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Salesforce Dashboard & Predictive Analytics

Case Study: Salesforce Dashboard & Predictive Analytics — KPI dashboards plus Einstein scoring that add forward-looking signals to enterprise CRM reporting.

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KPI+

Predictive Layer

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Data Pipelines

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Enterprise Business Intelligence

Salesforce Dashboard & Predictive Analytics

Analytics & AI Consultant

Case Study: Salesforce Dashboard & Predictive Analytics — KPI dashboards plus Einstein scoring that add forward-looking signals to enterprise CRM reporting.

Business IntelligenceCRM AnalyticsEinstein AnalyticsAutomated Data Pipelines

KPI+

Predictive Layer

Auto

Data Pipelines

In-app

Adoption

Cross

Cloud Metrics

Salesforce Dashboard & Predictive Analytics overview

Executive Summary

Existing reporting showed only historical performance, leaving leadership without forward-looking signals to prioritize deals, accounts, or resources.

We combined CRM Analytics KPI dashboards with Einstein predictive scoring and automated refresh pipelines — delivering both what happened and what is likely to happen next inside Salesforce.

Product UI

Project Screens

Salesforce Lightning Web Component screens from the loan origination platform — application intake, underwriting risk assessment, and pipeline Kanban — each built for financial services lending teams.

KPI Dashboards — Salesforce loan origination platform UI
Screen 01

KPI Dashboards

CRM Analytics KPI dashboards spanning sales, service, and operational metrics with automated data refresh.

KPI Dashboards give enterprise leaders a single Salesforce-native view of performance. Automated CRM Analytics pipelines feed curated datasets so sales, service, and operations metrics stay current — without exporting to a separate BI stack.

  • Cross-cloud KPI coverage for sales, service, and operations
  • Automated data refresh pipelines into CRM Analytics datasets
  • Leadership dashboards with trend and distribution views
  • Adoption-friendly embedding inside familiar Salesforce UX
CRM AnalyticsAutomated Data PipelinesDashboards
Predictive Scoring Models — Salesforce loan origination platform UI
Screen 02

Predictive Scoring Models

Einstein-driven predictive scoring surfaces likelihood-to-close and risk indicators inside existing dashboards.

Predictive Scoring Models layer Einstein Analytics insights on historical CRM data to highlight what is likely to happen next. Likelihood-to-close and risk signals appear inside the same dashboards leadership already uses, reducing adoption friction versus a standalone AI tool.

  • Einstein-based likelihood-to-close and risk indicators
  • Scores surfaced directly in CRM Analytics dashboards
  • Historical training data from Salesforce opportunity history
  • Actionable prioritization for deals, accounts, and resources
Einstein AnalyticsCRM AnalyticsPredictive Models

The Challenge

Historical-only reporting delayed prioritization and left revenue teams without predictive guidance.

  • •Dashboards reflected the past, not likelihood or risk ahead.
  • •Separate analytics tools created adoption friction.
  • •Data refresh was manual and inconsistent.
  • •Leaders lacked a unified KPI and prediction surface.

The Solution

We built a Salesforce CRM Analytics layer with automated pipelines and Einstein predictive scores embedded in leadership dashboards.

Key Features & Technical Implementation

01

KPI Dashboard Suite

Sales, service, and operational metrics in one analytics experience.

Technical Detail: CRM Analytics dashboards on curated datasets with scheduled refresh.

02

Einstein Predictive Scoring

Forward-looking close likelihood and risk indicators.

Technical Detail: Predictive models trained on historical CRM data and surfaced in-dashboard.

03

Automated Data Pipelines

Keep insights current without manual extracts.

Technical Detail: Scheduled dataflows and recipes feeding analytics datasets.

Technical Architecture

Automated CRM Analytics pipelines load curated datasets. KPI dashboards visualize performance while Einstein scoring overlays predictive signals directly in the same leadership experience.

The Results

KPI+

Predictive Layer

Historical KPIs plus Einstein scoring.

Auto

Data Pipelines

Scheduled refresh into analytics datasets.

In-app

Adoption

Scores shown where leaders already work.

Cross

Cloud Metrics

Sales, service, and ops coverage.

  • Unified KPI and predictive views for enterprise leaders
  • Faster prioritization of deals and accounts at risk
  • Higher adoption by keeping AI insights in existing dashboards
  • Reliable refresh cadence via automated pipelines

Technology Stack

CRM AnalyticsEinstein AnalyticsAutomated Data Pipelines

Conclusion

Pairing CRM Analytics KPIs with Einstein predictive scoring turned rear-view reporting into actionable, forward-looking Salesforce intelligence.

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