Toma

Running an auto dealership (like many retail businesses) can be framed as an optimization problem. There are two components to this problem. Objective Function: A collection of things you intend to maximize or minimize. Like the affordability of the product or the customer experience. Constraints: These are limitations that you must deal with. Think of Inventory you have access to or headcount budget. For auto dealerships, the constraints are numerous. Think: the choice of the manufacturer, hourly wage rates, income bracket of the customers, etc. Management chooses what to optimize for and what constraints to relax or tighten. The team executes on it. The blend of this creates a fingerprint of the business. Its a highly dynamic system. As we’ve implemented AI for hundreds of dealerships, we’ve learned that its impossible to map this system and the resulting nuances by having a few people fill a form. Sometimes its tribal knowledge, sometimes its so far at the back of the mind no one remembers. And the critical part is it keeps changing. That bothered us. Dealerships have institutional knowledge built over years — specific to their store, their team, their customers. And the AI wasn’t able to use it. So we built Toma IQ to change that. It analyzes all interactions of all your employees, identifies what they know that AI doesn’t, and proposes policies for a supervisor to accept or reject. Instant behavior change across every call the AI handles. Overtime, this becomes a living and breathing reference for AI Coworkers to work with. We rolled it out to existing customers last month. Resolution rates went up. Appointment revenue went up. Today we're announcing Toma IQ publicly. Full story at the link in the comments. 👇

Some customers don’t leave because of a bad experience. They leave because no one answered, they had to call twice, or because the conversation stalled and never picked back up. In our upcoming episode, Sean H. of Toma AI digs into how those small, easy-to-miss moments quietly impact service retention, and what dealerships can do about them without losing the human side of service. If you’ve ever wondered how many opportunities slip through the cracks after hours or between visits, this one’s worth your time. Full episode airs tomorrow!

Many dealers are overwhelmed with tools that never reach customers where it matters most: Today on the CDG Industry Spotlight, host Sam D'Arc sits down with Chris Murphy, General Manager of Volkswagen of Oakland and Monik Pamecha, CEO of Toma, to explore how forward-thinking dealers are reallocating human talent away from low-value phone work and back into high-impact, in-store customer interactions. Top lessons learned from Chris and Monik: 1. Missed opportunities in the fixed operations department. – Declined services, unfinished multi‑point inspection work, and overdue maintenance customers are the biggest problems. – Advisors rarely have time to call every declined‑line customer back once the day gets busy. – The store can saw thousands of dollars in potential labor and parts sitting in the DMS as “recommendations” that never get revisited. 2. How Volkswagen of Oakland is leveraging AI for service customers. – Toma is connected to the store’s service data so it can see declined work, inspection results, and recommended maintenance. – The AI is configured to reach out on behalf of the service department. – Messaging, timing, and offers are based on rules Chris and his team defined. 3. What has changed in fixed ops since launching AI. – More customers started coming back once Toma starts contacting them. – The shop is seeing an increase in ROs tied directly to AI‑initiated conversations about past recommendations. – Advisors are spending more time writing and explaining current work and less time trying to manually chase customers. 4. How Volkswagen of Oakland measures AI in service. – The team tracks additional ROs, hours per RO, and total dollars generated from AI outreach. – They review how many declined‑line customers Toma reaches, how many schedule appointments, and how many actually show. – And message logs are used to confirm that outreach is accurate to the vehicle and the prior visit. 5. What “good AI” means in fixed ops. – Outreach has to be specific to the car and the work that was previously recommended, not generic service reminders. – Timing and cadence need to fit the customer and the kind of work so it feels relevant, not spammy. – The only test that matters is whether AI‑driven service communication turns dormant recommendations into booked and completed repair orders. The full episode is live! Stream it now – YouTube: https://lnkd.in/d-zevDZW Apple Podcasts: https://lnkd.in/d3-UvKSc Spotify: https://lnkd.in/drPEHUsf