By Winston Harrell, Strategic Growth Manager, DealerSocket

There is no right way to data-mine; the only wrong thing you can do is let all those sales prospects sit untouched in your CRM. Because on any given day, someone in your customer database is thinking about purchasing a new car. The key is not to be afraid to experiment, especially when it comes to the search parameters you use to ID those in-market opportunities.

That holds true when using a data mining tool that serves up those prospects automatically. Your store’s average credit score, time of ownership, and annual driving miles can make all the difference. And as we all know, being the first to engage means higher margins. Best of all, you don’t have to pay a lead provider to deliver the business you’ve already earned. Let’s dig into six prospect lists you can optimize by simply doing a little homework.

List No. 1: Customers Who Can Lower Their APR While Trading Up

Interest rates are historically low, which means there are opportunities to help past buyers take advantage. Typically, these will be customers with an APR above a certain range. Getting them back into your showroom could mean saving them thousands of dollars in interest while they drive off in a new or newer used car.

Search parameters for this list include average credit score, loan term, and down payment. Specifics vary depending on location. For example, in an area with an average credit score of 650 or less, use 2.5 years or greater as a timeframe of ownership. The reason is that’s the amount of time it takes for consistent payments to improve a credit score.

It’s a good idea, especially during these uncertain times, to conduct a regular credit bureau analysis. What you need is a credit tier profile of your customers. This knowledge will not only fuel your prospecting activities but can also guide your inventory sourcing strategy when finance sources tighten up their guidelines.

List No. 2: Customers Who Can Lower Their Payment While Trading Up

Equity isn’t the only way to help customers lower their payments while trading out to a new car. As many new-car owners discovered in 2020, OEM incentives can have the same effect. For example, it may be possible to identify car owners who are in a less equitable position but are eligible for a significant rebate or zero APR to offset their lack of equity.

Since OEM incentives are fluid, an excellent way to identify this list is by following the guidelines of the book your dealership uses to estimate current equity, then adjust according to incentives offered. An example would be Kelley Blue Book wholesale plus $500, or NADA average trade-in minus $500.

Some data mining solutions will automatically match a prospect’s current vehicle with a vehicle in your inventory that’s similar to the one they already own. That makes it easy to send emails containing your lower payment offer and a link to a vehicle details page highlighting their vehicle’s current model-year version.

List No. 3: Customers Who Declined High-Cost Service Repairs

When customers decline recommended services, it might be an indicator that the repair bill was more than the person was willing to invest. That means there’s a good chance they are weighing their options: Pay $3,000 for that repair or buy a new car? Set your data mining tool or search for customers who declined repair estimates above $1,500 and $3,000. Success will depend on brand and location, so experiment.

List No. 4: Customers Approaching End of Lease

What makes identifying end-of-lease customers a popular activity is there’s a deadline for them to make a decision, and we know the date. So, it’s a far easier process to set up that campaign than one that centers on, let’s say, equity position. The key is to engage these customers six months out and increase the frequency of communications as the deadline approaches.

When building your lists or setting your data mining tool’s parameters, you may want to target a specific model your used car manager wants to add to inventory. You can also search by ZIP code, area code, distance to the dealership, or even finance source. For instance, based on the lease criteria, certain finance sources may come out with a lease pull-ahead program that targets Ford F150s.

List No. 5: Customers Approaching End of Finance Term

As customers approach the end of their finance terms, they may be open to considering a new vehicle. Parameters on what the right timeframe is can be a bit tricky. Location and clientele are major considerations. For example, if the majority of your finance deals are for six years, it might be worth contacting some of your customers as soon as three years into the loan.

Customers who drive 12,000-plus miles per year will be more open to trading their car at a higher frequency rate than those who drive only 8,000 miles per year. Other parameters to experiment with include equity position and payments.

The key with this list is to find owners who are past the honeymoon phase of ownership, as there is always a period when most car owners don’t hate their vehicles but would be willing to trade up if presented with the option.

List No. 6: Customers in an Equity Position on Their Current Vehicle

If you’ve ever data mined, you are familiar with this list. It doesn’t matter when customers purchased their vehicle. If they have a certain amount of equity in their vehicle, they can trade for a new one. During periods like the one we’re in today, this list can be especially helpful to your used car manager.

I recommend slanting search parameters for this type of list toward the used car manager’s needs. Specify makes, models, mileage, and other criteria that you want in used inventory. Once you pull these lists from your CRM, enroll prospects into campaigns that utilize email, text, and phone calls from your BDC or sales team.

While not as “hot” as new lead submissions, these lists should yield many potential car buyers. Plus, it’s a great way to check in with your customers. To make the effort worthwhile, ensure that your salespeople or BDC agents know how to qualify prospects. If someone isn’t interested, close them out and move on.

As previously mentioned, there’s no wrong way to mine your customer database, but a little homework and some experimenting will go a long way toward a fruitful data-mining expedition. But you better have a good process that’s written down, implemented, and managed to turn those prospects into sales.