How Data Analytics Plays a Role in the Automotive Industry

by SG Analytics Global Insights & Analytics Company

Private vehicle ownership is one of the important accomplishments of many people, and the automotive industry must also help the public transport systems to enhance the economy. However, different individuals have their unique preferences for a bike or car. How can the automotive industry predict such consumer preferences? This post will discuss the role of data analytics in the automatic industry to solve these queries for business growth.

What is Data Analytics in the Automotive Industry?

Data analytics combines computer programming techniques with statistical modeling methods to describe business performances and extract insights from consumer data. Many consulting firms offer innovative data analytics solutions that are customized for the automotive industry.

The transportation business might want to enable mass transport in a densely populated urban zone. Likewise, a travel agency will prefer distinctive double-decker vehicles capable of withstanding the physical strain of lengthy roads and stormy winds.

Data analysts can help corporations belonging to the automotive industry understand client needs and identify new business opportunities and design improvements. Therefore, companies can optimize their business strategies using professionally validated insights.

Advanced Analytics Solutions for the Automotive Industry

Conventional analytical tools might be suitable for structured data management. However, novel communication platforms have encouraged consumers to express themselves using unstructured data objects. So, advanced analytics solutions that can process unstructured data are also indispensable to the automotive industry.

For example, a person doing an entry-level job might be a sports-bike enthusiast. Meanwhile, citizens might be upset with their public transportation systems. They will express their dissatisfaction through descriptive tweets, online video streaming platforms, and audio clips.

What are the difficulties in handling such unstructured data categories using simple statistics? The data objects—like images, elaborated paragraphs, or audio tracks—do not have a direct numerical basis. After all, these feedback data types are fundamentally different from the star rating systems or like-dislike ratios.

You need to analyze descriptive consumer feedback to gain valuable consumer behavior insights. This aspect explains why many firms must help automotive companies through NLP (natural language processing) in their advanced analytics solutions. These techniques allow you to conduct customer sentiment analysis by processing qualitative reviews.

What is Big Data Analytics in the Automotive Industry?

Consider the following car companies to understand why big data analytics are significant for business success in the automotive industry.

Toyota Motor Corp., a Japanese enterprise, provides its vehicles across 170+ countries.

Ford Motor Company operates in 125+ territories with diverse cultures and client bases.

General Motors produces everything, from cars to trucks, in 30+ nations.

Therefore, automakers and other manufacturers supplying automotive parts must maintain international databases with region-specific customer behavior insights. This endeavor is impossible without integrated automation support and big data analytics solutions.

Big data analytics is a special case of data analysis that encompasses an ever-increasing volume of data that originates from all kinds of sources. Moreover, companies require extensive data storage and pipeline capabilities to build efficient ETL (Extract, Transform, and Load) workflows. Big data in the automotive industry helps firms use analytics for demographically appropriate business decisions.

Use Cases of Data Analytics Solutions in the Automotive Industry

We have discussed several categories of data analytics solutions that fulfill a specific purpose. You can use analytical modeling for simple business queries, unstructured data processing, and large-scale insight.

Still, the automotive industry must efficiently implement different advanced analytics solutions considering how they want to grow their consumer base and compete in the markets. Let us explore the different uses of data analytics in the automotive industry.

Application #1| Sales and Revenue Projections

Standardized data analytics solutions are sufficient for studying the historical performance of vehicle models through sales metrics. You can also understand how launching a new vehicle influences the sales of older models.

However, revenue forecasts require the predictive modeling capabilities offered by advanced analytics solutions. If you use conventional statistical modeling, the geometric extension of historical data might mislead you. Therefore, you require machine learning models to conduct multivariate sales analytics. They help you with more reliable evening projection reporting.