According to our 2024 Industrial Machinery Service Benchmark Report, many companies with dealer-based service models lack comprehensive data visibility. This challenge is not unique to any single sector—but it is particularly pronounced in industries that rely on warranty data to drive business decisions.
Warranty data can be deceiving, mainly because it offers only a partial glimpse of service history. However, companies can better understand their service operations by implementing personalized field service AI tools and strategies like Shift Left.
This broader approach enables smoother experiences for employees and customers alike:
Faster resolution times: AI tools can quickly analyze vast amounts of data, predicting potential issues before they become significant problems. This proactive approach allows for faster resolutions, significantly reducing customers’ downtime. Personalized AI tools help tailor communication and service options to individual customer preferences and history, leading to a more personalized service experience and happier customers.
Increased First Time Fix Rates: AI tools can provide real-time guidance to technicians, offering troubleshooting steps and maintenance history at their fingertips. This boosts their confidence and efficiency, leading to higher job satisfaction. With AI-driven insights, technicians are better equipped with the correct information and tools to resolve issues on the first visit, enhancing customer satisfaction and reducing return visits.
Efficient resource allocation: AI can help optimize scheduling and resource allocation by accurately predicting service needs, ensuring that the right technician with the right skills is sent to the right job.
Enhanced training opportunities: AI-driven analytics can identify common knowledge gaps across service teams, allowing companies to tailor training programs more effectively, thereby continuously improving service quality.
The Illusion of Warranty Data
Historically, Original Equipment Manufacturers (OEMs) have focused intensively on manufacturing operations, often at the expense of service management.
This traditional view sees the product as the core revenue generator, with service relegated to a necessary but secondary role. This perception is deeply ingrained in the operational strategies of many OEMs, where service departments are considered cost centers rather than potential profit centers.
The long-term impact of this approach is significant, affecting the OEM through:
Missed revenue opportunities: By not prioritizing service, OEMs miss out on significant revenue streams from after-sales services such as maintenance, repairs, upgrades, and parts sales. These services often have higher profit margins than the initial sales and can contribute substantially to the bottom line over the product’s lifecycle.
Reduced Customer Lifetime Value: When service is not a priority, the customer relationship ends at the point of sale. This short-sighted view ignores the potential for ongoing revenue through repeat sales, service contracts, and customer referrals, which are critical for increasing each customer’s lifetime value.
Higher long-term costs: Without a focus on maintaining and improving product performance through service, more significant failures and customer issues are likely to occur, which are costlier to resolve than regular maintenance would have been.
Employee morale and retention: When service departments are considered less important, it can affect employee morale and retention. Service professionals may feel undervalued and less motivated, leading to higher turnover rates and additional costs for hiring and training new employees.
Data Deficiency and its Implications
A manufacturing-centric approach typically leads to significant gaps in collecting and analyzing service-related data, resulting in several missed opportunities. For instance, customer details like contact information, purchase history, and preferences often remain scattered or incomplete, hampering personalized service and limiting opportunities for upselling or cross-selling. Moreover, detailed knowledge about assets such as machinery or equipment—including operational contexts, environments, and usage patterns—is crucial for predictive maintenance.
However, poorly maintained records mean OEMs will miss preemptive service interventions, which could otherwise prevent costly downtimes. Furthermore, incomplete data on equipment age or inconsistent maintenance logs can lead to inadequate service schedules, potentially reducing equipment lifespan and increasing customers’’ total cost of ownership.
Similarly, relying on warranty data as the primary source of service information further exacerbates these issues. Warranty data typically provides only a narrow glimpse into a product’s lifecycle and is often partial and inaccurate. It primarily reflects a subset of potential defects or issues covered under warranty terms and is typically skewed towards early-life failures, neglecting problems outside warranty conditions.
Since dealers are incentivized to report only warranty-covered issues, significant aspects of product performance and failures remain unreported, resulting in a fragmented and misleading data landscape. This reliance on insufficient data affects operational efficiency and limits businesses’ ability to provide comprehensive and effective service solutions.
Did you know?
First Time Fix Rate (FTFR) is a vital KPI that suffers from these systemic data shortcomings. Ideally, FTFR should reflect the effectiveness of initial service interventions, but it is often based on incomplete data.
Dealer reporting biases: Dealers, operating under the constraints of warranty terms, predominantly submit claims they expect to be approved. This selective reporting distorts FTFR calculations, painting an overly optimistic picture of service success.
Uncompensated service attempts: Many OEMs do not compensate dealers for unsuccessful first repair attempts. This policy discourages dealers from reporting these attempts, further skewing the data and undermining efforts to measure and improve service efficiency.
Bridging the Data Gap
The path to overcoming service challenges in dealer-based models lies in dismantling the old paradigm that views service as an afterthought. By embracing a data-centric approach and AI-driven intelligent service tools, companies can unlock the full potential of their service operations, turning every customer interaction into an opportunity for growth and customer loyalty enhancement.
Service leaders need to get strategic about closing the data gap between dealer submissions and OEM records. Improving data collection practices, incorporating comprehensive service data analytics, and redefining the role of service within the OEM business model are crucial steps in addressing these long-standing issues. By recognizing the full scope of service operations and its potential as a profit center, OEMs can unlock new levels to:
Training and upskilling: Ensuring that all dealer technicians possess up-to-date training and technical expertise is essential, especially with the continual introduction of new products and technologies. Without comprehensive data, it’s difficult to identify gaps in knowledge and areas needing improvement, leaving some technicians underprepared and impacting service quality.
Expanding capacity and growing install bases: For OEMs managing extensive machinery portfolios, tracking and servicing these assets efficiently is crucial. The complexity increases for dealers servicing multiple brands. Often, dealers find themselves entangled in warranty claim resolutions, while the real opportunity for revenue lies in proactive service events. A robust data-driven approach would allow OEMs and dealers to streamline operations and focus on more lucrative, high-revenue tasks. Moreover, this shift can significantly impact the entire service chain, enhancing resolution times and improving the customer experience.
Enhancing customer relationships: Consistency in service quality across all dealers is vital for maintaining a solid brand reputation. Inconsistent service standards can harm a brand, as customers experiencing poor service at one dealer might attribute this to the entire company, potentially resulting in lost loyalty and negative word-of-mouth. Complete and accurate data can help OEMs enforce high service standards uniformly, ensuring customer satisfaction remains high across all touchpoints.
Service is personal—and your AI should be, too.
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