Published on 19-Nov-2013
SPSS Modeler, BA - Business Analytics, BA - Predictive Analytics, Big Data, Big Data & Analytics, Big Data & Analytics: Operations/Fraud/Threats, Business Integration, Business Performance Transformation, Business Resiliency, Empowering People
The BMW Group is the only manufacturer of automobiles and motorcycles worldwide that concentrates entirely on premium standards and outstanding quality for all its brands and across all relevant segments.
Securing customer loyalty plays a key role in the commercial success of automotive companies such as BMW Group. The company is constantly looking for ways to further boost customer satisfaction.
IBM® SPSS® Modeler enables BMW Group to use the wealth of data that is generated every day to detect causes of technical faults, and optimize manufacturing processes to rectify these issues.
Big data analytics boosts customer satisfaction by detecting and rectifying problems in products and processes. The IBM solution provides a “months to days” improvement in the time taken to perform data analyses for the group’s subsidiaries.
To read a German version of this case study, click here.
Satisfied customers are more likely to exhibit brand loyalty when buying vehicles and using authorized repair workshops, compared to those who are unhappy with the product or service they have received. Sales of new vehicles and repairs to older ones can have a significant impact on the commercial success of automobile manufacturers.
As part of a broader initiative to boost customer satisfaction, BMW Group established a competence center called FACTS (Field-data Analysis for Customer Satisfaction), which is responsible for analyzing the huge amounts of data available within the organization. The company aimed to gain a constant stream of new insights into the quality of its products, as well as other factors that influence customer satisfaction. It wanted to use these findings to improve the parts of its business that are most important to commercial success.
Every day the amount of data available, which includes information on warranties as well as diagnostics and repairs from the group’s mechanics workshops, increases by around 30 gigabytes. BMW Group analyzes this data using IBM SPSS data mining tools.
The FACTS team, which numbers around 30 employees, serves more than 40 BMW business units worldwide. The team is responsible for performing comprehensive data analyses, and currently receives approximately 1,000 analysis requests every year – although the high level of interest in the service is causing this figure to rise.
Rapidly identifying and rectifying causes of faults
The team often receives requests for analyses relating to the detection and localization of problems in new models of vehicles before the products are launched. By combining and analyzing data from numerous test drives of prototypes, approximately 15,000 “error memories” recorded by the vehicles, and recent reports from workshops, BMW can identify and eliminate weaknesses in new models before they go into production, thus reducing the need for frequent repairs in the future.
In the past, it took several months to provide most data analyses; now, the new competence center and the IBM SPSS platform can deliver them in a matter of days. This huge time-saving enables the company to adapt its product designs and manufacturing processes early to avoid recurring errors.
Eliminating faults in production processes using predictive analytics and rapidly generating instructions for fixes to common problems can significantly boost customer satisfaction and cut long-term costs, since preventing errors reduces the need for cars to visit workshops. Faster repairs ensure that serviced cars can be returned to their owners earlier.
BMW Group is using further analyses to work out which factors are most important for customers and have the greatest impact on their satisfaction. For example, one of the most important factors that has been found to negatively affect satisfaction is when a car needs multiple repairs in a short space of time. By intelligently analyzing technical data with the IBM SPSS data mining solution and combining it with feedback from customers and partners, the company is often very successful in identifying such factors and achieving its aims.
Automating analysis boosts efficiency
Different subsidiaries and departments within BMW Group often issue requests for very similar information, so the FACTS team often needs to use the same types of analysis multiple times for different purposes. IBM SPSS enables the team to develop generic analysis solutions that can be used to provide answers to a range of queries.
Around 250 of these analytics applications are now available, enabling the platform’s 500+ users to perform their own analyses. The proportion of analytics provided on a self-service basis is rising continuously.
Co-operation promotes future projects
The collaboration of IBM and BMW Group has also been the driving force behind many other interesting initiatives. For example, the two companies recently examined whether and how IBM Watson™, the AI (artificial intelligence) solution developed by IBM, can be used to improve the identification of vehicular faults in the future.
The findings generated by IBM SPSS enable BMW Group to continuously improve customer service, further boosting satisfaction. This process of continuous improvement will also help the premium car manufacturer to gain competitive advantage in the future.
About IBM Business Analytics
IBM Business Analytics software delivers data-driven insights that help organizations work smarter and outperform their peers. This comprehensive portfolio includes solutions for business intelligence, predictive analytics and decision management, performance management, and risk management.
Business Analytics solutions enable companies to identify and visualize trends and patterns in areas, such as customer analytics, that can have a profound effect on business performance. They can compare scenarios, anticipate potential threats and opportunities, better plan, budget and forecast resources, balance risks against expected returns and work to meet regulatory requirements. By making analytics widely available, organizations can align tactical and strategic decision-making to achieve business goals.
For more information
For further information please visit ibm.com/business-analytics
Products and services used
IBM products and services that were used in this case study.
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