Case Study - Real-time KPI Analytics for Auto Dealerships: A BigQuery, Vertex AI, and Looker Studio Solution

· KPI,Real-time


Auto dealerships generate vast amounts of data across various departments, but often struggle to translate that data into timely, actionable insights. Traditional reporting methods can be slow and cumbersome, hindering the ability to make informed decisions. Real-time analytics offer a solution, providing up-to-the-minute visibility into key performance indicators (KPIs) for optimal decision-making and operational agility.


Solution - Real-time analytics architecture using Google Cloud Platform (GCP):


Google BigQuery: A scalable data warehouse for centralized data storage and analysis.

Vertex AI: For building machine learning models to predict customer behavior and optimize operations.

Looker Studio (formerly Google Data Studio): A visualization tool to create interactive, real-time dashboards.




Track sales trends, sales rep performance, lead conversion rates, inventory turnover.


Monitor service appointments, technician workload, parts usage, customer wait times.

Insurance and Finance: 

Track contract volume, approval rates, revenue by product.

Body Shop: 

Analyze repair cycle times, backlogs, profitability per job.


Identify popular accessories, attachment rates, upsell opportunities.

Accounts Receivable/Payable: 

Monitor outstanding balances, aging reports, cash flow.

Customer Satisfaction: 

Track service ratings, survey responses, sentiment analysis.

Audit Compliance: 

Adherence to reporting standards, inventory accuracy.



1. Data Sources


Dealership Management System (DMS): Core data from sales, service, accounting modules.

CRM: Customer data, interactions, marketing campaigns.

Parts Inventory System: Real-time inventory levels, pricing.

External Data: Economic indicators, demographics, competitor research.

2. Data Ingestion into BigQuery


Batch ETL: For scheduled data loads from DMS, CRM (using tools like Fivetran, Stitch, or GCP Dataflow).

Streaming Pipelines: For real-time updates from systems supporting it (Pub/Sub, IoT sensors, streaming API integrations)

3. Data Preparation in BigQuery


Data Cleaning and Transformation: Address inconsistencies, format data for analysis, and create derived fields.

Data Modeling: Design schemas and relationships for efficient analysis.

4. Vertex AI for Predictive Analytics


Model Development: Using historical data:

Predict customer churn.

Forecast parts demand.

Identify customers likely to purchase accessories.

Optimize service appointment scheduling.

Model Deployment: Deploy models as API endpoints for integration.

5. Looker Studio Dashboard Design


KPI Definition: Collaborate with business users to pinpoint critical KPIs.

Visualization Creation: Design charts, tables, scorecards, maps, etc., focusing on clarity and impact.

Real-Time Data Sources: Connect dashboards to BigQuery for up-to-minute refreshes.

Integrate Predictive Insights: Display Vertex AI outputs alongside operational KPIs.

6. User Access and Sharing

Role-based Permissions: Implement security within Looker Studio to control data access.

Scheduled Reports: Automated delivery of snapshots to users' inboxes.

Table 1: Example KPI Mapping


KPI Examples

Data Source


Sales by model, Salesperson performance, Lead conversion



Appointments booked, Technician utilization, Parts usage


Body Shop

Repair turnaround time, Profit per job, Technician workload



Revenue by product line, Accounts receivable aging

DMS, Accounting System