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Guest Blog - Everything You Need to Know About Leveraging AI in all its Glory with Ilana Shabtay

Posted by Dave Lemmon on July 9, 2018

Automotive AI

By now we’ve all gotten used to the fact that artificial intelligence machines are watching our every move. It’s not even that we’re resigned to it– we welcome it. Most people would probably admit that if they’re buying a cellphone on Amazon, suggestions for a matching case and screen protector are actually really helpful. And think how much time you save if an online grocery store remembers your last order and can automatically fill your cart with the items it predicts you’ll want.

There’s no doubt that we’re living in an artificial intelligence-enhanced world. And the more we get used to this kind of personalized convenience, the more we expect it in every area.

Like car shopping, right?

There’s a lot of buzz around AI in the automotive industry, but it’s usually focused on driverless cars or connected entertainment systems. The average car shopper might be looking for AI-enhanced features on their new vehicle, and they might talk about that with their friends. They probably don’t talk as much about how they’re looking for a dealership with an AI-enhanced, personalized website. But they they absolutely are looking for that– across industries they will choose, recommend, and pay more for personalization. And even if they can’t put their finger on all of the reasons a digital experience is great, they know it when they see it.

From the dealership’s perspective, though, it’s important to know what’s going on behind the scenes– what AI can do for your customers that dramatically improves their experience online. And what it ultimately can do to help you sell more cars.

What is AI, and why is it suddenly all the rage?

Artificial intelligence, roughly defined as machines programmed to do what humans do, has been around for a while. A robot is a crude form of AI. Machines have been able to play chess and backgammon for years. What’s made AI explode onto the scene is the confluence of two things: the rise of big data and the advancement of a subset of AI, machine learning.

Here’s a rundown of both of them:


Big Data

Simply put, big data is…a lot of data. It’s typically characterized by three features: volume, velocity, and variety. Volume refers to the amount of data, velocity means the speed at which it streams in, and variety indicates its different formats. In other words, with big data, we have access to huge amount of data arriving at rapid speed in different forms. Knowing what to do with it– and how– is a big challenge. That’s where predictive analytics comes in.

Predictive Analytics

Predictive analytics is an application of machine learning, which is a type of AI.

In machine learning, computers are programmed not only to perform tasks, but also to use data and learn from it to continually improve performance. In predictive analytics, machines are programmed to analyze huge sets of data, look for patterns, and predict what will happen based on those patterns. If you use Netflix, you see this in action all the time. Watch a few superhero movies and you’ll start to get suggestions for Wonder Woman and The Avengers. Watch Jane the Virgin and The Handmaid’s Tale and you’ll recommendations for titles with “strong female leads.” Netflix knows you well, thanks to predictive analytics.

In order for machine learning and predictive analytics to work, there needs to be a lot of data, more data than any person could analyze themselves. Because it’s not just your movie preferences Netflix needs to predict– it’s all of Netflix’s users’ movie preferences, all around the world. But the more data, the more the technology can apply itself.

So now that we have machine learning, we can actually use big data. And there are a lot of benefits for automotive digital marketing. Let’s take a look.

Big Data and Predictive Analytics for Automotive

On Your Website

One major way big data and predictive analytics can make a difference is on your website.

Take any customer, recently arrived on-site. Machine learning collects data about them immediately: how did they get there? From a Facebook ad? Organic search? What device are they using? What time of day is it? Have they been to your site before? For how long, how many times, and on what pages?

Next, the machine learning tracks what your customer does. What do they click on? How long do they spend on a VDP? What are the in-page interactions? What do they convert on and what do they dismiss? Do they come back later?

At this point the predictive analysis begins: What is this customer likely to do next? What have similar customers done at similar points? Based on this analysis, it’s possible to reach out and engage shoppers with relevant content that can actually help them, and move them forward in the buying process. So a predictive analytics tool might suggest that a customer browsing a service page should book an appointment, and engage them accordingly. It might show someone browsing a VDP an offer on that particular vehicle– and then, if they convert on that offer, take it a step further by allowing them to start valuing their trade on the spot. These actions are designed to convert leads by offering real value to the shopper and actually helping them complete tasks while car shopping. What predictive analytics won’t do is show that same vehicle offer to a first-time visitor who’s clicking around trying to find your address (by the way– why is it hard to find your address? That should be really prominent). That shopper decidedly does not want to see that because it has no value at this point in their journey. To that shopper, you’re just getting in the way.

You might be thinking: it’s not so revolutionary to help a service customer book a service. And you’d be right. So here’s where it’s important to point out the difference between machine learning predictive analytics and rules-based personalization– that is, a system that responds to specific customer behaviors according to a pre-set group of rules. A rules-based system can also personalize to a degree, but it can’t analyze huge amounts of data in real-time. So while it can show every shopper on a VDP the same relevant special, machine learning can see an individual shopper, know that it’s their third time on-site, that they arrived after clicking a Facebook ad for leasing at your dealership, and that they’ve been looking at specs for 15 minutes– and show a targeted lease offer on that same vehicle. For that customer, knowing they were looking to lease and not buy makes all the difference– one offer is relevant and one is not.  It’s not just this first interaction, though. When a customer does convert in a rules-based system, that generally ends the digital conversation, whereas a machine learning system can encourage the next step and keep the interaction going.

The difference between rules-based and machine learning is the difference between a salesperson deducing your interests based on your age or your style or any number of immediately-recognizable but superficial traits– versus one who actually asks you questions about what you need and listens to the answers. The first salesperson will get it right some of the time, but the one who asks questions is going to knock it out of the park.

In Your Data and Strategy

In addition to sifting through reams of data for personalization, machine learning can actually suggest marketing strategies, and implement them. It can ask: How many customers interacted with a conversion tool on your site? At what time of day? On which device? Is this campaign working or does it need to be tweaked? And then it can tweak that campaign on its own.

Predictive analytics can take it a step further: Did shoppers who clicked on one ad before visiting your site have a higher rate of purchase than those who clicked on a different one? When a customer clicked on your Google ad, was it before browsing your site, or after they already converted because they were looking for directions and it was the first search result when they Googled you from their phone? In this way, predictive analytics can lead to amazingly accurate attribution and ROI tracking. It can tell you which of your campaigns actually delivered and which did not. It can tell you where money is worth spending.  

Big data and predictive analytics offer truly revolutionary ways to personalize your website at a  large scale and monitor your ROI with accuracy. These technologies bring the excellence of your customer service to every single digital interaction while saving you money. The best part? When it comes to the possibilities of AI-enhanced websites, we’re just getting started.


Ilana Shabtay is an expert in sales, inbound marketing, and business development. She is constantly thinking about how to perfect conversion funnels to build brand awareness and turn leads into sales. As Director of Business Development at AutoLeadStar, Ilana works to build relationships and find mutually valuable partner solutions. Feel free to reach out to ilana@autoleadstar.com.

Topics: Marketing