The challenge: analytics no one could actually use.
EvenFlow's platform helps automotive dealerships schedule service appointments more efficiently. The data behind those appointments, appointment history, parts inventory, recall lookups, technician utilization, is the most valuable signal a dealership has. None of it was reaching the people who needed it.
EvenFlow customers ran into the same four obstacles every SaaS team eventually meets when analytics is treated as a backend feature instead of a product capability:
- Insights locked in a black box.Dealership data sat in a backend system only developers could query. Operations and service managers, the people running the floor, couldn't get to it.
- Manual analysis on demand.Historical analysis happened ad hoc in Excel or Python whenever a developer had bandwidth, which wasn't often.
- One-off reporting. Reporting meant manual exports created inconsistently and only when someone had time to make them.
- Customer support flying blind.Support teams couldn't see the operational data behind a ticket, which stretched out resolution times on issues that should have taken minutes.
It wasn't a missing-feature problem. It was a missing-layer problem. EvenFlow had a great product. It didn't have an analytics layer designed to live inside it.
What EvenFlow chose: analytics that lives inside the product.
EvenFlow evaluated traditional BI tools and ruled them out quickly. They needed something that could live inside their AWS stack, keep dealership data multi-tenant secure, and deliver dashboards to customers without standing up a parallel platform.
They chose Qrvey for four reasons:
- Embedded analytics for SaaS.Customer-facing dashboards inside EvenFlow's application, not a separate analytics portal customers had to leave the product to use.
- Multi-tenant security.Strict separation of dealership data, governed at the platform level, without EvenFlow's team writing custom logic to enforce it.
- Self-service and democratization.Non-technical users, operations managers, service managers, and EvenFlow's own internal team can filter, segment, and explore data on their own.
- Workflow automation. Event-triggered reports and notifications that turn data into action, not just dashboards into more dashboards.
We chose Qrvey because it embeds natively in our AWS stack with true multi-tenant controls. Unlike traditional BI tools, Qrvey delivers in-app customer-facing dashboards plus the business intelligence and automation layer we need, all in one platform.
In production: three flavors of analytics, one platform.
EvenFlow now runs three different uses of Qrvey inside the same product, serving three different audiences.
Customer-facing dashboards
Multi-tenant dashboards live inside EvenFlow's application, giving dealerships real-time visibility into operational metrics that previously required a support request. Dashboards started as standardized views and now extend to dealer-specific configurations.
Workflow automation
The most visible automation EvenFlow has built so far is the Daily Recall Report: a workflow that pulls VIN-based recall data, generates a per-dealer report each day, and delivers it to parts managers before service appointments. The result is parts on hand when the customer walks in, instead of a callback and a rescheduling apology.
The flexibility and ease of use with Qrvey's platform lets us satisfy any use case our customers ask for. They're blown away when we say Sure, we can support this request. We'll have this ready later today. These help dealerships run more efficiently and deliver better customer experience.
Internal business intelligence
The third use is for EvenFlow itself. Non-technical EvenFlow team members can now troubleshoot customer issues, configure software, and pull insights from core datasets without filing a ticket with engineering. The same platform that serves their customers serves their team.
The outcomes: scale, experience, and a different operating model.
EvenFlow's analytics layer now does three things its old setup couldn't:
Scalability without headcount
EvenFlow has grown beyond its initial customer base and accelerated revenue without proportional growth in engineering headcount. Every new dealership doesn't come with a new analytics ticket queue, the platform absorbs the work.
Customer experience as a differentiator
EvenFlow has become the go-to resource for operational insights inside the dealerships it serves. When a customer asks why is this appointment slot slow on Tuesdays, the answer is in the dashboard, not in a ticket queue.
A different internal operating model
EvenFlow shifted from manual, developer-dependent reporting to self-service analytics. The team's time goes into building new product capabilities instead of running reports on request.
What's next: dealer-specific dashboards, and more.
EvenFlow is preparing to launch dealer-specific dashboards tailored to each customer's unique configuration. The company calls this an early stage, most of the metrics, automations, and insights still ahead of them. The point of this case study isn't that EvenFlow is finished. It's that the analytics layer is no longer the thing slowing them down.
