Digital Transformation in Automotive: Customer Life Cycle Management Strategies

Introduction

The automotive industry is undergoing a transformative period due to the proliferation of digital technologies. Digital transformation is a crucial aspect for traditional automotive enterprises to remain competitive in the market. Customer life cycle management (CLM) is a strategic process that helps automotive enterprises to manage the customer journey from initial contact to post-purchase activities. This literature review will focus on the digital transformation of traditional automotive enterprises based on CLM.

Digital Transformation of Traditional Automotive Enterprises

According to a report by McKinsey & Company, the automotive industry is undergoing a digital transformation that is changing the way customers interact with automotive enterprises. Traditional automotive enterprises are investing in digital technologies to improve customer experience, increase customer engagement, and enhance customer loyalty. Digital technologies such as big data analytics, artificial intelligence (AI), and the Internet of Things (IoT) are being used to collect and analyze customer data to personalize the customer experience.

Customer Life Cycle Management

CLM is a strategic process that helps automotive enterprises to manage the customer journey from initial contact to post-purchase activities. According to a report by Deloitte, CLM is a key driver of customer loyalty and retention. CLM helps automotive enterprises to understand customer needs and preferences, which enables them to provide personalized experiences and improve customer satisfaction. CLM also helps automotive enterprises to identify opportunities for cross-selling and upselling.

The Role of Digital Technologies in CLM

Digital technologies play a crucial role in CLM. The use of big data analytics enables automotive enterprises to collect and analyze customer data to gain insights into customer behavior and preferences. AI-powered chatbots and virtual assistants are being used to provide personalized customer service and support. The IoT is being used to collect data from vehicles to provide proactive maintenance and repair services.

References

Mckinsey & Company. (2016). Advanced analytics in automotive: Managing complexity and enabling agility. Retrieved from https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/advanced-analytics-in-automotive-managing-complexity-and-enabling-agility

Deloitte. (2018). Customer lifecycle management in the automotive industry. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/de/Documents/manufacturing/Customer-lifecycle-management-in-the-automotive-industry.pdf