Data Science Customer-Centric Approach
(Data Science Blog Series, part 3)

This blog is the third article in our AutoAlert Data Scientist Editorial series.

This series provides key data science terms and important uses in automotive software.

Data Science LiFe Cycle

We have touched on a few subjects; vocabulary, history, and geology. Now for the science in Data Science.


There is an overlap between using data mining and data science. They both have in mind the goal of extracting actionable insights from data. Machine learning algorithms are often at the center of creating artificial intelligence (AI) systems to accomplish this goal.

However, the term data science emphasizes the scientific process followed and documented to ensure the quality and integrity of the resulting AI. This process is known as the data science life cycle.

Data Science Life Cycle includes:

  • Defining the business problem and objectives
  • Preparing and maintaining the data (including gathering, cleaning, reformatting, etc.)
  • Exploratory data analyses (including forming and testing hypotheses)
  • Training and testing machine learning models against the business objectives
  • Deploying machine learning models for use in live software
  • Following what is known as the software development lifecycle, or data science life cycle
  • Measuring and monitoring model performance against the business objectives
  • Iterating as necessary

By surrounding machine learning with clear business objectives, data wrangling, data cleaning, statistics, and software development, data science teams can develop and maintain robust artificial intelligence systems that improve everyday processes.


How can AI & Machine Learning Technology Be Used In Automotive Software?

The automotive industry does not lack data. There are many opportunities to help improve the car buying experience. Customer data is everywhere. AutoAlert brings it together in one place and allows dealerships to make it actionable.

AutoAlert’s platforms are designed to help dealerships and OEMs do as much as possible with large amounts of real-time, actionable data.

    Building a customer-centric approach into the data

    The Data Science Life Cycle used by the next-gen CRM tool, AutoAlert CXM, is revolutionizing the dealership-customers relationship.

    AutoAlert CXM offers dealerships a single solution to synchronize dealership communications for a personalized, seamless customer journey from test drive to collecting signatures, both online and in-store, on one screen, on any device. It is fully integrated with AutoAlert’s 20-years, industry-leading data mining solution, AlertMiner.

    AutoAlert CXM data is driven by the customers’ activities & behaviors. Dealers receive a complete picture of their customers with real-time data that shows consumer research and buying behavior. It connects the dots into a clear path of action and provides specific direction for each customer and accountability for dealership teams.

    AutoAlert CXM not only predicts customer behaviors but managers your dealership’s interactions and enhances customer experiences with dynamic, work-from-anywhere technology.