Aftersales is now a growth engine for OEMs, not just a support channel
Top-performing manufacturers are monetizing service with predictive models and remote-first delivery
Subscription-based maintenance, digital catalogs, and workflow automation drive recurring revenue
Smart platforms lower cost-to-serve while improving customer stickiness
Treating service as a product opens new margin streams beyond equipment sales
Aftersales & Service are undergoing a strategic rebranding inside OEM boardrooms. Once viewed as a reactive, cost-intensive necessity, the function is now a prime candidate for revenue innovation and profitability. In fact, many industrial manufacturers report that service accounts for over 30% of total gross margin, with significantly higher stickiness than new equipment sales.
This shift is fueled by tightening capital expenditure budgets among customers. Buyers aren’t replacing assets as frequently; instead, they’re investing in performance, longevity, and operational efficiency — all of which fall under the service umbrella.
Service departments are now expected to deliver top-line growth while optimizing bottom-line results. That means reducing the cost-to-serve through digitization and automation, while creating monetizable touchpoints via tiered offerings, digital features, and outcome-based contracts.
OEMs that adapt fastest are already seeing an increase in attach rates, upsells, and multi-year service contracts. Those who still treat service as reactive are missing out on a critical revenue stream — and risking long-term relevance.
The key to this transformation is treating service with the same discipline and intentionality as hardware. That means defining offers, pricing models, success metrics, and go-to-market strategies just as you would for a new piece of equipment. The future belongs to OEMs who productize value, not just ship machines.
Recurring revenue is no longer just for software companies. Industrial OEMs are increasingly turning to subscription-based service models to create predictable, scalable income streams — and customers are responding.
With equipment lifecycles often spanning decades, service subscriptions allow OEMs to stay engaged throughout the asset's life, delivering proactive value while capturing revenue beyond the initial sale. These offerings can include scheduled maintenance, remote diagnostics, spare part bundles, system updates, and guaranteed uptime SLAs — all bundled into tiered packages.
Rather than a one-time service call or parts order, customers are paying for outcomes: maximum uptime, minimal disruption, and performance optimization. This shift in pricing logic allows OEMs to align their success with customer success — a far more compelling long-term relationship than transactional support.
Smart OEMs are building service tiers that map directly to customer needs and budget levels. For example:
Basic Tier: Access to online support and documentation
Standard Tier: Includes scheduled checkups, remote diagnostics, and replacement parts
Premium Tier: Predictive maintenance, performance monitoring, guaranteed uptime SLAs
This not only increases customer lifetime value but also simplifies internal planning and staffing. Service teams can forecast workloads based on subscription tier and plan technician dispatches, inventory allocation, and support coverage accordingly.
For many OEMs, recurring service contracts now make up a higher margin portion of their revenue mix than equipment sales — and provide a cushion against economic volatility.
As industrial customers shift their focus from ownership to operational availability, OEMs are capitalizing on the trend with predictive service contracts. These agreements go beyond scheduled maintenance to offer data-driven uptime guarantees, often tied to specific financial outcomes.
The value proposition is compelling: rather than reacting to failures or over-maintaining assets “just in case,” predictive models leverage IoT sensors, machine logs, and AI to pinpoint exactly when intervention is needed — and what kind. This minimizes unplanned downtime while optimizing resource use.
In practical terms, OEMs embed performance benchmarks into contracts, offering services like:
Failure prediction alerts
Automated part replacement scheduling
Condition-based maintenance visits
SLA-backed system monitoring
By aligning service scope with asset performance, OEMs shift from input-based pricing (hours worked or visits made) to value-based pricing — what the customer actually cares about: system availability.
Predictive contracts aren't just better for customers — they’re better for the bottom line. With fewer emergency dispatches and more accurate inventory planning, service teams can reduce waste, labor spikes, and last-minute logistics costs.
OEMs also gain higher contract renewal rates and stronger differentiation from local or third-party service providers who can’t offer the same predictive capabilities. The data gathered through these services becomes an asset in itself — powering future R&D, product improvements, and cross-sell opportunities.
In this model, uptime is no longer a nice-to-have — it’s a monetizable service, tightly integrated into the business model and delivered at scale through intelligent systems.
One of the fastest-growing — and most financially impactful — trends in Aftersales is the move to remote-first service delivery. What started as a response to pandemic-era travel restrictions has evolved into a core component of scalable, cost-efficient service models in 2025.
The math is clear: every avoided on-site visit saves OEMs thousands in technician travel, vehicle use, and lost time. At the same time, customers benefit from faster response times and reduced downtime. This win-win has made remote diagnostics and support a strategic default, not just a backup.
From smartphone-based visual support to augmented reality overlays and remote sensor monitoring, remote-first models drastically reduce the need for physical dispatch while preserving — and often improving — service quality.
The financial upside of remote-first service isn’t just in cost savings — it’s in gross margin expansion at scale. Here’s how:
Technician bandwidth increases, allowing fewer specialists to support more assets
First-time resolution rates rise as experts guide on-site staff or customers through fixes
Repeat visits drop, reducing part shipping and labor inefficiencies
Data capture improves, enriching asset intelligence for future product/service design
In many cases, even a simple video call can avoid days of costly downtime for the customer and eliminate unnecessary dispatch for the OEM. Advanced platforms are now embedding remote triage as the first step in every support ticket — using AI to route, pre-qualify, and even auto-resolve common service cases.
When combined with digital parts identification tools and mobile field enablement, remote-first support becomes not just a cost reducer — but a revenue enabler, increasing capacity and contract value without expanding headcount.
Aftersales has entered a new era — one where value creation happens long after the equipment is delivered. OEMs that treat service as a monetizable, productized engine are outperforming those stuck in reactive, transactional models.
By embracing subscription tiers, predictive contracts, and remote-first delivery, manufacturers aren’t just improving support — they’re expanding margins, increasing customer retention, and building smarter, more scalable service operations. The companies winning in 2025 aren’t just servicing assets — they’re selling uptime, outcomes, and intelligence.
From Partium:
Stop Overlooking Your Aftersales Business—Start Growing It
A deeper look at how top OEMs are embedding visual support, real-time triage, and remote diagnostics to reduce cost-to-serve while increasing speed-to-resolution.
From Industry Experts:
McKinsey: Why aftermarket and service are vital to OEMs—and how to excel
McKinsey breaks down why aftermarket and service now account for a major share of OEM profit, and outlines the strategic capabilities needed to outperform — from digitized service models to data-driven customer engagement.
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