Understanding the Buzz Around Data Science at Your Dealership

At AutoAlert, we empower innovative automotive partners to improve data-driven customer experiences every day. But have you ever wondered about the science behind all your data?

AutoAlert has a dedicated team of data scientists, complete with Ph.D. degrees, whose sole job is to revolutionize the dealership-customer relationship for your dealerships.

So, what can data science do? And how can data science innovate the automotive industry?

This article will give you a crash course on defining data science terms and understanding how it’s utilized in automotive software.

You may have heard of universal terms such as artificial intelligence or data mining, but these are just a few types of data-related technologies.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Data Mining
• Data Science

Such creative use of correlated and overlapping technical terms has the unfortunate effect of propagating both confusion and a tendency to associate them more with hype than with the underlying capabilities that created the buzz in the first place.

Artificial Intelligence

Artificial Intelligence (AI) refers to computers and machines to mimic humans. There are various technologies considered to be AI, including:

• robotics
• self-driving cars
• speech-to-text
• virtual assistants
• and recommendation engines (i.e., “Since you bought A, you might also be interested in B or C”)

It is essential to recognize that these are not purely rule-based tasks with pre-determined responses to every possible scenario. Instead, these tasks require on-the-fly decisions based on a specific set of precise inputs that may be unique.

For example, a virtual assistant that interacts with existing customers may have never seen the exact sequence of letters and words in an incoming email response. However, it still may be capable of accurately determining the basic meaning of that message and generating a coherent and relevant response.

Thus, AI technologies accomplish tasks that typically require human intelligence to complete them because the decision-making process is automated by humans to be performed by machines.

Taking the intelligence analogy further, humans have “taught” devices to make these decisions, or machines have “learned” how to make them.

Machine Learning

Machine Learning is the sub-field of AI that focuses on algorithms and techniques for teaching machines to make decisions when provided with contextual inputs.

Machine learning is a vibrant and mathematically based field with many algorithms applied to many problems.

Deep Learning

Deep Learning is a sub-field within machine learning that focuses on one approach to teaching algorithms. Deep learning uses neural network models inspired by how the human brain and nervous system process information.

While it is a narrow slice of machine learning, deep learning has significantly impacted AI systems that include image and text processing tasks.

AutoAlert’s AutoAssistant solution is just one example of AI technology as it mimics human behavior in processing and responding to messages. AutoAlert built this using machine learning algorithms, including deep learning.

Another example is AutoAlert’s Recall Management Solution. AutoAlert searches the dealership’s DMS for an opportunity for customers recently involved in a recall. AutoAlert may also pull in other data to help the dealer drill down into customer loyalty programs, marketing campaigns, and more, depending on the OEM. The dealership now has an ideal customer to start a dialog and engage with.

All intelligence, data, and science used by AutoAlert have continually revolutionized the dealership-customer relationship.

AutoAlert makes data easy to use and accessible from anywhere, creating direct opportunities for meaningful connections and seamless online and offline experiences.