3 Min Read • August 28, 2025

How Machine Learning Helps Dealerships Price and Manage Inventory

Over half of dealers struggle to price used vehicles with confidence, and 62% say pricing prevents them from achieving financial goals. That's a significant barrier in an already delicate balancing act: Carry too little inventory and customers can’t find what they want in your stock so they go elsewhere. Carry too much, and vehicles sit on the lot, increasing your carrying costs and shrinking your margins.

Machine learning offers a data-driven, localized and real-time approach to striking that balance.

Traditional Inventory Approaches Struggle To Keep Pace

Before machine learning, inventory management felt like a guessing game. General Managers reviewed historical data and combined it with their own knowledge of seasonality, regional influences and gut instinct to drive inventory decisions and pricing adjustments. But with dealership turnover rates at alarmingly high levels and fewer people staying in the business long term, that institutional knowledge is disappearing.

To complicate matters further, the market often moves faster than traditional inventory approaches can handle. By the time GMs and Sales Managers analyze mountains of historical data and charts to dig for insights, market conditions may have often already shifted.

Machine Learning Transforms Every Stage of the Process

Machine learning addresses these timing and knowledge challenges across every stage of the inventory management process, starting with acquisition. Over half of dealers focus on acquiring inventory directly from customers, and others hit the auction lanes. In either case, dealership staff have to decide which vehicles to acquire, in which condition, and how much they should pay for them.

That decision hinges on understanding the margins: actual value minus reconditioning, repair, holding and acquisition costs. Fail to accurately determine even one of those inputs, and your margin faces risk and could turn negative. Machine learning tools provide real-time market analysis and accurate valuations, including forecasts that flag slow movers before you buy them.

Once the dealership acquires the vehicle, getting it listed quickly becomes the next hurdle. Nearly three out of every five dealers say listing limitations and quality issues hindered their efforts, with half struggling with listing across channels. AI-powered photo and writing tools help here, too, by generating images and descriptions so dealers get vehicles listed quickly and attract more buyers.

When inventory doesn’t move as quickly as expected, it's tempting to adjust the price or ship it off to auction to move it off your lot. But machine learning tools can take the guesswork out of it by evaluating when to cut losses and when it’s worth holding on to.

It also helps fill experience gaps by supporting newer GMs or smaller teams with knowledge they may be missing. That means dealers can confidently hire fresh candidates instead of searching for someone with decades of experience — a profile that, as we mentioned earlier, is becoming increasingly rare.

Measure What's Working With Machine Learning

One of the biggest advantages of machine learning is how it uses data to show where it's helping or hurting your business. You can compare the forecast versus actual turn, track pricing accuracy, and monitor reduction in inventory days. Have your strategies hit the mark? Is there something you’re still missing? Machine learning keeps up-to-the-minute updates on your dashboards to ensure you’re aware of all your operations and so you can act in real time.

The question isn’t whether you should use machine learning but how? It allows you to shift from reactive inventory management to proactive management, helping you determine the conditions instead of being a victim of them.

To learn how CDK integrates machine learning into its inventory solutions, contact us.