but emotionally invested for tomorrow.