You may not know it, but artificial intelligence has already taken over your life.
Okay, maybe it hasn’t taken over. But think about it: you might spend time reading up on the latest AI automotive tech, like self-driving or connected cars. Maybe you use a personal assistant like Alexa or Siri, or a directions platform like Waze, or maybe you just enjoy the customized movie recommendations on Netflix.
If any of that is true, your routine already features some kind of AI– and if not, it will soon. AI has begun cropping up all over our daily lives, from transportation to banking to shopping, and its prevalence is growing all the time.
The digital marketing space is no exception. Evolving marketing technology increasingly uses AI, which means companies have the task– seemingly overwhelming at first, but ultimately beneficial– of mastering and implementing it into their own processes. AI is becoming the key to staying ahead of the competition, but it’s not just about being cutting edge and cool. These solutions, as they develop, will create new possibilities for building revenue through a dramatically improved customer experience. They also hold the potential for increasing dealership efficiency and productivity so staff can focus on high-value activities instead of getting bogged down in maintenance and busywork.
So what are some of these technologies, how do they work, and what do they mean for your dealership’s marketing strategy? Let’s dive in.
What is AI?
Artificial intelligence, or AI, is a system where a machine is trained to do work originally performed by a human. Robots are a type of AI. Facial recognition software is AI. Remember Deep Blue, the computer that beat World Chess Champion Garry Kasparov in the late ‘90s? AI.
What is machine learning?
Along with artificial intelligence, you may also be hearing about machine learning. Though people often use the term interchangeably with AI, the two are actually not the same. Instead, machine learning is a type of AI where machines are trained not only to perform a task, but also to learn and improve– like humans. So for example, a machine learning translation program is able to understand and imitate human speech– but it can also improve its own language skills using each of its experiences as input. If you’ve ever used a sub-par translation tool, you understand how great this can be.
What are some examples of machine learning?
Aside from the aforementioned translation tool, there are many common uses of machine learning today:
- Pandora uses machine learning to build and continuously improve your personalized playlists.
- Personalized shopping recommendations. You know how Amazon offers you products similar to ones you’ve browsed, and presents customized recommendations when you visit the site? This, my friends, is machine learning.
- Risk management software. When you get a call from your bank about a suspicious charge, that’s due to the machine learning systems monitoring your account. Huge amounts of data about your likely purchases and locations go into these alerts.
What is predictive analytics?
One other important term is predictive analytics, which is an application of machine learning (which, as you’ll recall, is an application of AI). Here’s a basic rundown: predictive analytics uses machine learning to analyze data from past events and, based upon that, predict what will happen in the future.
For example, predictive analytics can take data from customers’ online shopping behavior and use it to predict what will appeal to them, and to other future customers. More on that in a minute.
Wait, is AI new? Robots have been around for a while. Why the sudden buzz?
Technically, AI is not new. What’s changed is that in the past few years, its applications have developed rapidly– and, most importantly, have become available to a much wider audience. AI’s usability, accessibility, versatility, affordability– these are all exploding onto the scene, with companies big and small starting to get in on the action. Want to see a timeline of crucial AI developments? Check out this article and scroll down for a timeline of the last hundred years of AI.
Why is marketing technology starting to include more AI?
So you might look at all this new artificial intelligence and think to yourself: this is cool, but why is it important for marketing?
The answer really boils down to two things: accuracy and scalability.
Artificial intelligence technologies have the capacity to take huge amounts of data, process them, and then take action. They can enable you to reach customers with personalized accuracy across a variety of platforms, processes, and channels. Predictive analytics, in particular, allows for reams of data to create reasonable predictions about customer interest– and then act on them in real time. The possibilities are staggering.
Think about it: your team pays attention to every customer who walks through your showroom doors. But things get a lot dicier when it comes to the digital customer, faceless, nameless, and inaccessible as they are. How can a website possibly provide the personalization of the showroom? How can lead generation, capture, and follow-up be effective for a customer who is anonymous but also demands customization throughout the shopping process? And most urgently, how can dealerships find the time to create the perfect website experience, the perfect follow-up, the perfect nurture for every single customer without completely draining its resources?
Put simply: machines can handle a heavier load than humans– and they can handle it well. They can analyze more data and make more accurate predictions, they can think fast, make decisions, and take the feedback from those decisions to make more, better decisions in the future.
In this way, AI can take work off your hands, and reach more customers with personalized value than ever before. That means you can build loyalty, foster engagement, and boost sales to a much larger market than you could without AI.
So let’s get specific. Here are some of the ways AI will change the face of digital marketing– and can help your dealership make more sales:
7 Ways AI Will Change Digital Marketing for Dealerships:
1. Smart targeting on your website
Smart targeting means using predictive analytics to reach each shopper on your website with the individualized attention they’d receive at your showroom. Not all of your website visitors are looking for the same thing: some are early stage shoppers doing research, some are looking into a specific car, some need an oil change. Predictive analytics allows your website to speak to each of these visitors differently. So a first-time website visitor might see your Memorial Day special, and someone browsing your service pages might be presented with a maintenance package– all with optimal timing.
Here’s an important note: It is possible to present variation to customers on your website without using machine learning– that is, by creating rules so that each time a customer visits your site, they can be categorized and responded to according to those rules.
So why use machine learning? Accuracy and scalability, of course. AI creates a system that continuously tracks and synthesizes data, and uses it to make real-time predictions about customer interest, deliver relevant content, and record the customer’s response. This type of system is both more on-target, and more scalable than rule-based personalization. You can reach more people, and with more accurate predictions, with less work. The results over time can be exponential.
2. Targeting and retargeting
Here is an area of marketing technology your dealership likely uses already. Targeting systems track the online behavior of various shoppers and show them ads likely to interest them. Retargeting continues the process after customers visit your site, presenting ads throughout their future internet wanderings for content they already browsed.
In this way, AI increases familiarity with your brand while reminding shoppers of actions they took on your website, or questions they had, encouraging them to come back for more. This is machine learning at work, tracking and synthesizing data, then acting on it in real time.
SEO is another area you likely invest in– and it too relies on AI systems that have recently grown more sophisticated. Creating a great SEO strategy involves optimizing your site so that it is ultra-relevant and valuable for car shoppers– and so that search engines will discern this value and rank your site well for searchers. How do they discern this value? With the help of AI, they actually read and comprehend text, and evaluate it for relevance. Masterful SEO interacts with the current machine learning search engines to bring more traffic to your dealership website.
4. Sales Efficiency
Customers report a need for an easier, more convenient sales process. In some dealerships, customers can already start their paperwork from the comfort and convenience of their devices, wherever they are. What AI aims to do is totally customize this process with automation and personalized recommendations that actually help customers through the often-confusing process.
Chatbots are becoming more popular as a way of interacting with customers, providing answers, and getting lead contact information. Your dealership may even use them. You can see how chatbots hold a lot of promise: they can provide 24/7 assistance to customers, they can answer questions that BDCs don’t have time for, they can access large databases of information and provide accuracy on a huge breadth of topics. Language processing, a major field of AI, presents many challenges in terms of nuance and idiom– the comprehension you can imagine would be hard for a computer– but as this technology develops, it becomes highly impactful for businesses.
Dealerships, like all other business, are unfortunately at risk of being hacked. Emerging AI technologies aim to build cybersecurity with a new level of sophistication: customized systems that eliminate uniformity across different companies and industries, the ability to identify threats early, and the capacity to sift through data and detect suspicious activity in your DMS or CRM without much input from your team.
7. Personalized follow-up, lead nurture, and relationship building
With AI, the tedium of segmenting customers into appropriate follow-up can be largely mitigated: predictive analytics can perform these tasks automatically and provide constant contact with customers throughout the buying cycle.
For example: let’s say you have 10 customers who purchased vehicles the same day. And let’s say you send a follow-up note to every customer two weeks after purchase. If it turns out that nine customers are satisfied, but one is not, and has returned to your dealership for help several times after purchase, you might want to change your follow-up with that customer. But as it is now, you would need to manually move that shopper to a different campaign. AI would be able to automatically adapt the next steps for each customer based on information updates, cutting a lot of planning and tweaking out of your day.
As AI develops, it promises to provide new leads with customized assistance to help them along the buying process, and to stay in touch with previous buyers with vehicle updates, continuous education, and service reminders. Again, the advantage of machine learning is the ability to provide these services with much greater accuracy and scalability automatically and in real time.
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This blog was originally published on May 25, 2017. It has been updated to reflect current AI trends.