Customer loyalty grows with age

by Brad Smith 3 min read April 20, 2017

shutterstock_561446416The auto industry has had an impressive recovery from the Great Recession and has enjoyed steady growth for the past seven years. After bottoming out in 2009 at 10.5 million new vehicle registrations, the industry has grown each year since, culminating in 17.3 million new vehicle registrations in 2016.

However, the rate of growth has been slowing over the past several years, increasing just 1.03 percent from 2015 to 2016. While retail registrations were nearly flat, the growth came from fleet, with a 13.69 percent spike in registrations by government entities and a 5.59 percent increase in commercial/taxi registrations.

When automotive sales growth begins to taper, hanging onto existing customers becomes more important than ever. Fortunately, customer loyalty in the auto industry is rising for manufacturers, dealers and lenders.

The manufacturer loyalty rate through November 2016 was 62.8 percent, up from 59 percent in 2010. At the make level, the loyalty rate went from 50.6 percent in 2010 to 54.5 percent through November 2016.  Loyalty to a specific dealer is significantly lower but still on the rise, moving from 19.5 percent in 2010 to 23 percent through November 2016.

Interestingly, 61.3 percent of all new vehicle registrations in 2016 were to customers 45 years old and older. Manufacturers and dealers who can keep these customers in the fold in the next several years are likely to maintain and grow their overall share.

Our recent analysis also looked as how age impacts vehicle purchasing loyalty. In general, older customers tend to be more loyal than younger customers. Manufacturer loyalty rates by age include:

  • 18-24 years old – 58.3 percent
  • 25-34 years old – 55.4 percent
  • 35-44 years old – 59.9 percent
  • 45-54 years old – 64.4 percent
  • 55-64 years old – 68.2 percent
  • 65+ years old – 70.4 percent

General Motors market share still number one

For manufacturer market share in 2016, General Motors led the way at 16.91 percent. However, this is a significant drop from the 24 percent share of total vehicles in operation (VIO) enjoyed by GM. Toyota was second in manufacturer market share at 15.46 percent, followed by Ford Motor Co. at 12.59 percent and FCA US at 11.77 percent. Honda rounded out the top five manufacturers at 11.19 percent.

For manufacturer customer loyalty, however, Tesla came out on top at 73.6 percent, followed by Toyota at 68.7 percent and Subaru at 66.8 percent. Ford and GM round out the top five at 65.7 percent and 64.7 percent respectively.

Pickup trucks claim top model share, loyalty rankings

Pickup trucks again held the top two positions among the most popular vehicles, with the Ford F-150 at 3.06 percent and the Chevy Silverado at 2.61 percent. Honda claimed the next three spots with the Honda Civic (2.53 percent), the Honda CR-V (2.46 percent) and the Honda Accord (2.37 percent).

While the F-150 and Silverado were the most popular models, their competition led the way in customer loyalty. The Ram 1500 full-size pickup truck had a customer loyalty rate of 50.9 percent, followed by the F-150 at 46.3 percent and the Lincoln MKZ at 43.9 percent.

In other trends:

  • Non-luxury small CUV/SUVs were tops in segment market at 17.81 percent, followed by non-luxury mid-size sedans (13.89 percent) and non-luxury mid-size SUVs (13.22 percent).
  • Tesla led the industry with a Conquest/Defection ratio of 13.77 to 1.
  • 4-cylinder engines overtook 6-cylinder engines as the top engine type, 38 percent to 37.4 percent
  • Vehicles in Operation are expected to reach 292 million by 2020

For more information on how to drive customer loyalty rates, visit Experian Automotive.

Related Posts

Customer Spotlight: How Matrix Rental Solutions Strengthens Trust in Affordable Housing

Learn how Matrix continues to deliver a secure, trusted rental experience as fraud tactics evolve. Read more!

July 31, 2026 by Laura Burrows
What Is AI Decisioning?

Every business makes decisions about people and transactions all day long. Should we approve this loan? Is this purchase fraud? Which customer should get this offer, and what should it be? For a long time, those decisions were made in one of two ways: a person reviewed each case by hand, or the company wrote fixed rules, like "approve anyone with a credit score above 700." Both work. Both also leave value on the table. The manual review is slow and hard to scale. The fixed rule can turn away good applicants and is slow to adapt when the market shifts. AI decisioning is a third way. What makes AI decisioning work Instead of relying on a single reviewer or a rigid rule, automated decisioning uses models that learn from data — studying how thousands of past cases turned out, finding the patterns that predict an outcome, and applying them to each new decision, often in real time. The result is faster, more consistent decisions. But a model on its own isn't the whole story. Getting real value from AI decisioning takes good data to learn from, AI analytics to generate insights, the tools to act on it and the governance to keep it compliant. What we've found is that the pieces only pay off when they work together, and that is where we're built differently. A model is only as good as what it learns from, and we pair your data with one of the deepest views of consumer and commercial credit: decades of full-file history and vetted attributes. Then we give you the tools to act on it. Use cases across your business Whether you're trying to grow your customer base, reduce fraud, manage lending risk, or improve collections, automated decisioning brings all the pieces together to make more accurate, consistent and explainable decisions at scale. Fraud and Identity A fraudulent transaction that slips through costs money and erodes trust. Rules are static, and fraudsters move fast. They'll probe boundaries, find the blind spots and move to the next scheme. By the time the rules are updated, they're already three steps ahead. How AI decisioning changes this: AI fraud detection with real-time risk scoring and decisioning across transactions and customer interactions Intelligence that continuously learns from results to help adapt fraud strategies as threats evolve Reduced false positives and less friction for customers at account opening and checkout Identity verification tools that confirm someone is who they say they are without slowing down the experience Credit and Lending Loan approval is where the relationship begins. Credit risk decisioning helps lenders find that delicate balance between approving enough people to grow, but carefully enough to manage risk. Missing that balance means turning away good customers or taking on losses that are difficult to absorb. How AI decisioning changes this: Increased approval opportunities for creditworthy applicants without increasing overall risk Models you can update and deploy quickly as market conditions change, rather than waiting months Ability to run "what-if" scenarios to test how a new strategy would have performed on your historical data before putting it live Collections Which customer should your team reach out to today? Through which channel? What kind of message? If you reach out too aggressively, you push someone who might have recovered into default. If you wait too long, you lose them. If you call someone at work, they resent you; if you text, they might ignore it. If you offer a payment plan, they might accept it, but only if the terms make sense to their financial situation. How AI decisioning changes this: Optimized next-best-action and contact-channel strategies for each individual customer Improved recovery potential through better targeting Less time spent on accounts with a lower propensity to pay, freeing your team for higher-impact cases Ability to segment and test new strategies before rollout Customer Acqusition Finding the right customers is about reaching the right people with the right offer at the right time. To stay competitive, it’s now a requirement to balance growth with risk while creating a seamless experience converting prospects into customers. How AI decisioning changes this: More precise prospect targeting using credit, behavioral, and alternative data, where permitted, to identify consumers most likely to respond Personalized offers delivered in real time Dynamic decision strategies that can be updated quickly as market conditions and customer behavior change Ongoing testing and optimization of acquisition strategies to improve campaign performance and support customer lifetime value Driving results with AI decisioning Every customer interaction is a decision. Businesses that can adapt quickly will be better positioned to grow, manage risk, and deliver the experiences customers expect. The technology will continue to evolve, but the goal remains the same: making informed decisions that balance business objectives, risk, and customer experience. Learn more about our decisioning software

July 27, 2026 by Zohreen Ismail
Why Innovation Matters for Members First Credit Union

Learn how Members First Credit Union uses innovation and data-driven insights to better serve members and expand financial opportunity.

July 24, 2026 by Scarlet Nickel

Subscribe to our Newsletter

Enter your name and email for the latest updates.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Subscribe to our Newsletter

Don't miss out on the latest industry trends and insights!
Subscribe