Auto Dealership Groups
You added rooftops.You can't see what's inside them.
Every store has a GM who runs it their way. Corporate gets a composite, not a picture. Used cars age out at one store while another overpays at auction for the same model. Service bays sit empty on Tuesday, then turn away work on Saturday. We embed with your team, map every rooftop from acquisition to F&I, and deploy AI specialists that find what your DMS dashboards never show you.
The Problem
Where the money
is going.
Cost
Used Vehicle Aging & Pricing
Every day a used car sits on your lot costs $32 to $45 in floor plan interest, insurance, and overhead. Your 60-day policy is soft and every GM knows it. One store wholesales a unit at a $2,200 loss on day 75 while another store would have retailed it in a week. Recon takes 10 days at your slowest store and 4 at your fastest, but nobody's comparing.
Process
Service Department Throughput
Your average bay utilization is somewhere around 55%, but you don't actually know because no one measures it consistently across stores. Master techs are doing oil changes. Bays sit empty mid-week while Saturday turns customers away. Hours per RO vary by 40% between your best and worst locations, and the fix isn't more technicians. It's scheduling, dispatching, and parts availability.
Cost
Parts Inventory Duplication
Each store stocks independently. Your group carries 25 to 30% excess parts inventory across all locations. One store has a brake rotor sitting on the shelf for nine months while another store overnights the same part from the OEM at full list price plus a hot-shot courier fee. Nobody runs a cross-store inventory check before placing an emergency order.
Knowledge
F&I Product Penetration Variance
Same products, same menu, same training provider. One store runs $2,400 PVR. The store 20 miles away runs $1,600. Service contract penetration is 55% at your top store and 28% at your bottom one. The gap isn't talent. It's process. Desk handoffs, menu presentation sequence, objection handling. But you can't fix what you can't see at the deal level.
How We Work
Three steps. Hands on.
We embed with your team, map your operation, find what no one could see, and deploy specialists that fix it. You get a dedicated team, not a login.
Map
We start with a structured discovery across every rooftop. Our team interviews GMs, used car managers, service directors, parts managers, and F&I directors at each location. We connect to your DMS, CRM, inventory management, and desking tools. The result is your Blueprint: a complete, live map of how each store actually operates, from vehicle acquisition through recon, service, parts, and F&I. Not what corporate thinks happens. What actually happens.
Uncover
We analyze everything we mapped. Our platform finds the recon bottlenecks adding five days to your time-to-line, the pricing gaps where one store undercuts another by $800 on the same model, the service scheduling patterns that leave bays empty 40% of the week. We validate every finding with your team before acting on it. Not a one-time audit. Always running, always finding more.
Execute
Every finding comes with a concrete plan and a deploy button. We build AI specialists that handle the fix end to end. Tighten used vehicle pricing across stores, optimize service bay scheduling, flag cross-store parts transfers before emergency orders go out, surface F&I process gaps at the deal level. You approve, they run. We stay with you to make sure they deliver.
Example Findings
What Yield typically finds.
Based on a typical mid-market company with $20M–$50M in annual revenue.
Cost
Aged Inventory Wholesale Losses
$247K/yr
Cost
Parts Duplication & Emergency Orders
$108K/yr
Process
Recon Workflow Bottlenecks
14 hrs/wk
Cost
Service Bay Underutilization
$152K/yr
Knowledge
Undocumented Store-Level Processes
11 per rooftop
In Practice
See it work.
From day one.
Week 1
Discovery
We talk to every rooftop.
AI-led conversations with every GM, used car manager, service director, parts manager, and F&I lead across all locations. Not surveys. Real conversations that capture the workarounds, the pricing instincts, the scheduling tricks, and the vendor relationships no DMS records.
Month 1
Blueprint + First Savings
Your Blueprint is live. Agents are saving money.
A complete, verified map of how each rooftop works, from vehicle acquisition through recon, retail, and fixed ops. The first cross-store opportunities are identified. AI specialists are already flagging aged units, balancing parts inventory, and surfacing F&I process gaps.
Ongoing
Continuous Returns
Savings compound. Every quarter.
Yield keeps finding inefficiencies, deploying specialists, and compounding savings. Pricing gets sharper as market data flows in. Service scheduling adapts as seasonal patterns shift. The platform pays for itself and keeps going.
FAQ
Common questions.
Our used car managers at different rooftops price the same model thousands apart — can you actually standardize that without overriding local market knowledge?
We don't override it. During discovery we capture each used car manager's pricing logic — the auction relationships, the local demand patterns, the trade-in instincts they've built over years. Then we build a pricing specialist that uses that combined knowledge across all rooftops. It flags when one store lists a three-year-old Accord at $2,100 below the store 30 miles away that just wholesaled the same trim at a loss. The local manager still makes the call, but now they see what every other store sees.
Our DMS data is inconsistent across brands — CDK at some stores, Reynolds at others — so how does the Blueprint account for that fragmentation?
We connect to CDK, Reynolds, Dealertrack, and other DMS platforms through their respective APIs and normalize the data into a single operational map. The inconsistency between platforms is actually one of the first things we expose: deals coded differently, service categories that don't match, parts catalogs with conflicting SKUs. Your Blueprint treats all rooftops as one operation regardless of what system each store runs.
F&I penetration varies by over 25 points between our best and worst stores — what specifically does the diagnostic look like for that?
We interview every F&I director, desk manager, and sales manager involved in the handoff. We pull deal-level data — PVR, product mix, decline rates, menu presentation time, and objection frequency. The specialist identifies where the gap forms: a weak desk-to-box handoff at one store, a menu sequence that buries the most profitable product at another, a manager who discounts service contracts before the customer objects. Each store gets a specific finding, not a blanket training recommendation.
We hired a 20-group consulting firm two years ago and their fixed ops playbook gathered dust within six months — why would this be different?
A 20-group engagement gives you a benchmarking report and a set of best practices to implement. The problem is implementation. Nobody at the store level has time to sustain the changes once the consultant leaves. Yield builds AI specialists that run the playbook continuously. They don't hand you a report on service bay utilization — they flag the empty bay on Tuesday morning, suggest the schedule change, and track whether the GM actually follows through. The system doesn't leave.
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See what Yield finds in
your dealerships.
30 days. Real results. Or walk away.