The CFO asks: How many XK-7 machines are currently running at customer sites? Silence. The service manager checks his Excel sheet from 2019. The sales director looks in the CRM. The numbers differ by 30 percent. Nobody knows which figure is correct. This scene is everyday reality in German mechanical engineering companies. 67 percent of OEMs report that their installed base data is distributed across three to four different systems, and 90 percent find it extremely time-consuming to even obtain reliable figures. The digital machine record isn’t IT gimmickry. It’s the key to the most profitable division in mechanical engineering: the aftermarket.
What an Installed Base Really Is
The installed base is the totality of all machines installed at customer sites, including serial number, configuration, location, service history, and contract status. In B2B mechanical engineering, it’s a manufacturer’s most valuable asset. While new business generates one-time revenue, a well-documented installed base opens up recurring revenue streams over the entire machine lifecycle.
Specifically, this means: A machine manufacturer who knows their installed base doesn’t just know how many systems are in the field, but also which components are wear-prone, when maintenance cycles are due, and which customers offer cross-sell potential. This information is the foundation for everything that works in the aftermarket.
Reality looks different: Delivery data is in the ERP, configuration information in PLM, service histories in field service management, and current master data status in asset management platforms like Siemens Insights Hub or comparable systems. Each system maintains its own version of truth. When the CFO asks how many machines of a specific type are in the field, a manual reconciliation begins that takes days and still offers no guarantee of accuracy.
Aftermarket as a Margin Lever
BCG quantifies EBIT margins in the aftermarket at around 20 percent for top performers, while new business is significantly lower at 10 to 15 percent. McKinsey’s analysis across 30 industries concludes that aftermarket services achieve an average EBIT margin of 25 percent, compared to 10 percent in new business.
These figures are no coincidence. Aftermarket business is less cyclical, more predictable, and associated with higher customer retention effects than volatile new business. Those who know their installed base can forecast spare parts demand, sell service contracts strategically, and implement predictive maintenance models. Those who don’t lose this lever.
A concrete example: BCG shows in engagements with machine manufacturers that aftermarket penetration can be increased by 20 percentage points through targeted installed base intelligence. This means: A manufacturer with 100 million euros in new business could generate an additional 20 million euros in aftermarket revenue solely through better use of their existing data, at significantly higher margins than new business.
Three Source Systems, Four Data Points, Zero Overview
The technical cause of the problem is clear: Installed base data originates at different points in a machine’s lifecycle, and no single system has a complete overview.
The ERP provides delivery data: Serial number, customer, order date. But it doesn’t know which components were actually installed if the machine was customer-configured. PLM provides the technical configuration: Bills of materials, CAD drawings, variants. But it has no live access to the machine’s current state at the customer site. Field service management provides service history: Maintenance interventions, replaced parts, fault reports. But it doesn’t know whether the machine is still in operation or has long been decommissioned. And asset management platforms like Siemens Insights Hub provide the current master data status: Location, operating hours, condition. But they lack a complete history back to delivery.
Additionally: In many mechanical engineering companies, there is no clear owner for installed base data. Sales, service, marketing, and finance each maintain their own lists without ever consolidating them. The consequence: 90 percent of OEMs report that it’s extremely time-consuming to even compile relevant installed base data for sales decisions.
Initial Load from Excel Lists and Legacy Systems
The typical mechanical engineering reality is even more complex. Asset data resides in old CRM databases, Excel lists per service location, and custom developments from the 90s. Anyone wanting to build a digital machine record must consolidate, cleanse, and transfer these heterogeneous sources into a unified data model.
The manual effort is enormous: Identifying duplicates, completing missing serial numbers, correcting customer assignments. A mid-sized machine manufacturer with 10,000 installed machines can expect several months for this initial load if conducting the work internally without support.
AI-assisted data cleansing significantly accelerates this process. Pattern recognition algorithms can automatically normalize serial number formats, identify duplicates through fuzzy matching, and extract missing data from service reports. What takes months manually can be reduced to a few weeks with professional support.
Crucially: The initial load isn’t a one-time project, but the starting point for continuous data management. Once the digital machine record is established, manufacturers must ensure new machines are automatically recorded, service histories updated, and master data maintained. Only then does a reliable foundation for all aftermarket activities emerge.
Aftermarket Levers on the Installed Base
Those who know their installed base can monetize it. The concrete levers are diverse and operate at all levels of aftermarket business.
Increase service contract penetration: OEMs with complete installed base transparency systematically identify machines without active service contracts and target them specifically. BCG shows in mechanical engineering engagements that aftermarket penetration can be increased by 20 percentage points when sales and marketing work with clean installed base data.
Manage end-of-life migrations: Machines with 15 years of operation are candidates for upgrades or new investments. Those who know which machines reach this age can engage customers in timely dialogue and initiate modernization projects.
Systematize cross-sell: A customer operating Machine A is a prospect for accessories, expansion modules, or complementary machines. These relationships can only be leveraged when the installed base is completely captured. More on this in our article on cross-sell strategies based on installed base.
Implement predictive maintenance: IoT data from field machines is only usable when linked to the installed base. Those who don’t know which machine has which serial number cannot train predictive maintenance models. More on this in our article about predictive maintenance and master data.
Where MARINI Comes In for Mechanical Engineering
MARINI, the platform for Customer Intelligence with Data Integration, Data Cloud, and Agentic, solves the fragmented installed base problem through a central DataEngine Data Object: Asset. This object consolidates all relevant machine information, regardless of source system.
Specifically: HubEngine Plans to ERP, field service management, CPQ, PLM, and asset management platforms like Siemens Insights Hub synchronize data bidirectionally and in real-time. Delivery data from SAP S/4HANA, service histories from Salesforce Field Service, configuration data from PLM systems, and current master data from Insights Hub flow into a unified Asset object in the MARINI Data Cloud.
The Asset object isn’t isolated, but linked to Account, Contact, service history, service contracts, and renewal pipeline. A sales representative sees not only that the customer operates three XK-7 machines, but also when the last service occurred, which components were replaced, and when current service contracts expire. This connection creates the foundation for Customer Health Scores in B2B mechanical engineering and enables proactive engagement instead of reactive ticketing.
The third phase, Agentic, extends this data foundation with AI-powered workflows: Automated data cleansing for duplicates, classification of service histories by error patterns, and recommendations for cross-sell approaches based on installed base. More on this approach in our article about AI Agents in CRM sales.
MARINI Professional Services supports machine manufacturers during initial load: AI-assisted data cleansing, initial classification in migration projects, and development of customer-specific workflows for recurring data processes. The result: A digital machine record that doesn’t just exist, but is actually used, because it’s integrated into existing systems and continuously updated.
The Installed Base Isn’t a Data Project, It’s a Margin Lever
Machine manufacturers who don’t know their installed base leave the most profitable part of their business in the fog. While new business stagnates at 10 percent EBIT margin, aftermarket services achieve 25 percent and more. But these margins are only achievable when the manufacturer knows which machines are deployed where, when maintenance is due, and which customers offer cross-sell potential.
The digital machine record is the starting point. It consolidates data from ERP, PLM, field service management, and asset management platforms into a central object linked to Account, Contact, and service history. Those who take this step create the foundation for systematic aftermarket business, from service contract penetration through end-of-life migrations to predictive maintenance.
The question isn’t whether a machine manufacturer should digitize their installed base. The question is when they start-and how much aftermarket potential they leave untapped until then.
