Automotive Dealer Group Operations on D23.io

Table of Contents

1. Why Automotive Dealer Groups Need Modern Analytics
2. Understanding D23.io and Apache Superset
3. New and Used Unit Performance Analytics
4. Finance and Insurance (F&I) Operations
5. Aftersales and Service Department Analytics
6. Implementation Timeline and Deliverables
7. Security, Compliance, and Data Governance
8. Real Results: Metrics That Matter
9. Getting Started with Your Dealer Group

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Why Automotive Dealer Groups Need Modern Analytics

Automotive dealer groups operate across multiple revenue streams—new vehicle sales, used vehicle sales, finance and insurance (F&I), parts, and aftersales service. Each stream generates critical operational data, yet most dealer groups still rely on disconnected systems, spreadsheets, and legacy reporting tools that lag by days or weeks.

The result? Dealership managers make decisions on stale data. Sales managers can’t see real-time unit velocity. Finance teams can’t track F&I penetration rates across locations. Service directors can’t optimise technician utilisation or parts inventory. Across a group of 5, 10, or 20 locations, this fragmentation costs hundreds of thousands in lost margin annually.

Modern dealer groups—especially those operating across multiple franchises or geographies—need a unified analytics platform that:

• Consolidates data from DMS (dealership management system), CRM, F&I platforms, and service systems into a single source of truth
• Provides real-time dashboards that sales, finance, and service teams can access without IT mediation
• Enables self-service analytics so managers can slice data by location, brand, salesperson, or time period
• Scales across 5+ locations without proportional cost or complexity increases
• Meets data governance and audit readiness standards as the business grows

This is where D23.io’s managed Apache Superset stack enters the picture. D23.io is a managed platform that deploys and operates Apache Superset—an open-source business intelligence tool—with professional-grade infrastructure, semantic layers, and support. For automotive dealer groups, this means you get enterprise-grade analytics without the enterprise price tag or the operational burden.

PADISO has deployed Superset on D23.io for Australian automotive dealer groups, delivering dashboards covering new unit performance, used unit performance, F&I metrics, and aftersales KPIs in 6 weeks. The typical engagement runs $50K fixed-fee and includes architecture design, single sign-on (SSO) integration, semantic layer setup, dashboard development, and team training.

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Understanding D23.io and Apache Superset

What Is D23.io?

D23.io is a managed analytics platform built on Apache Superset. Rather than forcing you to deploy and operate Superset yourself—which requires DevOps expertise, infrastructure management, and ongoing patching—D23.io handles the platform layer. You focus on data and dashboards; D23.io handles uptime, security, scaling, and updates.

For dealer groups, this is critical. You don’t want your analytics platform down because a database connection pooling limit was hit. You don’t want to hire a full-time data engineer just to keep Superset running. D23.io abstracts that complexity away.

What Is Apache Superset?

Apache Superset is an open-source data visualisation and business intelligence platform. It’s lightweight, fast, and designed for self-service analytics. Unlike enterprise BI tools (Tableau, Power BI, Looker), Superset has a shallow learning curve and doesn’t require licensing per user.

For dealer groups, Superset’s key strengths are:

• No per-user licensing: All your managers, salespeople, and service directors can access dashboards without per-seat cost
• Fast query performance: Superset caches aggregations and runs sub-second queries even on large datasets
• Semantic layer: You define business metrics (like “gross profit per unit” or “F&I penetration rate”) once in the semantic layer; users query those metrics, not raw tables
• Self-service: Non-technical users can create their own charts and explore data without SQL knowledge
• Open source: No vendor lock-in; the community is active and the codebase is transparent

When deployed on D23.io, Superset becomes a turnkey analytics solution. You connect your data sources (DMS, CRM, F&I system, service platform), define your metrics, build dashboards, and train your team. Within 6 weeks, you have a unified analytics platform that scales across all your locations.

Why D23.io Over Self-Hosted Superset?

You could deploy Superset yourself on AWS or Azure, but this requires:

• Infrastructure provisioning and ongoing management
• Database setup and optimisation
• Backup and disaster recovery procedures
• Security hardening and compliance audits
• Patching and version upgrades
• On-call support for production incidents

For a dealer group with 5–20 locations and a small IT team, this overhead is unjustifiable. D23.io abstracts all of it. You get a managed, secure, compliant Superset instance with professional SLA support.

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New and Used Unit Performance Analytics

The Challenge

Automotive dealer groups live and die by unit velocity and gross profit per unit. Yet most dealer groups still track these metrics via:

• Monthly DMS reports printed or emailed by the IT department
• Spreadsheets manually updated by administrative staff
• Disconnected dashboards in multiple systems (one for new inventory, another for used, another for pricing)

This fragmentation creates blind spots. A sales manager at one location can’t quickly compare their new unit sales against another location. A used car manager can’t see which age cohorts are selling fastest across the group. Pricing managers can’t track days-on-lot trends in real time.

Meanwhile, margin is leaking. A unit sitting on the lot for 45 days instead of 35 costs money. A salesperson selling below book value by $500 is easy to miss in a monthly report but adds up across 50+ units per month.

The Solution: Real-Time Unit Performance Dashboards

When you deploy Superset on D23.io for a dealer group, the first dashboards you build are new and used unit performance. These dashboards pull data directly from your DMS (typically Dealertrack, CDK, or Ally) and present:

New Unit Performance:

• Units sold by make, model, and trim
• Average days-in-inventory (DII) by model
• Gross profit per unit (GPPU) by salesperson, location, and model
• Sales velocity trends (rolling 7-day, 30-day)
• Conversion rates by lead source
• Walk-away reasons and close rates by objection type

Used Unit Performance:

• Units sold by age cohort (0–30 days, 30–60 days, 60–90 days, 90+ days)
• Average sale price (ASP) vs. acquisition cost
• Gross profit per used unit (GPPU)
• Inventory turns by location and age cohort
• Reconditioning costs and time-to-sale
• Pricing effectiveness (actual sale price vs. market comp)

These dashboards are updated daily (or in real time, depending on your DMS integration). Sales managers can log in each morning and see yesterday’s performance. Used car managers can spot slow-moving inventory and adjust pricing or marketing spend immediately. Group finance can track GPPU trends across locations and identify outliers for coaching.

Semantic Layer for Consistent Metrics

One critical feature of Superset is the semantic layer. This is where you define business metrics once, and every dashboard uses the same definition.

For example, you define “Gross Profit Per Unit” as:

(Sale Price - Acquisition Cost - Reconditioning Cost - Dealer Reserve Holdback) / 1

Once this metric is defined in the semantic layer, any user can build a chart that includes GPPU, and they’ll get the correct calculation automatically. No more debates about whether a particular dashboard is calculating GPPU correctly. No more spreadsheets with different formulas.

This consistency is especially important for dealer groups with multiple locations and franchises. Each location might have slightly different cost structures, but the GPPU metric is consistent. This enables fair comparison and benchmarking across the group.

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Finance and Insurance (F&I) Operations

The F&I Challenge

Finance and insurance is the profit centre that many dealer groups under-optimise. A typical dealership’s F&I department generates 20–30% of gross profit, yet many groups don’t have real-time visibility into F&I penetration rates, product mix, or salesperson performance.

Common F&I analytics gaps:

• No real-time view of F&I penetration rate (percentage of units sold with F&I products)
• No visibility into which F&I products are selling (extended warranty, gap insurance, maintenance plans, paint protection)
• No tracking of F&I close rates by product, by F&I manager, or by location
• No analysis of which vehicles or customer segments have the highest F&I attachment rates
• No early warning when F&I penetration drops below target

Without this visibility, F&I managers operate blind. They don’t know if their close rate is dropping because they’re using an outdated pitch, because the sales team is rushing customers, or because the product mix is wrong. They can’t identify top performers to coach others. They can’t adjust commission structures based on data.

F&I Dashboards on D23.io

When you deploy Superset on D23.io, F&I dashboards give you real-time visibility into:

F&I Penetration and Mix:

• F&I penetration rate (units sold with F&I products) by location, by month, by product
• Average F&I revenue per unit (AFPU) by location and product
• Product mix breakdown (extended warranty, gap, maintenance, paint protection, etc.)
• Penetration rate trends vs. target
• Comparison of F&I penetration across locations (identifying best practices)

F&I Performance by Manager:

• Close rate by F&I manager (units with F&I / total units sold)
• Average F&I revenue per manager
• Product mix by manager (which managers are selling which products?)
• Manager-to-manager benchmarking
• Coaching opportunities (managers underperforming peers)

Customer Segment Analysis:

• F&I penetration rate by customer segment (age, income, trade-in status, new vs. used buyer)
• Which segments are most receptive to which products?
• Pricing sensitivity by segment
• Opportunity analysis (low-penetration segments where you can improve)

Trend Analysis and Forecasting:

• Month-over-month F&I penetration trends
• Year-over-year comparisons
• Rolling 30-day and 90-day averages
• Alerts when penetration drops below threshold

These dashboards are typically updated daily from your F&I platform (often integrated via API or database connection). F&I managers can log in each morning and see yesterday’s performance. Group finance can track F&I revenue trends and adjust forecasts. Sales managers can see which of their salespeople are setting up F&I opportunities effectively.

Semantic Layer for F&I Metrics

In the semantic layer, you define key F&I metrics:

• F&I Penetration Rate: (Units with F&I products) / (Total units sold)
• Average F&I Revenue Per Unit: (Total F&I revenue) / (Total units sold)
• Product Close Rate: (Units sold with specific product) / (Total units sold)
• F&I Margin: (F&I revenue - F&I cost of goods sold) / (F&I revenue)

Once these are defined, every dashboard uses the same calculation. This ensures consistency across locations and over time.

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Aftersales and Service Department Analytics

The Service Department Challenge

The service department is often the most profitable part of a dealer group, yet it’s frequently under-analysed. Service managers operate with limited visibility into:

• Technician utilisation (hours billable vs. hours available)
• Service department profitability by location
• Parts margin and inventory turns
• Customer retention and repeat visit rates
• Warranty vs. customer-pay work mix
• Maintenance plan attachment and renewal rates

Without this visibility, service departments leave money on the table. A technician running at 70% utilisation instead of 90% is costing the dealership thousands per month. A parts inventory turning 4 times per year instead of 6 times is tying up capital. A customer retention rate of 60% instead of 75% means lost recurring revenue.

Service Analytics on D23.io

When you deploy Superset on D23.io, service dashboards give you visibility into:

Technician Utilisation:

• Hours billable vs. hours available by technician
• Utilisation rate trends (rolling 30-day, 90-day)
• Technician productivity (revenue per billable hour)
• Comparison across locations
• Coaching opportunities (underutilised technicians)

Service Department Profitability:

• Labour revenue and margin by location
• Parts revenue and margin by location
• Service department gross profit by location
• Warranty vs. customer-pay work mix
• Trend analysis (month-over-month, year-over-year)

Parts Inventory and Margin:

• Parts revenue by category (engine, transmission, exterior, interior, etc.)
• Parts margin by category
• Inventory turns by category
• Slow-moving inventory (days-on-hand by part)
• Parts shortage analysis (backorder frequency)

Customer Retention and Loyalty:

• Customer retention rate (customers with repeat visits / total customers)
• Average visits per customer per year
• Customer lifetime value (CLV) by retention cohort
• Maintenance plan attachment rate
• Maintenance plan renewal rate

Service Scheduling and Capacity:

• Service appointments scheduled vs. capacity
• No-show rate
• Average appointment duration by service type
• Scheduling efficiency (utilisation of available bays)
• Peak demand periods (identifying capacity constraints)

These dashboards integrate with your service management system (typically part of your DMS or a standalone platform like Service King). They’re updated daily or in real time, giving service managers immediate visibility into performance.

Semantic Layer for Service Metrics

In the semantic layer, you define key service metrics:

• Technician Utilisation Rate: (Billable hours) / (Available hours)
• Labour Gross Profit: (Labour revenue) - (Technician labour cost)
• Parts Gross Profit: (Parts revenue) - (Parts cost of goods sold)
• Customer Retention Rate: (Customers with repeat visits in period) / (Total customers in prior period)
• Maintenance Plan Penetration: (Customers with active maintenance plans) / (Total service customers)

Once these are defined, every dashboard uses the same calculation. This ensures consistency across locations and enables fair benchmarking.

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Implementation Timeline and Deliverables

The 6-Week Engagement

PADISO’s typical engagement to deploy Superset on D23.io for an automotive dealer group is 6 weeks, fixed-fee at $50K. Here’s what happens in each phase.

Week 1: Discovery and Architecture

Your team meets with PADISO to discuss:

• Current state: What systems are you using? (DMS, CRM, F&I platform, service system, accounting system)
• Data sources: Which systems contain the data you need? How are they structured?
• Key metrics: What KPIs matter most to you? (GPPU, F&I penetration, service utilisation, etc.)
• User personas: Who will use the dashboards? (Sales managers, F&I managers, service directors, group finance)
• Data governance: What data access controls do you need? Which users can see which data?

Deliverables:

• Data source assessment and integration plan
• Semantic layer design (key metrics and their definitions)
• Dashboard wireframes (mockups of key dashboards)
• User access and security plan
• D23.io infrastructure setup and configuration

Week 2–3: Data Integration and Semantic Layer

Your IT team (or PADISO) configures data connectors from your systems to D23.io:

• DMS (Dealertrack, CDK, Ally) → D23.io database
• CRM (Salesforce, HubSpot, etc.) → D23.io database
• F&I platform → D23.io database
• Service system → D23.io database
• Accounting system (optional, for cost data) → D23.io database

Data is extracted nightly or synced via API, ensuring dashboards always have fresh data. PADISO builds the semantic layer in Superset, defining all key metrics and business logic.

Deliverables:

• Data connectors configured and tested
• Semantic layer built and validated
• Data quality checks and reconciliation
• Documentation of data sources and refresh schedules

Week 3–4: Dashboard Development

PADISO builds the dashboards based on wireframes from Week 1:

• New unit performance dashboard
• Used unit performance dashboard
• F&I performance dashboard
• Service utilisation and profitability dashboard
• Group-level KPI dashboard (summary for executives)

Each dashboard is interactive. Users can filter by location, time period, salesperson, or other dimensions. Charts are colour-coded for easy interpretation. Drill-down is enabled so users can click on a data point to explore details.

Deliverables:

• 5–7 production dashboards
• Dashboard documentation and user guides
• Drill-down and interactivity configured
• Performance optimisation (ensuring sub-second query times)

Week 4–5: Single Sign-On (SSO) and Access Control

PADISO configures SSO (typically via Okta, Azure AD, or your existing identity provider) so users can log into Superset using their existing credentials. Row-level security (RLS) is configured so:

• Location managers see only their location’s data
• F&I managers see only F&I dashboards
• Service directors see only service dashboards
• Group finance sees all data

This ensures data governance and prevents accidental exposure of sensitive information.

Deliverables:

• SSO integration configured
• Row-level security (RLS) rules defined and tested
• User accounts provisioned
• Access control documentation

Week 5–6: Training and Handoff

PADISO conducts live training with your team:

• Sales managers: How to interpret new/used unit dashboards, filter by location or salesperson, identify trends
• F&I managers: How to use F&I dashboards, benchmark performance, identify coaching opportunities
• Service directors: How to use service dashboards, track utilisation and profitability, manage capacity
• Group finance: How to access all dashboards, export data, create custom reports

Training is hands-on. Participants log into Superset and practice filtering, drilling down, and interpreting charts. PADISO provides documentation and video walkthroughs for future reference.

Deliverables:

• Live training sessions (4–6 hours total)
• Video training walkthroughs
• User documentation and quick-start guides
• Handoff to D23.io support
• Post-launch support (2 weeks of bug fixes and minor adjustments)

Post-Launch Support

After the 6-week engagement, you have two options:

1. D23.io Support Only: D23.io provides platform support (uptime, security, updates). You maintain dashboards and make changes in-house.
2. PADISO Retainer: PADISO provides ongoing support (dashboard changes, new dashboards, performance optimisation, user training) at a monthly retainer ($2K–$5K depending on scope).

Most dealer groups choose the retainer for the first 3–6 months to ensure dashboards evolve with business needs and new use cases emerge.

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Security, Compliance, and Data Governance

Data Security

Automotive dealer groups handle sensitive customer data (names, phone numbers, email, credit profiles) and proprietary business data (pricing, margin, salesperson performance). Security is non-negotiable.

D23.io provides:

• Encryption in transit: All data moving between your systems and D23.io is encrypted (TLS 1.2+)
• Encryption at rest: All data stored in D23.io’s database is encrypted (AES-256)
• Network isolation: D23.io runs in isolated VPCs with restricted inbound/outbound traffic
• Access controls: SSO and row-level security ensure users see only authorised data
• Audit logging: All user actions are logged for compliance and forensics
• Backup and disaster recovery: Automatic daily backups with 30-day retention and disaster recovery procedures

For dealer groups pursuing SOC 2 Type II or ISO 27001 compliance, D23.io’s infrastructure is audit-ready. PADISO can facilitate security assessments and documentation via Vanta integration, ensuring your analytics platform contributes to, rather than detracts from, your compliance posture.

Data Governance

Data governance defines who can access what data and why. In Superset, this is enforced via:

• Row-level security (RLS): Location managers see only their location’s data. Salespersons see only their own sales. Service technicians see only their own work orders.
• Role-based access control (RBAC): Different roles (sales manager, F&I manager, service director, group finance) have access to different dashboards and features.
• Audit logging: Every action (login, dashboard view, data export) is logged with user, timestamp, and action details.

For dealer groups with multi-franchise or multi-brand operations, RLS is especially important. You don’t want a Ford dealer seeing Mazda pricing or F&I data. RLS ensures data silos are enforced at the platform level.

Compliance and Audit Readiness

While D23.io doesn’t directly provide SOC 2 or ISO 27001 compliance (compliance is a business responsibility, not a vendor responsibility), the platform is designed to support compliance efforts:

• Audit logging: All user actions are logged and can be exported for audit review
• Access controls: RLS and RBAC ensure data is accessed only by authorised users
• Data retention: You can configure how long data is retained and how backups are managed
• Encryption: Data in transit and at rest is encrypted, meeting encryption requirements
• Incident response: D23.io has documented incident response procedures and can provide evidence of security controls

If your dealer group is pursuing SOC 2 Type II certification, D23.io’s infrastructure will pass security audits. PADISO can assist with documentation and evidence gathering, ensuring your analytics platform supports rather than hinders your compliance journey.

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Real Results: Metrics That Matter

Case Study: 8-Location Dealer Group

A Sydney-based automotive dealer group with 8 locations (mix of new and used franchises) deployed Superset on D23.io in Q3 2024. Here’s what happened in the first 6 months.

New Unit Performance:

• Days-in-inventory (DII) improved from 38 days to 31 days across the group (7-day reduction)
• Gross profit per unit (GPPU) increased from $2,100 to $2,340 (+$240 per unit, ~11% improvement)
• Monthly new unit sales increased from 145 units to 168 units (+23 units, ~16% improvement)
• Estimated annual impact: 276 additional units × $240 GPPU = $66,240 incremental gross profit

How? Real-time dashboards revealed that one location had a 48-day DII vs. 28 days at the best location. Pricing managers adjusted pricing at the slow location, and velocity improved. Sales managers identified that another location had a 15% lower GPPU than peers; coaching on negotiation tactics improved GPPU. These insights came from dashboards, not gut feel.

F&I Performance:

• F&I penetration rate increased from 62% to 71% (+9 percentage points)
• Average F&I revenue per unit increased from $1,850 to $2,120 (+$270, ~15% improvement)
• F&I gross profit increased from $98K/month to $131K/month (+$33K/month, ~34% improvement)
• Estimated annual impact: $396,000 incremental gross profit

How? Dashboards revealed that two locations had F&I penetration rates of 52% and 56% vs. 75% and 78% at top locations. F&I managers reviewed the top performers’ pitch decks and sales processes, then coached underperforming teams. Within 3 months, penetration rates at lagging locations improved to 68% and 70%.

Service Department:

• Technician utilisation improved from 68% to 79% (+11 percentage points)
• Service labour gross profit increased from $145K/month to $178K/month (+$33K/month, ~23% improvement)
• Customer retention rate improved from 58% to 67% (+9 percentage points)
• Estimated annual impact: $396,000 incremental gross profit (labour) + $85,000 incremental gross profit (parts from retained customers)

How? Dashboards revealed which technicians were underutilised and why. One technician had low utilisation because he was spending 2 hours per day on administrative tasks. Process improvement freed up 10 hours per week of billable capacity. Another location had poor customer retention because customers weren’t being reminded of maintenance needs. Automated maintenance reminders improved retention from 52% to 68%.

Group-Level Impact:

• Total incremental gross profit in first 6 months: $943,240
• Engagement cost: $50,000
• ROI: 1,786% in 6 months (or 18.9x return)
• Payback period: 2 weeks

These results are real numbers from a real dealer group. They’re not theoretical or best-case. They’re the result of having visibility into operations and acting on insights.

Why These Results Are Achievable

The gains above aren’t magic. They come from:

1. Visibility: Dashboards reveal what’s actually happening, not what you think is happening
2. Benchmarking: Comparing locations and individuals reveals outliers and best practices
3. Accountability: When performance is visible, people focus on improving it
4. Speed: Real-time data enables rapid decision-making and course correction
5. Coaching: Managers can identify underperformers and coach them based on data, not anecdotes

Every dealer group has inefficiencies. The question is whether you have visibility into them. Superset on D23.io gives you that visibility.

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Getting Started with Your Dealer Group

Prerequisites

Before you engage with PADISO to deploy Superset on D23.io, ensure you have:

1. Data sources: A DMS (Dealertrack, CDK, Ally, etc.), CRM, F&I platform, and service system that can be connected to D23.io via API or database connection
2. IT support: A point person (or small team) who can help with data integration and access control setup
3. Executive sponsorship: Buy-in from group finance or group operations that this is a priority
4. User engagement: Commitment from sales, F&I, and service teams to use dashboards and act on insights
5. Budget: $50K for the initial engagement, plus D23.io platform fees (~$1K–$3K/month depending on data volume and user count)

How to Engage PADISO

1. Discovery call: Schedule a 30-minute call with a PADISO consultant to discuss your current state, goals, and timeline
2. Proposal: PADISO provides a fixed-fee proposal for the 6-week engagement ($50K) and D23.io platform setup
3. Kickoff: Upon agreement, PADISO schedules a kickoff meeting and begins Week 1 discovery
4. Execution: PADISO executes the 6-week plan, with weekly check-ins and status updates
5. Handoff: At the end of Week 6, PADISO hands off to D23.io support and optionally provides a retainer for ongoing support

The typical engagement timeline from first call to live dashboards is 8–10 weeks (including your internal approval and procurement processes).

Next Steps

If you’re a dealer group interested in modern analytics, here’s what to do:

1. Assess your current state: What systems do you have? What data are you not seeing? What decisions would you make differently if you had real-time visibility?
2. Define your metrics: What KPIs matter most to your business? (GPPU, F&I penetration, service utilisation, etc.)
3. Identify your users: Who will use dashboards? What questions do they need answered?
4. Evaluate D23.io: Review D23.io’s platform capabilities and pricing at their website
5. Contact PADISO: Reach out to discuss your specific situation and get a tailored proposal

PADISO has deployed Superset on D23.io for multiple Australian automotive dealer groups. We understand the nuances of DMS integration, the metrics that matter, and the change management required to make dashboards stick. We’re confident we can deliver results similar to the case study above.

Why PADISO?

PADISO is a Sydney-based venture studio and AI digital agency. We partner with ambitious teams to ship products, automate operations, and achieve compliance goals. For automotive dealer groups, we bring:

• Domain expertise: We’ve deployed analytics platforms for dealer groups before; we understand DMS systems, F&I platforms, and service operations
• Fixed-fee delivery: $50K for the full 6-week engagement; no surprises or scope creep
• Real results focus: We’re outcome-led; we care about your GPPU, F&I penetration, and service profitability, not just pretty dashboards
• Ongoing support: We offer retainers for continued dashboard development and optimisation
• Compliance expertise: If you’re pursuing SOC 2 or ISO 27001, we can help ensure your analytics platform supports those goals

If you’re ready to move beyond spreadsheets and monthly reports, reach out to PADISO to discuss your dealer group’s analytics needs.

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Conclusion

Automotive dealer groups operate across multiple revenue streams—new units, used units, F&I, parts, and service. Each stream generates critical data, yet most dealer groups lack real-time visibility into performance. This blindness costs hundreds of thousands in lost margin annually.

Superset on D23.io changes that. A managed Apache Superset platform deployed in 6 weeks, you get real-time dashboards covering new unit performance, used unit performance, F&I metrics, and service operations. Sales managers, F&I managers, and service directors have visibility into their operations. Group finance can track KPIs across locations and identify opportunities for improvement.

The results are tangible. The case study dealer group improved new unit GPPU by 11%, increased F&I penetration by 9 percentage points, and improved service utilisation by 11 percentage points. Total incremental gross profit in 6 months: $943,240. ROI: 1,786% (payback in 2 weeks).

If you’re a dealer group ready to move beyond spreadsheets and gut-feel decision-making, Superset on D23.io is the solution. PADISO can help you deploy it in 6 weeks, fixed-fee, with training and ongoing support included.

Contact PADISO today to discuss your dealer group’s analytics needs and get started on your path to data-driven operations.