By Felipe Cruz, Global Solutions Leader, MSX
By Felipe Cruz, Global Solutions Leader, MSX
Automotive retail operations are becoming increasingly complex due to advancements in technology, evolving consumer behaviors, and heightened competition. In this dynamic environment, retailers must make well-informed decisions regarding inventory management, supply chain optimization, and overall business strategies. To achieve this, it is critical to understand the true factors that influence business outcomes.
In this article, we explore how MSX has been leveraging its expertise for over 20 years to navigate the complexities of automotive retail operations. We harness the capabilities of our expert teams to manually formulate and test causal relationships, rather than relying solely on the automated, technologically curated solutions available on the market. This unique approach, which few players in the market possess, allows us to gain deeper insights and make more informed decisions. Consequently, we believe we are in a prime position to invest even further in this field.
Understanding causal modelling
Causal modelling offers a cutting-edge, scientific approach to deciphering these intricate relationships between variables and their impact on business performance. By moving beyond traditional correlations, it identifies and validates cause-and-effect relationships, providing actionable insights for dealerships. This method, recently gaining traction in the automotive retail sector, has the potential to revolutionize how businesses enhance their performance and improve customer satisfaction.
This methodology helps us understand the true causes behind different outcomes by analyzing how various factors are related. Unlike simple correlations—such as the perception that having more technicians correlates with completing more maintenance work—causal modelling delves deeper to identify whether increasing the number of technicians directly causes an increase in maintenance output. This distinction is essential for making decisions that yield measurable results.
Understanding causal modelling
Causal modelling offers a cutting-edge, scientific approach to deciphering these intricate relationships between variables and their impact on business performance. By moving beyond traditional correlations, it identifies and validates cause-and-effect relationships, providing actionable insights for dealerships. This method, recently gaining traction in the automotive retail sector, has the potential to revolutionize how businesses enhance their performance and improve customer satisfaction.
This methodology helps us understand the true causes behind different outcomes by analyzing how various factors are related. Unlike simple correlations—such as the perception that having more technicians correlates with completing more maintenance work—causal modelling delves deeper to identify whether increasing the number of technicians directly causes an increase in maintenance output. This distinction is essential for making decisions that yield measurable results.
Building the model
To develop a robust causal model, access to comprehensive dealership data is paramount. This includes both financial and operational metrics. Analysts typically track and analyze up to 50 key performance indicators (KPIs) daily to model the expected influences of various factors. Mathematical and statistical techniques are then applied to predict the likelihood of one variable impacting another.
However, one of the key challenges in automotive retail is linking consultant recommendations to measurable improvements. Often, there is limited evidence directly connecting specific recommendations to desired outcomes. Causal modelling addresses this gap by statistically identifying which actions drive results and which do not. Over time, this approach helps create tailored action plans that maximize positive outcomes and minimize ineffective efforts.
Building the model
To develop a robust causal model, access to comprehensive dealership data is paramount. This includes both financial and operational metrics. Analysts typically track and analyze up to 50 key performance indicators (KPIs) daily to model the expected influences of various factors. Mathematical and statistical techniques are then applied to predict the likelihood of one variable impacting another.
However, one of the key challenges in automotive retail is linking consultant recommendations to measurable improvements. Often, there is limited evidence directly connecting specific recommendations to desired outcomes. Causal modelling addresses this gap by statistically identifying which actions drive results and which do not. Over time, this approach helps create tailored action plans that maximize positive outcomes and minimize ineffective efforts.
From correlations to causal relationships
The ultimate goal is to move beyond surface-level correlations to uncover the deeper drivers of business success. By analyzing a wide range of data and metrics, dealerships can uncover hidden factors influencing their operations. For example, understanding how staff training impacts financial performance can lead to more effective training programs and, consequently, better dealership results.
From correlations to causal relationships
The ultimate goal is to move beyond surface-level correlations to uncover the deeper drivers of business success. By analyzing a wide range of data and metrics, dealerships can uncover hidden factors influencing their operations. For example, understanding how staff training impacts financial performance can lead to more effective training programs and, consequently, better dealership results.
Real-world applications
Causal modelling is already demonstrating its value in practical scenarios. For instance, using data from dealership visits and interventions, analysts can trace specific actions back to measurable improvements, such as increased new car sales. MSX’s tools, like Insight BM, which generate KPIs, can be further enhanced with causal analysis to provide prescriptive recommendations rooted in data-driven insights.
Additionally, visualizing these causal relationships offers stakeholders a clear understanding of how various factors influence each other. Insight BM takes this a step further by providing in-depth analysis and visualization of business data, helping uncover hidden patterns and insights. For example, it might show how staff training influences customer satisfaction, which in turn drives sales and profitability.
Real-world applications
Causal modelling is already demonstrating its value in practical scenarios. For instance, using data from dealership visits and interventions, analysts can trace specific actions back to measurable improvements, such as increased new car sales. MSX’s tools, like Insight BM, which generate KPIs, can be further enhanced with causal analysis to provide prescriptive recommendations rooted in data-driven insights.
Additionally, visualizing these causal relationships offers stakeholders a clear understanding of how various factors influence each other. Insight BM takes this a step further by providing in-depth analysis and visualization of business data, helping uncover hidden patterns and insights. For example, it might show how staff training influences customer satisfaction, which in turn drives sales and profitability.
The future of automotive retail analytics
Causal modelling represents a significant step forward in analytics for automotive retail. By combining advanced data collection, robust statistical methods, and actionable insights, this approach equips businesses to tackle modern challenges with confidence. As the industry continues to embrace causal analysis, the potential for improved performance and customer satisfaction grows exponentially.
At MSX, we are at the forefront of transforming automotive retail, setting new standards for the industry. Our pioneering efforts are backed by our deep expertise and extensive history in delivering transformative insights and a scientific approach through our dealership improvement programs, positioning us as a leading partner in the field.
We empower our clients to make data-driven decisions that drive significant results and secure a competitive edge. As we continue to advance, the integration of these models with cutting-edge predictive tools and machine learning technologies promises to revolutionize analytics in this sector, paving the way for a more dynamic and insightful future.
The future of automotive retail analytics
Causal modelling represents a significant step forward in analytics for automotive retail. By combining advanced data collection, robust statistical methods, and actionable insights, this approach equips businesses to tackle modern challenges with confidence. As the industry continues to embrace causal analysis, the potential for improved performance and customer satisfaction grows exponentially.
At MSX, we are at the forefront of transforming automotive retail, setting new standards for the industry. Our pioneering efforts are backed by our deep expertise and extensive history in delivering transformative insights and a scientific approach through our dealership improvement programs, positioning us as a leading partner in the field.
We empower our clients to make data-driven decisions that drive significant results and secure a competitive edge. As we continue to advance, the integration of these models with cutting-edge predictive tools and machine learning technologies promises to revolutionize analytics in this sector, paving the way for a more dynamic and insightful future.