By Lois Valente, Global Solutions Leader, Convenience and Capacity
By Lois Valente, Global Solutions Leader, Convenience and Capacity
Have you ever wondered why some automotive retailers consistently outperform others? Often KPIs look great, but it’s no longer enough to simply identify good performance; we need to understand the ‘why’ behind it.
This captures the challenge that plays out daily across automotive networks. OEMs need to establish what actually worked, and more importantly how they can do it again, deliberately, at scale.
The hidden knowledge gap
In global or national field programs, no matter how well-designed they are, outcomes hinge on human interaction. Thousands of data points track transactional and financial results. But the real impact – the conversation that turned around a frustrated customer, the new approach a coach used with a hesitant service advisor – is rarely captured.
Similarly, traditional retailers chase metrics. If satisfaction is low, we act. If sales dip, we react. But what if we could understand the causal chain that led to the outcome?
Imagine knowing, not guessing, that:
A specific coaching style improves first-time-fix rates.
Tailored training boosts upsell performance.
A slight change in how feedback is delivered increases retention.
With these insights, we stop pushing generic solutions and start amplifying what actually works in the right place, at the right time.
This is the grey zone between strategy and execution, between insight and intuition. And until now, it’s been difficult to measure, let alone optimize.
The hidden knowledge gap
In global or national field programs, no matter how well-designed they are, outcomes hinge on human interaction. Thousands of data points track transactional and financial results. But the real impact – the conversation that turned around a frustrated customer, the new approach a coach used with a hesitant service advisor – is rarely captured.
Similarly, traditional retailers chase metrics. If satisfaction is low, we act. If sales dip, we react. But what if we could understand the causal chain that led to the outcome?
Imagine knowing, not guessing, that:
A specific coaching style improves first-time-fix rates.
Tailored training boosts upsell performance.
A slight change in how feedback is delivered increases retention.
With these insights, we stop pushing generic solutions and start amplifying what actually works in the right place, at the right time.
This is the grey zone between strategy and execution, between insight and intuition. And until now, it’s been difficult to measure, let alone optimize.
Evidence over instinct
At MSX, we developed a solution that could capture this level of information and reflected the reality of what was tried, what was said, and what sparked change. The outcome, rich with context and personal insight, combined with the power of AI, began to recognize patterns. It uncovered consistent behaviors, recurring challenges, and success factors across regions, not based on assumptions, but on evidence. This evidence-based learning can then be used to empower OEMs to scale what works, where it works, and why it works.
At the heart of this approach is a continuous cycle: capture, learn, validate, scale.
If a coach tries something new, the system listens, AI identifies a trend, and strategy teams test it. If it proves effective, it becomes a shared best practice. Suddenly, what once felt anecdotal becomes actionable.
We’re no longer relying on instinct or isolated success stories. We’re building a learning organization that adapts in real time, that treats the field as a source of insight, not just implementation.
But this isn’t one-size-fits-all solution. What works in a suburban dealership in Spain might look different than a high-volume service center in the US. But by capturing these local variations and connecting them to outcomes, we start to understand what matters where, and why.
It’s an intelligence layer that blends scale with sensitivity. And it’s changing how we steer performance programs.
Evidence over instinct
At MSX, we developed a solution that could capture this level of information and reflected the reality of what was tried, what was said, and what sparked change. The outcome, rich with context and personal insight, combined with the power of AI, began to recognize patterns. It uncovered consistent behaviors, recurring challenges, and success factors across regions, not based on assumptions, but on evidence. This evidence-based learning can then be used to empower OEMs to scale what works, where it works, and why it works.
At the heart of this approach is a continuous cycle: capture, learn, validate, scale.
If a coach tries something new, the system listens, AI identifies a trend, and strategy teams test it. If it proves effective, it becomes a shared best practice. Suddenly, what once felt anecdotal becomes actionable.
We’re no longer relying on instinct or isolated success stories. We’re building a learning organization that adapts in real time, that treats the field as a source of insight, not just implementation.
But this isn’t one-size-fits-all solution. What works in a suburban dealership in Spain might look different than a high-volume service center in the US. But by capturing these local variations and connecting them to outcomes, we start to understand what matters where, and why.
It’s an intelligence layer that blends scale with sensitivity. And it’s changing how we steer performance programs.
Real voices. Real data. Real impact.
We use voice-to-text tools to gather reflections from the field that are then processed through large language models. These models analyze sentiment, extract themes, and identify success factors, and help us understand what made it work.
This closed-loop system captures the actual action, not what we believe should have happened. It identifies what has been effective, and it allows us to test those insights across the network to see if they hold true elsewhere.
The benefits of this approach aren’t just incremental but also transformative.
We move from reactive to proactive. Instead of waiting for KPIs to dip before taking action, we anticipate challenges and opportunities before they surface. This shift empowers leaders to steer with foresight, not hindsight.
Real voices. Real data. Real impact.
We use voice-to-text tools to gather reflections from the field that are then processed through large language models. These models analyze sentiment, extract themes, and identify success factors, and help us understand what made it work.
This closed-loop system captures the actual action, not what we believe should have happened. It identifies what has been effective, and it allows us to test those insights across the network to see if they hold true elsewhere.
The benefits of this approach aren’t just incremental but also transformative.
We move from reactive to proactive. Instead of waiting for KPIs to dip before taking action, we anticipate challenges and opportunities before they surface. This shift empowers leaders to steer with foresight, not hindsight.
This is how we move from performance to precision. From isolated wins to repeatable excellence. From good enough to truly great.
This is how we move from performance to precision. From isolated wins to repeatable excellence. From good enough to truly great.
MSX Contour: Turning insight into impact
MSX Contour is a powerful platform that captures real-world insights, analyzes them with AI, and delivers actionable intelligence back to the business. It connects the dots between activity and business outcomes, helping organizations understand not just what’s working, but why.
The future of automotive retail isn’t just about data. It’s about understanding. It’s about turning real-world experience into repeatable success. It’s about listening differently and acting smarter. Are you ready to lead with insight?
MSX Contour: Turning insight into impact
MSX Contour is a powerful platform that captures real-world insights, analyzes them with AI, and delivers actionable intelligence back to the business. It connects the dots between activity and business outcomes, helping organizations understand not just what’s working, but why.
The future of automotive retail isn’t just about data. It’s about understanding. It’s about turning real-world experience into repeatable success. It’s about listening differently and acting smarter. Are you ready to lead with insight?