Supercharging the automotive
customer experience

Customer experience in today’s automotive landscape

In an era defined by heightened customer expectations driven by online shopping, the automotive industry faces the increasing need to deliver not just quality vehicles, but also a seamless shop-and-buy experience. In a recent survey conducted by Deloitte,¹ visibility to order status and accurate estimated time of arrival (ETA) dates are recognized as critical components for building trust, driving customer engagement, and differentiating original equipment manufacturers (OEMs) within a competitive marketplace.

A previous study conducted as part of our market research identified several critical factors influencing consumer decisions to disengage during their vehicle purchasing process. Notably, the findings indicate that up to 20% of global customers walk away due to the unavailability of their preferred vehicle, highlighting a significant disconnect between inventory and actual customer demand.

Figure 1. Customer dealership walkaway [ES1] factors

Source: 2024 Deloitte Consumer Loyalty Survey; 2,500 participants and ~1,500 respondents to the question: Why did you decide not to purchase from the [brand] dealer?

Figure 1 shows why potential customers leave dealerships without buying. The walkaway factors include availability (10%–20%) and lead time (3%–6%) as key indicators.

When vehicles are not immediately available, customers can work with dealerships to determine the next expected delivery date for their chosen trim and color, based on the manufacturing plant’s build-to-stock production pipeline. To bridge the gap between limited inventory and customer preferences—especially for premium, customized vehicles—dealerships can offer a build-to-order (BTO) process, enabling customers to design their ideal vehicle. Research indicates that customers who opt for BTO are willing to wait for their purchase, especially when overall delivery times are kept short.

Figure 2. Comparison of willingness to wait (W2W) across years (% acceptance)

Source: 2024 Deloitte Consumer Loyalty Survey; 2,500 participants and ~1,500 respondents to the question: Why did you decide not to purchase from the [brand] dealer?

Figure 2 shows the willingness of customers in the US to wait for BTO vehicles. Willingness to wait drops significantly after a 14-day wait period, highlighting a strong preference for shorter wait times.

These insights collectively underscore opportunities for OEMs, dealers, and manufacturers to not only better align stock with market preferences but also streamline order management processes and enhance transparency throughout the purchasing experience. 

The value of order status visibility

Today’s customers are accustomed to real-time updates in nearly every aspect of their lives, thanks to e-commerce platforms. When purchasing a vehicle, often a significant financial and emotional investment, customers expect the same level of transparency. Providing clear, timely updates on a vehicle’s production status is no longer a luxury; it is a strategic imperative.

Key benefits of order status visibility include:

• Elevated customer engagement: Proactive communication about order milestones (e.g., order confirmation, order scheduled and sequenced, in production, vehicle delivery) keeps customers not only informed but also excited about their purchases.
• Elevated customer engagement: Transparent updates reduce inbound inquiries, allowing dealership teams to focus on value-added sales and planning processes and reduce missed sales opportunities.
• Brand trust and loyalty: Demonstrating openness, especially when delays occur, strengthens customer trust and enhances brand reputation.

The critical role of accurate ETA models

While order status visibility is critical, customers also want to know when to expect their new vehicle. Accurate ETAs enable customers to plan and purchase with confidence and empower dealerships to optimize inventory and resource allocation. Figure 3 displays the benchmarks used in North American deliveries, based on Deloitte analysis.

Figure 3[ES1] . North American deliveries, imported and domestic

Source: Deloitte analysis

Challenges of traditional ETA models

• Static service level agreement (SLA)-based ETAs that do not incorporate actual performance often resulting in inaccurate delivery estimates
• Inability to account for real-time supply chain disruptions

• Siloed and operational process inefficiencies drive instability resulting in poor predictions

Enhancing predictions with machine learning

To address these challenges, leading automotive organizations are turning to advanced analytics and machine learning (ML) to transform ETA predictions from a reactive process to a proactive, data-driven capability established by a foundation of data products from the organization’s supply chain ecosystem.

How machine learning enhances ETA accuracy

• Real-time data integration: ML models aggregate data from production lines, suppliers, logistics providers, and external factors such as weather or geopolitical events.
• Pattern recognition: By analyzing historical and real-time data, ML identifies trends and potential bottlenecks, enabling more precise predictions.
• Continuous improvement: ML algorithms learn and adapt over time, refining ETA accuracy as new data becomes available.
• Personalized insights: ML enables tailored ETAs for individual vehicle orders providing crafted updates moving beyond generic timelines.

Sample ML features for ETA optimization

• Production throughput and cycle times

• Defect classifications

• Transportation network performance

• Historical disruption patterns (e.g., holidays, strikes, weather events)

• Real-time logistics and tracking data

Delivering value across the ecosystem

The integration of production status visibility and reliable ETA models delivers benefits for both customers and automotive OEMs:

• Enhanced customer satisfaction: Transparent, accurate communication reduces anxiety and builds brand advocacy.

• Reduced operational costs: Lean data-driven operations streamline downstream processes and inventory management.

• Value chain optimization: ETA adherence and ETA buffers are key metrics to identifying and addressing bottlenecks across the automotive value chain.

How can we help?

At Deloitte, we help automotive clients harness the power of digital technologies and advanced analytics to reimagine the end-to-end customer journey. By embedding transparency and predictive intelligence into production and delivery processes, organizations can improve customer satisfaction and operational excellence. Predictive models are foundational to the future of the automotive order-to-delivery experience. Leveraging data products and machine learning across a complex automotive supply chain empowers OEMs to enhance value chain efficiency and position themselves for sustained success in a rapidly evolving industry.

Endnotes

¹ Bobby Stephens et al., 2024 Consumer Loyalty Survey, Deloitte, October 1, 2024.