As summer comes to a close, you may have noticed more changes than just the cooling weather. Odds are, if you think back to your most recent customer interactions, you notice the demographics, wants, and needs of your customers changed slightly from what they were even a few months ago.

While collective habits of your consumers may have shifted slightly with the seasons, if you were to think of some of your specific customers, you would notice more drastic changes in their individual wants and needs from you over the years, too.

• This poses the question: How do you know that you’re reaching the right customers right now?

The key to it all is predictive analytics.

Predictive Analytics? What…is That?

Predictive analytics is exactly what it sounds like—data (analytics) gathered from existing customers that can provide insight into future (predictive) customer behavior. For your dealership, this data might include demographic information, purchase history, and certain preferences (think customers who lease cars over purchasing them). It might involve a tech stack or database solution that analyzes your audience against other factors, or your own savvy understanding of your target audiences and the happenings in your community and region. Either way, this is how you should be framing your marketing messages.

Let’s use a simple example:

• Krista is a college student who (according to your database) purchased a used car from your dealership two years ago. She lives in a city apartment near her school and commutes to classes as well as to her part-time job.
• Last year, Krista scheduled a service appointment with you in the early fall, but she hasn’t been back to your dealership since.

Using this information, your dealership’s data helps you predict that Krista is now driving her car to-and-from school again. She is overdue for service, but is now more likely to pay attention to your calls-to-action. So, you decide to reach out to Krista.

In September, you send her an email about the importance of routine service, ending with a “Claim your student discount” offer. A week later, your efforts pay off—Krista stops into your service drive for a discounted inspection, routine maintenance, and a recommended service.

A Changing of the Seasons

Predictive analytics can do a lot more than just bring Krista back to you for maintenance.

Think about what affects your dealership from month-to-month. Around this time of year (fall), you’re likely looking at end-of-year incentives, anticipating new model releases, and preparing for the holiday sales season. Pretty soon, your potential customers will be thinking about these things as well.

A few seasonal points to consider:

• Depending on your area, cooler weather means a change in services for which your customers have a need to get fixed or maintained. AC repairs are few and far between in most markets. Think instead about marketing the maintenance and repairs required to keep customers warm and safe as temperatures cool. You could even target customers you predict (based on your data) may be in need of things like new tires before the threat of snow, or those who fear battery-related trouble in older year models.
• Like we saw with Krista, the back-to-school season will affect some of your younger buyers. Marketing to this demographic with student discounts or with images and suggestions for smaller vehicle owners might prove helpful for your dealership (especially if you have a college or university campus nearby). What other audience segments have needs during fall and at the start of winter?
• New model releases will likely attract customers interested in the latest tech and safety features. They also draw customers looking for deals on previous year models. Targeting each these groups with custom messaging ensures you communicate only what’s relevant to them.

A Changing of Your Customers

While your customer base’s needs, wants, and interests will shift with the incoming season, your customers themselves—their interests, lifestyles, and priorities—also change over time.

Let’s go back to Krista.

• This time, instead of being further into her college career, Krista is two years out of college. She’s a bit older (and maybe wiser), is a graduate and a two-year employee at a steady job in her chosen career field. She’s also recently moved to the suburbs.

Based on your customer data, demographic information, etc., your dealership successfully identifies that Krista is not the same college student that she was a few years ago thinking about the same vehicle needs. A student discount offer will be thrown in the trash (or trash folder), and any mention of a small sedan (like the one she has been driving since college) will, too.

So, the next time she comes in for some repair work, you approach her with a deal that would put her in an SUV for a competitive monthly payment, thanks to some end-of-year sales events on last year’s new models.

All of this sounds great to Krista. Although she didn’t come to your dealership with the intent to purchase a new vehicle, she’s been meaning to start the car shopping experience soon anyway as her sedan ages, and you’ve just made that process increasingly simple for her.

Of course, no one can truly predict the future—but predictive analytics do put you in a position to make likely assumptions about your customers’ intent. If your data, tools, and analytical skills can predict intent and you communicate the most relevant message to that intent, you are going to win some major business more often.