Data-driven post-sales client activation strategies in the automative sector

Challenge

In the automotive sector, marked by fierce competition and high-value products, customer loyalty plays a decisive role in long-term success. In this environment, personalized offers and targeted campaigns become essential tools, but they are difficult to scale manually.

At the same time, technological innovation is reshaping the way businesses operate. Artificial Intelligence (AI) has emerged as a powerful tool to accelerate processes and deliver insights that were previously difficult to obtain. While it does not replace the expertise and relationship-building role of sales advisors, AI adds a valuable layer of intelligence by uncovering complex patterns in customer behavior and supporting faster, data-driven decision-making. This intelligence requires more than just good models, it requires platforms that bridge the gap between data science and business users.

‍

Approach

The project aimed to answer two critical questions: which customers with annual service bookings are most likely to change their vehicle for a new one, and which are most likely to skip or abandon the brand’s maintenance program.

Before developing any analytical models, it was essential to begin with a thorough mapping of the available data and an in-depth alignment with the client. For potential vehicle changers, variables such as vehicle age, mileage, and customer purchasing history played a central role. For churn identification, the emphasis was on service intervals, adherence patterns, and vehicle antiquity. Establishing this foundation ensured that the analytical methodology would not only be technically sound but also directly aligned with the business context.

One of the first considerations was the nature of the automotive sector. Unlike other industries where products are replaced frequently, cars are high-value assets with a medium-to-long lifetime. This makes the decision to change vehicles more complex, involving financial, emotional, and lifestyle factors. Predicting this behavior, therefore, requires special attention, as customer signals are less frequent and often more subtle compared to industries with shorter product cycles. Understanding this nuance was key in shaping the approach.

In parallel, it was critical to align for this automotive player what a “churner” in the context of after-sales services meant for them. Unlike subscription-based businesses where churn is relatively straightforward, here it is represented by clients who do not comply with the scheduled maintenance program.

‍

Solution

The proposed solution was structured around two main analytical streams, each addressing a distinct business challenge within the post-sales department.

The first stream focused on developing a predictive model capable of learning from past cases where customers decided to replace their vehicles. Using a traditional machine learning approach, the model analyzed historical customer behavior and identified patterns that signal a higher likelihood of vehicle change. Beyond simply flagging clients potentially willing to change their vehicle, a complementary model was also designed to recommend which new model should be offered to each customer, increasing the probability of acceptance.

To further empower the sales team, these insights were enriched using GenAI. Based on the predictive outputs, GenAI generated tailored sales scripts that included the rationale for targeting a specific customer and a clear breakdown of the medium-term savings that could be achieved with the proposed vehicle. These savings included information that provided sales advisors with persuasive and client-specific arguments to support their conversations.

The second stream contemplated a dedicated model to identify customers most likely to abandon or fail to comply with the maintenance schedule. The model incorporated a wide range of variables, including service history, vehicle usage patterns, and contextual factors such as proximity to the service center. Additionally, the model analyzed potential motives behind non-compliance, including dissatisfaction, perceived costs, logistical challenges, and notably, the likelihood of changing vehicles. Customers flagged for high churn risk due to an impending vehicle change were highlighted and passed to the sales team, allowing their insights to feed into the first stream. This integration ensured that both maintenance compliance and vehicle replacement strategies were aligned, creating a more holistic, proactive approach to customer engagement.

Together, these two streams provided a comprehensive solution that combined predictive analytics with GenAI capabilities, equipping sales and post-sales teams with actionable intelligence to proactively engage clients, improve loyalty, and unlock new revenue opportunities. Beyond augmenting the commercial team with AI-generated insights, this strategy also centered their focus on the most critical cases. Previously, the team was pushed to pursue broad customer segments based on simple business rules, an approach that often led to lower conversion rates and rising dissatisfaction.

Rather than overwhelming the client with technical complexity or expecting internal teams to host and manage predictive systems, LTPlabs introduced AIR, an off-the-shelf platform that simplifies AI deployment.

To accelerate adoption and ensure easy access for the client, both streams were integrated into AIR, a platform hosted within LTPlabs infrastructure. AIR enabled:

• ‍End-to-end model management, including access to results in a simple, intuitive way and running predictions on demand;‍
• Self-service integration, requiring little to no configuration, which proved to be essential for running the pilots early in their development phase, giving client teams the chance to validate model results as soon as possible and provide continuous feedback;
• Browser-based access - teams could access insights via a simple web interface, avoiding technical barriers or installations;
• Scalable deployment, allowing heavy models to run on-demand in a reliable infrastructure, supporting daily use and experimentation during the pilot phase.

‍

Results

Pilots for both streams were conducted in a limited number of service centers.

In the first stream, focused on vehicle replacement prediction, the pilot resulted in two confirmed car sales and several proposals sent to targeted clients. Beyond immediate revenue, the project strengthened relationships between sales advisors and customers, laying the foundation for future opportunities.

For the second stream, predicting maintenance compliance and customer churn, the model was designed to minimize false negatives. It achieved an error rate of only 10%, with most errors being false positives. It was an acceptable trade-off since the cost of missing a potential churner is much higher than acting on a non-risk customer.

The flexibility of the delivery via AIR meant the client did not need to wait for full production readiness or integration into legacy systems. The platform became the bridge between advanced analytics and real-world business execution.

‍

‍