Game-changing AI opportunities are waiting to be exploited by auto dealers.
More on that in a minute, but let’s first look at technological buzzwords defined in the context of an auto dealership.
Big data is a dataset so big and complex that traditional data-processing software is not able to handle it. Typical dealership data may not be big data. However, it becomes big data if dealership data for the last 30 years is combined with external data such as demographics or behavioral data for every customer.
AI is a computer system that can perform tasks which typically require human intelligence (i.e., decision-making). Dealerships may ask: How does it apply to my dealership? Will robots be taking over my dealership? Hold onto that question for a bit.
Machine and Deep Learning
Machine learning is the science of computers acting without being explicitly programmed. Machine learning approaches are used to create your artificial intelligence-driven recommendations, predictions, and more.
Instead of telling the program what to do, these approaches learn from the data and continuously refine to become more intelligent.
Deep Learning is a sub-area of machine learning that is inspired by how human brains work.
Put simply, it is the art and science of drawing intelligent, actionable information from your data. Some dealers have been doing it in the basic form, called equity mining. In this basic form, it lets you know when your customer’s lease or warranty is expiring and more.
Simple use of analytics can help optimize your service shop or sales, but do you want to take it to the next level by adding external elements? As an example, how is the upcoming weather pattern going to affect your sales or service? What are the marketing efforts needed to minimize or maximize the effect?
How can dealerships take advantage of these opportunities and how do you reach this stage? A simple, seven-step approach can help you get there.
Digitize all your customer engagement touch points: Everything starts with the customer. The power of using these technologies comes when all your customer engagement touch points are digitized.
Digitized customer engagement enables you to make automated, real-time, direct communication with the customer.
Start with the business problem: When you are bringing in intelligent machines, you are not just trying to replicate the smartest person’s thinking and approach, though it can be a good starting point. What these machines enable you to do is put that person on steroids and provide them with unparalleled decision-making support and intelligence.
Bring-in external data: With the digitization of customer touch points, you will have your internal data available and ready. However, the real potential of these technologies comes in when you bring in external data. For each business problem, think through and identify what type of external data you need.
Stitch the data together: The next essential step is to stitch together relevant data for demographic, lifestyle, behavioral, manufacturer, valuations, industry, weather, economic data, along with your dealership data, to create a complete view that the machine can use for its predictions and recommendations.
Test and tune: The good part about machine learning and deep learning algorithms is that it is not a one size fits all solution. There are many algorithms. You can pick and choose the algorithm that best works for your problem. Where do you have extensive data, where do you not, what are you predicting, do you want a recommendation or ranking?
Automate and make it real time: The real power of these technologies comes in when it drives real-time business decisions. Imagine if Amazon analyzed and reviewed the data at the end of the week or month. What if they then decided on what recommendations they should offer the customer? Amazon would be out of business by now.
Learn and improve: The learning and improving capabilities of these technologies is a key strength. If you feel that Amazon and Google recommendations are becoming better and smarter, that is because they are continuously learning. In simplest terms, here is how it works: Let’s say you show the AI robot its conquest marketing report card, that consists of which customers it predicted and which customers bought from the dealership. For every wrong prediction, the machine gives a rap on its hand, and for every right prediction, it rewards itself. The goal is that the AI robot learns from the results and continuously improves. Therefore, this process assures that its next prediction and recommendation cycle is better than the last one. (Wards Industry Voices columnist Vikrant Pathak, below)
Let’s take the example of a dealership-customer interaction. Assume your service adviser has a virtual assistant. Its job is to tell the service advisor who the customer is, their needs and preferences, what is recommended, and what can you upsell/cross-sell to the customer. All of this is in real-time. This virtual assistant can represent the artificial intelligence or, as we call it, Augmented Intelligence you are providing to your dealership personnel.
So, no, I don’t see robots invading your dealership. With these technologies, it is like combining your best salesperson’s intuitiveness with the geek who gives you perfect analysis at the end of the month.
Welcome to the world of augmented intelligence virtual assistant for dealership employee!
Vikrant Pathak is the founder and CEO of myautoIQ that uses machine-learning driven predictions and recommendations to identify and target dealership sales and service customers.