The age of big data is here. Thanks to fast accumulating customer data, there are a growing number of opportunities to improve operation efficiency using big data analysis technology. Here is an interview with some of the Business Insight Team members from Hyundai AutoEver, which is leading analysis at the Hyundai Motor Group.
Q Tell us about the responsibilities of the Business Insight Team?
We are responsible for conducting big data compilation & analysis for Hyundai Motor Group member companies. We exchange ideas with R&D units within the companies, review ways to identify business opportunities and propose big data projects to business units on the frontline. The Business Insight team was first established on July 1, 2017, as a response to a growing demand for big data analysis. The team was created to serve all Hyundai Motor Group companies. The team has therefore been involved in a vast range of projects with different Hyundai Motor Group companies, which in turn has created some interesting challenges for the team. Although, it helps to learn about the business areas of the companies partnering with us, our top priority is always to learn big data analysis techniques which are quickly advancing.
Q What kind of projects have the Business Insight Team been involved in?
We are currently involved in an analysis of repeat buyers of Hyundai Motor Group vehicles in the domestic market, and first time and repeat buyers overseas. There are currently other ongoing projects that focus on analysis of customer behaviors using customer data. In addition to analyzing customer preferences, we are also using big data analysis to improve operations efficiency. For example, we applied a method called text mining to extract useful information from business proposals for overseas partners and identified some common elements for a successful proposal. We are also using big data analysis to predict defects by analyzing photos of parts at the production line, identifying fake used car sales postings and resolving customer complaints.
Q What are some examples of big data analysis for identifying customer preferences?
The analysis of repeat buyers of Hyundai and Kia brand cars is the first thing that comes to my mind. People buy cars for two reasons; as a replacement or as an additional car. Each customer can be described as a set of parameters, such as income level and family composition, and it is possible to analyze them using other elements such as region, gender and age. The analysis revealed that the choice of a new car model differed significantly depending on income, age of children, and age of car. Usually, repeat customers prefer a more upscale car than the one they are replacing. By contrast, they have a clear preference for compact sedans or smaller models when buying a new car as an additional car for spouses and children. The analysis results are verified through cross checking with real customer data collected by the sales division.
Q What is the real potential of big data technology in predicting customer preferences?
Big data technology is evolving rapidly. It is difficult to keep up with advancements in related fields such as statistics, data mining, data science, big data and deep learning. Few years ago, it was very difficult to analyze data accumulated over a single year. But now it is possible to analyze data collected over a decade. What really matters now is how to incorporate new technologies into the work processes of business operations. Although it depends on how much information is collected and how well the data is analyzed, further improvements are certainly possible. It is certainly possible to create data on when someone becomes interested in cars and what ultimately leads to the purchase of one. Other possibilities include collecting data on where a person lives, where the person frequently visits, what kind of hobbies the person enjoys and then making predications on what kind of maintenance services will be required based on the data. We can then inform customers accordingly and make recommendations. This can also pave the way for service provision business in the future. I think the popularization of the Car to Home service will lead to a broadening of the application for big data technology from car to living spaces.
Q What are the key benefits of successfully predicting customer preferences?
It is possible to predict the annual distance driven if we have data on what places the person visits frequently, driving patterns and how much time he or she spends driving. Using this data, it is possible to design the car insurance product best suited for this person and for other people with similar car use patterns. It is similar to an algorithm used by Netflix which analyzes video content consumption patterns and makes recommendations; a very standard use of big data technology. Recommending services or products customers are likely to prefer is one of the biggest merits of big data application. Customers can learn about products ideally suited to them without having to spend time and energy finding them.
Q What kinds of skills are necessary to conduct big data analysis?
Knowledge in statistics and computer programming are the two basic required skills. Knowledge in the relevant business sector as well as an ability to communicate with relevant experts can be very helpful as well. This is because big data experts are often too busy to keep up with all of the new big data technologies being developed. It is more effective to use experts from the specific business area and then apply the latest big data technologies to identify the useful information from the data. ‘Problem solving’ and ‘Analytical capacity’ are two very important qualities as well. Corporations are now demanding to learn more about what current and future generations of customers want. This requires not only the collecting and crunching of data but an incorporation of the latest trends into the analysis.
Q What would be the role and objective of the new big data center?
In July 2019, the Business Insight Team and Big Data Technology Team of Hyundai AutoEver were merged and formed the Big Data Center. The Center was created in response to a growing need for fast high-quality big data analysis. We are aiming to go beyond simply analyzing data and rather to identify meaningful trends from the analysis and then use the data to provide the services and support customers need. Staff will be provided with capacity building opportunities including training in the latest technologies to reduce the time required for data crunching.