Trade-Ins: The Sleeping Giant of Profitability
- How to boost used vehicle profitability by an average of $800.
- Predictive analytics helps you work smarter, not harder.
When you sell cars, you should make a profit. It sounds logical, but it’s not always the case. Whether it’s parked on the lot longer than anticipated or commissions eat into profit, you likely find yourself in a bad dream from time to time.
How do you wake up?
Make pre-owned a priority
With the average new vehicle retailing for $38,000, many consumers can’t afford them. Instead, they’re turning to pre-owned. They fit within budgets and are usually in great condition. To meet the customer demands, you too need to turn to pre-owned.
But I’ve heard it before – “used vehicles require extra grooming and maintenance so it’s not always worthwhile.” This concern is valid because once reconditioning is done, it can be overwhelming to focus on not only moving them off your lot, but moving them off your lot strategically.
Despite needing extra love and attention, they’re sleeping giants ready to wake. Trade-ins can bring in more gross profit than you realize – $800 on average. Pre-owned vehicles have more potential because you can control both variables – how much you buy and sell the car for.
To acquire vehicles, you likely have a pricing tool to identify how much to pay for a trade or which vehicles to pick up at auction.
The challenge with trades is you are ultimately trying to secure a sale, so the trade-in becomes part of the transaction, whether you need it or not. And often, you end up agreeing to pay more than you’d like to get the deal done. You wind up with inventory you can’t sell because you’re fixated on your trade-in price and worried about profitability.
The challenge with auctions is desirability. Everyone wants a specific vehicle and when multiple dealerships are fighting for it, the price gets driven up. You end up paying more for a vehicle than you would have purchasing it from a consumer, and the turnaround price can’t match what you acquired it for.
To move vehicles, you’re probably relying on data mining, some of which is a manual process. Sorting through opportunities, finding ones that look good on paper, and matching them to a vehicle you think they might like based on their profile. It’s a game of chance – roll the dice for this customer, roll again for that customer.
In today’s world, “looking good on paper” and “rolling the dice” aren’t feasible. What happens if your customer moved, took a new job, or is now married with children? Would you know? This is what I call traditional data mining. You need more. The data mining you need is smart and intuitive technology built for today’s modern world.
More than traditional data mining
Smart and intuitive data mining encompasses transactional, demographic, and behavioral data living within your four walls. These types of data fuel a predictive analytics engine. Using this data, the predictive analytics engine analyzes your customer base, compares customers against those in similar life circumstances, and factors in deal history, incentives, and in-stock inventory to determine buying likelihood. Not only does it determine who will buy, it determines what specifically they’ll buy. Your acquired pre-owned vehicles are no longer a liability; they’re an asset.
On average, dealers using this predictive analytics engine generate 15% higher grosses and a 30% sales increase. These trade-in results, thanks to predictive data, are proof of the sleeping giant. Predictive data helps you get the most bang for your buck, and it’s just the beginning of what you could be missing out on.
Working smarter, not harder
Let technology do the hard work – it’s built to work smarter, not harder. A predictive analytics engine utilizing the data already inside your four walls, inside your DMS, helps you overcome the challenge of earning valuable profit on used vehicles. Dealerships willing to change the way they see profit at the per unit level will cut losses, improve inventory, and receive higher returns.
*All data based on a Reynolds study.
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