If digital services remain free, staffing costs will rise—but revenue will not grow in line with the installed base. The conclusion is obvious: track digital services per machine as part of the contract, KPIs, and pricing logic, rather than hiding them within an hourly rate.
In short, there are three main points:
- From service calls to contract logic: Services per asset instead of individual hours
- From Data Access to Service Product: First Visibility, Then Analysis, Then Accountability for Results
- From scattered billing data to a clean database, clear KPIs, and verifiable documentation
An overview of the three monetization stages:
- Level 1: Asset Data Access as a Recurring Service
- Step 2: Monitoring, analysis, and expert review for a recurring fee
- Level 3: Managed Service with Availability or Usage Logic
- Requirement: a clear baseline, data source, and calculation rule for each asset
- Starting point: First organize the installed base, then connect it, and finally define the pricing logic
The key is manageability, not so much the technology: What exactly do you provide? What is the customer paying for? And how do you demonstrate performance per machine? That’s exactly where service effort turns into a service product.
Where Profit Margin Is Lost: Free Additional Services and Hourly Billing
Digital services are often implemented quickly. Yet they still do not appear as a separate line item on the invoice. If, in the end, only hourly and daily rates are billed, the digital component remains economically invisible. That is precisely where profit margins are lost, and that is precisely where the transition from service effort to service product often fails.
Why the Hourly Rate Is Slowing Down Service Growth
Under the hourly rate model, revenue depends on the number of available technician hours and individual service calls. This works as long as service is primarily organized on a reactive basis. However, as the number of machines grows, this model reaches clear limits: More equipment in the field leads to more work, but not automatically to more revenue.
For heads of service and executives, the point is simple: The model scales with the number of employees rather than with the installed base. Those who provide additional digital services—such as remote support, condition data analysis, or digital documentation—often end up giving more without being able to charge a separate fee for them.
Once machines are connected, the economic focus shifts. Then, the contract per asset becomes the key factor, rather than the individual hour. In practice, it becomes clear that service can only be decoupled from staffing levels once performance is linked to the number of connected machines.
Why the installed base is the real lever
For mass-production manufacturers with a large installed base, the greatest leverage lies in the service business—in the structured management of the installed base, not in individual service calls. Those who accurately track every asset—type, year of manufacture, operating hours, condition, and contract status—lay the foundation for a service portfolio with a clear pricing structure.
This is exactly where the Digital Machine File (IOTAM) comes in: It transforms distributed plant data into a reliable view of the installed base. This makes it clear which machines are eligible for standard contracts, condition-based services, modernization, or remote support. In Salesforce, this enables a comprehensive view of the customer, the machine, and the service contract.
Strategically, a simple maturity model can help: Digitize → Connect → Decide → Automate. Only once the installed base has been accurately digitized and connected can manufacturers systematically price services instead of quietly hiding them within hourly rates. The three monetization stages build on this foundation.
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From a Connected Asset to a Marketable Service Product
When machines in the field provide data, that doesn’t automatically translate into a service business. The first step is a practical one: the installed base must be transformed into a contractual entity that is understandable to the customer and can be clearly billed by the manufacturer. Monetization therefore begins with transparency, not with AI. This is followed by analytical services; accountability for results comes only at the end.
This aligns with the maturity model : Digitize → Connect → Decide → Automate. For heads of service, this is more than just a model: it shows when specific services can be reliably priced. Anyone who promises availability too early—without having a firm grasp of the data, responsibilities, and escalation procedures—is selling a risk rather than a service product.
Level 1: Asset data access as a paid entry-level option
In the first phase, data from the installed base is consolidated into clear performance metrics. The customer receives structured asset data for each machine, as well as access to a status overview in the portal. What’s being sold here is visibility, not yet a diagnosis: Which machine is in use where, what is its condition, and what basic data is available?
For many manufacturers, this is a sensible starting point because the service can be clearly defined. In Salesforce, this approach can be managed as a recurring contract line item—for example, per machine, location, or model series. The Digital Machine File (IOTAM) provides the data foundation for this, ensuring that information on inventory, history, and status isn’t scattered across individual systems or email inboxes.
The benefits for the customer are clear: less time spent searching, a unified view of assets, and a solid foundation for service discussions. For the manufacturer, this represents the first step toward recurring revenue.
Phase 2: Monitoring, Analysis, and Expert Review as a Contractual Service
Starting with Level 2, mere access to data is no longer sufficient. Now the customer pays for analysis and interpretation. Anomaly detection, condition monitoring reports, and an expert review transform raw data into a contractual service that helps in day-to-day operations: Deviations become apparent sooner, maintenance windows can be planned more effectively, and decisions are based on reliable data. While Level 1 sells access to asset data, Level 2 sells interpretation and confidence in decision-making.
This is precisely where knowledge becomes the bottleneck. When information from sensor data, service notes, and error histories all comes together, the technician or service coordinator must quickly arrive at a reliable assessment. Wherever knowledge management is part of the process, Empolis is a natural fit: logicline helped develop the Salesforce integration for Empolis Service Express. This makes it possible to leverage diagnostic knowledge, known error patterns, and service logic right where decisions are made.
When AI-powered diagnoses or claim assessment are also involved, a robust intelligence layer is essential. Service Decision Intelligence (SDI) provides the ideal framework for this, as it allows recommendations to be issued with a source citation while ensuring that data sovereignty remains within the customer’s infrastructure. For many industrial companies, this is precisely the key consideration when they need to meet EU compliance and traceability requirements.
Level 3: Managed Service and Outcome-Based Pricing as a Premium Model
In the third stage, the focus shifts. Now the manufacturer assumes part of the responsibility for results—beyond just data and analysis. Typical examples include availability commitments, extended maintenance intervals, or a pricing model based on measurable results rather than on effort alone.
This is economically attractive, but it can only be managed effectively if the rules are clear: Which metric applies, which data source is authoritative, which factors are within the customer’s control, and when does an escalation take effect? Without this clarity, outcome-based pricing quickly becomes a source of dispute.
This sequence is not just a theory. The turbomachinery manufacturer Everllence (formerly MAN Energy Solutions, Augsburg) provides a publicly documented example: Its tiered Digital Service Agreements range from connectivity and data access (PrimeServ Connect) through monitoring, analytics, and dedicated service engineers (PrimeServ Assist) to a managed premium contract with a customized maintenance strategy. In the white paper “Autonomous Operation” (2022, updated 2025), Everllence quantifies how, for turbomachinery, maintenance intervals can be extended from the usual three to five years to up to twelve years through 24/7 monitoring and AI —an industry example from the field of turbomachinery, not a blanket guideline.
Here’s how the typical scenario in mass production begins: A manufacturer starts by providing portal access for each asset, then supplements monitoring reports with expert reviews, and only later offers contracts with availability guarantees. It is precisely this sequence that reduces risk for both parties. The customer first purchases transparency, then context, and only after that a shared responsibility for results.
By this stage at the latest, service is no longer treated merely as a cost center. It operates according to a profit-center model, with recurring revenue, clearly defined service boundaries, and measurable service offerings. A special case of this logic is usage-based billing for entire machines, as described by pay-per-use and equipment-as-a-service models.
What outcome-based pricing requires: measurable metrics for each asset and a transparent data model
As soon as service is compensated based on results rather than effort, the focus of responsibility shifts. The question then becomes whether the agreed-upon performance for each asset has been verifiably achieved—beyond merely demonstrating that work was performed. Price, liability, and ultimately the trust between the manufacturer and the customer all hinge on this. Anyone who sells performance-based accountability therefore needs reliable metrics for each asset and verifiable proof of performance.
Which Key Metrics, Pricing Models, and Guarantees Are Included
Which KPIs are appropriate depends on the machine, the application, and the contract. Often, the focus is on availability or maintenance intervals. In other cases, throughput, energy consumption, scrap rate, or response time take center stage. What matters most is clarity for each asset, rather than the length of the KPI list.
Every metric needs three things:
- a clear baseline
- a defined data source
- a fixed calculation rule
Only then does a measured value become a reliable basis for pricing or warranties. For example, if availability is compensated, the contract must clearly define what constitutes a failure, which downtimes are excluded, and which sensor data or service events are relevant for this purpose. Equally important is the question of who is authorized to access raw data and which data will serve as a reference in the event of a dispute.
Why Transparent Data Is Crucial for Billing, Forecasting, and Trust
With performance-based compensation, a final value without a source is not sufficient. When billing is done on a per-asset basis, every figure must be traceable. Otherwise, an invoice can quickly turn into a discussion about data sources, measurement points, or calculations. This is exactly where Service Decision Intelligence (SDI) comes in. Every billing basis and every forecast requires a source trail: Which source provided which value at what time, and how was the metric derived from it? This ensures that it remains clear whether a value was measured directly or derived from multiple data points.
For decision-makers, this is more than just a technical issue. Verifiable evidence speeds up internal approvals, supports forecasts, and makes warranty commitments more reliable. When recommendations or forecasts are used in SDI, the source citation is retained. This is particularly important for claims, diagnostic cases, or bonus-malus models, because decisions must not end up in a “black box.”
Added to this is data sovereignty. The data remains within the customer’s environment, which supports EU-compliant operating models. For many industrial companies, this is not a minor consideration but a prerequisite—especially when machine data, usage profiles, and contract logic are integrated. In practice, this very point often decides whether an outcome-based model is approved.
For this to work, one thing is essential above all else: a well-structured installed base. This is the foundation for the 4-step logic: Digitize → Connect → Decide → Automate.
From a Structured Installed Base to a Monetizable Service Portfolio
The 4-step model: Digitize, Connect, Decide, Automate
To ensure that KPIs, guarantees, and billing are reliable, you first need a solid data foundation. This is precisely what the 4-step model— Digitize → Connect → Decide → Automate —is based on.
Phase 1 – Digitize: A structured asset data set is available for each machine. The serial number, configuration, and maintenance history are all stored in one place. The Digital Machine File (IOTAM) serves as the data foundation for this.
Stage 2 – Connect: Telemetry data is collected for each asset. Status values, operating hours, and usage data are recorded in a structured manner. This makes it possible to compare even mixed fleets on the same basis.
Stage 3 – Decide: Service Decision Intelligence (SDI) serves as the analytics layer on top of the data. Here, consumption and status data for each asset form the basis for billing and forecasting. For decision-makers, the most important factor is that recommendations remain traceable because SDI provides source citations and can be operated within the customer’s infrastructure in compliance with EU regulations. This is particularly important when service commitments, claims, or usage-based models must be clearly documented in contracts.
Stage 4 – Automate: It is only at this point that outcome-based contracts, usage-based billing, and availability guarantees come into play. For this to happen, Stages 1 through 3 must function reliably in day-to-day operations. Those who start too early here are basing their pricing logic and contractual commitments on incomplete data.
Entry into the Market for Medium-Sized Companies in the DACH Region
For many medium-sized manufacturers in the DACH region, a streamlined approach based on the installed base is a better starting point than a major overhaul. In practice, the same pattern often emerges: The service model seems complex because data is scattered across different systems, locations, and years of manufacture. The first step, then, is not to draw up a new vision on paper, but to create a manufacturer-independent data layer covering the existing fleet.
This allows for the standardization of asset, condition, and usage data without having to completely overhaul the entire service organization. In Salesforce in particular, this is of interest to the head of service and executive management because a clean installed base ensures that future processes in sales, service, and billing are all built on the same data foundation.
A sensible approach usually involves three steps:
- First, organize the installed base
- then link the prioritized asset groups
- Then define contract-ready KPIs and pricing logic
An Installed Base Assessment shows the current state of data, which assets are already suitable for billable services, and where gaps need to be closed. In this way, a structured installed base is gradually transformed into a service portfolio that is also financially viable.
Two practical ways to get started:
- Installed Base Assessment – if you want to determine which assets are already suitable for billable services and where data gaps exist.
- introductory call – if you’d like to discuss the specifics of your tiered service model and the appropriate pricing structure for your fleet.
FAQs
How do I get started with monetizing digital services?
Start with a robust data foundation for each asset. Only when consumption, condition, and usage data for each machine are available in a measurable form can you clearly define digital services, evaluate them economically, and price them transparently. Tiered service offerings are built on this foundation. In practice, the spectrum ranges from connectivity and data provision to monitoring and analytics, all the way to managed services or outcome-based contracts. Without this data foundation, billing is often open to criticism, and the benefits for the customer are difficult to demonstrate. The Digital Machine File (IOTAM) lays the foundation by consolidating information for each asset and making it usable in day-to-day operations. In Salesforce, this data can then be linked to service processes, contracts, and installation data. The model “Digitize → Connect → Decide → Automate” serves as a guideline. For diagnosis, forecasting, or claims, Service Decision Intelligence (SDI) serves as a decision-making layer where recommendations must be traceable—with source citation, EU-compliant data storage on customer infrastructure, and an LLM-agnostic approach via MCP/BYOM.
Which KPIs are suitable for outcome-based service?
KPIs that make performance per asset measurable are suitable. They form the basis for billing and forecasting. Typical metrics include availability, energy efficiency, maintenance intervals, and consumption and condition data. What matters most is the reliability per asset, rather than the volume of data. If measured values are missing, timestamps don’t match, or sources aren’t clearly assigned, every billing statement becomes vulnerable and every forecast unreliable. In practice, you need a traceable database for each asset that supports both technical and business needs. This is exactly where the Digital Machine File (IOTAM) comes in: It consolidates the relevant information for each machine, thereby laying the foundation for the accurate use of KPIs in Salesforce for service, billing, and planning. If usage- or performance-based models are added later, you must be able to demonstrate what value was generated, when, and on which asset.
When will my installed base be ready for a contract?
Your installed base is ready for a contract when reliable consumption and condition data are available for each asset. Only on this basis can services, results, and billing be clearly defined in a contract. The prerequisite for this is a robust data foundation. In practice, this follows a clear maturity model: Digitize → Connect → Decide → Automate. Every form of monetization builds on this. An Installed Base Assessment shows how far along your fleet is on this path today and which assets are already suitable for billable services.
