Service sales campaigns: How the installed base becomes a sales pipeline

Service sales: from reactive to structured

Many companies in the DACH region take a reactive approach to service sales: maintenance contracts are only extended shortly before they expire and spare parts are only supplied on request. This approach harbors risks: Competitors gain customers, while internal data remains scattered and unused.

Solution: Structured service sales campaigns

With a central database and clearly defined processes, companies can make targeted use of their installed base. The 4-step model—“Digitize → Connect → Decide → Automate”—describes how scattered data can be transformed into a scalable sales pipeline.

• Digitize: Record all machine data centrally (e.g. year of manufacture, contract status, telemetry data).

• Networking: Merging data to segment target groups.

• Decide: Identify triggers such as expiring contracts or wear patterns.

• Automate: Trigger campaign sequences, field service tasks and portal actions without manual maintenance.

Example: A maintenance contract expires in 90 days. An opportunity is automatically created, sales representatives receive relevant data and the customer is contacted in good time. Such processes increase the contract renewal rate and tap into untapped sales potential.

Conclusion: A structured approach to service sales protects existing customer relationships and strengthens competitiveness in the long term.

The four-stage model for service sales campaigns

Structured, scalable service distribution requires clear steps. The four-stage model provides a systematic approach to move from a reactive approach to a data-driven pipeline. Each stage builds on the previous one, and skipping leads to gaps that can jeopardize the entire process.

Stage 1: Digitize – Create a digital inventory

The first step is to collect all relevant equipment data: serial numbers, years of manufacture, locations, contract status, and lifecycle information. This data forms the basis for the digital equipment file. It marks a departure from the “sell and forget” approach and ensures a centralized database. Without this overview, the sales department remains trapped in an inefficient, reactive way of working, as fragmented data obscures opportunities and risks.

Stage 2: Networking – combining data and making it usable

In the second step, the collected data is linked together: lifecycle information, service histories and telemetry data are integrated across systems and made available in CRM. This enables targeted segmentation. For example, systems of a certain type, older than eight years and without an active maintenance contract, can be identified as a potential target segment. This turns the data collected in the first stage into a valuable context for sales.

“Service doesn’t actively sell; it identifies opportunities. With the right tools, these opportunities can be turned into automated campaigns.” – logicline

Stage 3: Decide – set triggers with Service Decision Intelligence

Data alone is not enough to launch campaigns effectively. This is where Service Decision Intelligence (SDI) comes into play. This technology recognizes patterns in telemetry and service data, such as increasing error rates or expiring contracts, and uses them to generate precise triggers for sales. Most importantly, the data remains on the customer’s infrastructure and complies with all EU regulations. Each recommendation is provided with a source reference so that the sales department can understand the background to each action.

Stage 4: Automate – implement campaigns efficiently

In the final step, the defined triggers are converted into automated campaigns. With Salesforce Workflows, mailing sequences, field service tasks or portal notifications can be started automatically when patterns such as wear and tear are detected. This automated approach relieves the sales department of the recurring assignment of triggers, assets and opportunities and ensures consistent processes. At the same time, it creates the basis for measurable successes that can be taken into account in future optimizations.

The following overview shows how these service strategies in mechanical engineering contribute to improving the pipeline:

Step 1: Digitize – Build a structured installed base

Consolidate data from scattered sources

The first step – digitization – involves capturing the installed base in a complete and structured manner. Machine and plant data is often scattered across different systems: ERP systems, PDF reports, Excel tables – often without clear allocation to specific plants. The aim is to bring this information together in a central database. This is the only way to decide which customers can be addressed with which offers. This central database is the basis for the digital machine file described below.

Contents of a digital machine file

A digital machine file combines master data with life cycle and service information to provide a comprehensive picture of each machine:

Lifecycle and contract data are particularly important to enable targeted campaigns. Without the exact expiration date of a maintenance contract, for example, no renewal campaign can be launched in good time – around 90 days in advance. The installation date is also crucial in order to segment older systems specifically for modernization offers.

Closing data gaps with an installed base assessment

Manufacturers often overestimate the quality of their existing data. A structured installed base assessment reveals where data is missing: Which machines do not have an active maintenance contract? Which machines are missing their current location? Which devices have been running for over ten years without a documented service history?

“The individual market potential must be assessed in order to determine the service’s actual performance capability. This is the foundation for a successful service strategy.” – Dr. Martin Habert, service expert, bachert&partner

The assessment is not a one-off cleansing process. It provides a prioritized overview of data gaps and shows which segments are already campaign-ready and which data first needs to be supplemented. This could mean for a manufacturer with 8,000 installed systems: Part of the base can be used directly for contract renewal campaigns, while master data must first be completed for older systems without a telemetry connection. This prioritization forms the foundation for the next steps in the campaign process.

Step 2: Networking – from data to segmented campaigns

Linking lifecycle and telemetry data

In the second phase, networking, static lifecycle data is combined with dynamic telemetry data. Only by linking information such as year of manufacture, contract status and service history with telemetry data (e.g. error codes, operating hours or vibration values) are usable segments created for targeted campaigns.

An example from special machine construction illustrates this: A machine built in 2013 is initially just an isolated data point. However, if you add an increasing error rate from the telemetry and take into account that the maintenance contract expires in 85 days, it becomes a clearly identifiable candidate for offers to extend or modernize the contract.

Define segments for campaigns

Precise segmentation is crucial in order to target only relevant machines. Typical segments for a manufacturer with around 8,000 installed machines could be as follows:

• Contract extension: Machines with maintenance contracts that expire in less than 90 days.

• Modernization: Machines older than 8-12 years, combined with an above-average error rate.

• Wear parts: Machines whose operating hours have reached or exceeded a specified maintenance interval.

• End-of-life: Machines in the final phase of their life cycle without an active service contract.

According to the VDMA, many companies in the mechanical engineering sector currently provide active service support for only 10% to 25% of their installed base (VDMA, “Mechanical Engineering Service Study,” 2022). This shows that a large portion of the installed base can be specifically targeted through structured data networking and segmentation.

Once the segments have been defined, the next step is to assign specific data fields as triggers.

Map data fields to campaign triggers

The following table shows how specific triggers for campaigns can be derived from the digital machine file:

Without this mapping logic, the existing data remains unused. However, a clear assignment of triggers lays the foundation for targeted, trigger-based sales activities. These form the basis for optimization through Service Decision Intelligence (SDI) in the next phase.

Four campaign types along the lifecycle

Before SDI controls the triggers in practice, it is worth getting an overview of the most important repeatable campaign types. They result directly from the segments and triggers from step 2 and form the operational basis on which the subsequent steps 3 (Decide) and 4 (Automate) are based.

Maintenance contract extension

The 90-day trigger provides a clear and effective starting point for structured service sales campaigns. As soon as a maintenance contract expires in less than 90 days, there is sufficient time for budget approvals and factual negotiations.

Prioritization is based on criteria such as contract value, system age and service history. High-value systems with a frequent fault rate and high contract value are preferably addressed personally by the field service. Smaller systems with a stable operating history, on the other hand, can be extended efficiently via automated e-mail sequences.

This campaign facilitates the entry into a structured pipeline and creates the basis for further lifecycle offers.

Modernization campaigns

Modernization campaigns are launched when equipment is between 8 and 12 years old and is experiencing an increased failure rate. During this phase, maintenance costs typically rise, spare parts become harder to find, and the machine’s performance declines—the so-called “performance gap.” Only when both criteria are met is the machine included in the modernization segment, in order to avoid wasted effort and to target sales resources effectively. The in-depth article on this topic explains how to systematically structure such retrofit campaigns at the end of the product lifecycle.

This campaign expands the structured pipeline and paves the way for further automation steps.

Spare parts packages

Telemetry data provides valuable insights into wear patterns even before a breakdown occurs. If vibration values or operating hours exceed defined thresholds, a suitable spare parts offer is automatically created that is precisely tailored to the machine’s usage profile. With this proactive approach, the manufacturer reacts before customers have to take action themselves. This reduces unplanned downtime and ensures a stable turnover in the spare parts business.

This campaign also complements the structured pipeline and leads to further automation.

End-of-life programs

Machines that are in the final phase of their life cycle require their own campaign strategy. Typical signs are spare parts that are no longer produced, available successor models or an internally set end-of-life status. Without actively addressing this segment, there is a risk that customers will opt for replacement solutions from other suppliers.

A successful end-of-life campaign combines a final spare parts package with attractive conditions in the short term and offers for successor systems or upgrade programs in the medium term. The key lies in early and transparent communication so that the customer does not realize that certain parts are no longer available.

In the next step, SDI uses these triggers to implement the campaigns in an automated and scalable way.

Step 3: Decide – Service Decision Intelligence as a trigger engine

Following the preliminary work in step 2 and the overview of the four campaign types, we now move on to the practical use of Service Decision Intelligence (SDI). SDI serves as a link between the raw data of the installed base and the operational processes in sales. The aim is to generate precise and market-oriented triggers from the existing data that provide targeted support for sales.

How SDI analyzes data patterns and converts them into actions

SDI uses telemetry data and service histories to derive specific recommendations for action. An example: A machine shows unusually high vibration values, the associated maintenance contract expires in 90 days and a suitable modernization package is available. SDI automatically recognizes this pattern and creates a prioritized trigger – without the need for manual adjustment.

However, the prerequisite for this is a structured database that links real-time telemetry with historical service data. SDI can only generate reliable and relevant triggers once this basis – corresponding to the second stage of the 4-stage model – has been created. Without this basis, the intelligence layer remains ineffective.

Transparency and control: data sovereignty is key

For the recommendations from SDI to be accepted by customers and sales representatives, it is crucial that they are transparent. SDI therefore provides clear source documentation for each trigger. For example: Instead of a general recommendation such as “Modernization recommended,” SDI shows in detail which factors led to this conclusion—such as an increased error rate since the last maintenance, exceeding the operating hours, or the unavailability of a replacement part.

Another advantage: SDI runs directly on the customer’s infrastructure and meets EU data protection requirements. This means that sensitive machine data remains under the company’s control and does not end up in external clouds without being monitored. This combination of data sovereignty and transparent recommendation strengthens trust in the solution – both among end customers and in sales.

Automated campaign control with SDI

In day-to-day operations, SDI seamlessly integrates the generated triggers into Salesforce workflows that control the corresponding campaigns. A maintenance contract trigger is displayed directly as a prioritized opportunity in the CRM of the responsible sales representative. A modernization trigger automatically initiates a campaign sequence with a suitable offer. A spare parts trigger initiates a series of emails in good time before the customer notices a breakdown.

In this way, SDI enables a structured and automated way of working in sales. Employees can focus on prioritized campaigns, while SDI transforms the entire process into a repeatable and efficient sales motion.

Step 4: Automate – scale campaigns

A trigger initiated by SDI starts a predefined sequence of actions in Salesforce. Each campaign follows a clear sequence: trigger, segmentation, action and follow-up.

A typical example from special machine construction with 8,000 installed systems: if a maintenance contract expires in 90 days, Salesforce automatically creates a prioritized opportunity and assigns it to the responsible field service employee. If telemetry data simultaneously indicates an increasing error rate, the opportunity is directly linked to a modernization offer. The sales employee not only receives the task, but also all relevant data – from operating hours and service history to current telemetry values.

Scaling begins with a minimum viable product (MVP): a campaign, a machine type, a defined trigger. Only when this process is stable and achieves measurable results is the model extended to other segments. Standardized campaign templates ensure that new campaigns can be created efficiently. The focus here is on repeatability and scalability.

Reactive vs. automated: A direct comparison

The difference between a reactive and an automated approach becomes clear in the following comparison:

According to the Bain study “Winning in Industrial Aftermarkets” (2021), manufacturers that systematically monitor their installed base and prioritize service sales can increase their aftermarket revenue potential by 30 to 60 percent within three to five years. According to industry experts, German machinery manufacturers are currently tapping into only a small portion of their installed base with service offerings. A structured and automated approach offers the opportunity to leverage this potential much more effectively.

The next step with automated campaigns is the continuous monitoring and optimization of sales performance.

Measure and continuously improve campaign success

KPIs for service sales campaigns

Automated campaigns only bring long-term results if it is clearly defined how success is measured. Key performance indicators include the contract renewal rate, the campaign conversion rate and the penetration of the installed base. These values not only show how effective the campaigns introduced in the previous phase are, but also serve as a basis for the targeted further development of SDI triggers.

Clear target values should be defined for each campaign – be it a contract extension, a modernization or the sale of spare parts packages – in order to check the effectiveness of the triggers. Dr. Martin Habert, service expert at bachert&partner, emphasizes this:

“What matters is not the profit margin, but the comparison between planned and actual results. Rigorous tracking and transparency of financial figures, combined with a clear strategy and defined measures, are the appropriate tools for evaluating and managing the service business.”

Refine SDI triggers based on campaign results

The evaluation of campaign results plays a key role in the further development of SDI triggers. This data provides valuable insights for continuous improvement. An example: If a modernization trigger achieves a high conversion rate for installations aged 8 to 12 years, but is hardly successful for installations over 12 years old, this shows that age alone is not sufficient as a criterion. Other parameters such as the cumulative error rate from the telemetry or the number of open service cases should be integrated into the trigger logic.

SDI can recognize such patterns when campaign data is systematically fed back. Every opportunity and every feedback contributes to optimizing the trigger logic. This iterative process turns every campaign into a learning unit. In this way, the third step of the model, decision-making, is translated into a repeatable and data-based sales strategy.

TRUMPF provides a practical example; at the PARTS SUMMIT 2025, the company presented its “Data-Driven After-Sales” strategy. This strategy integrates machine data directly into the sales logic to precisely determine which systems should be targeted with which offer and at what time. This approach creates a continuous optimization cycle that further increases the efficiency and impact of automated campaigns.

Conclusion: From reactive to structured – the path to the service sales pipeline

The most important findings

The transition from a reactive to a structured service sales approach begins with a thorough analysis: Where is the data on the installed base located, and which events in the life cycle of the systems have not yet been utilized?

The 4-stage model provides clear orientation. Reliable segments can only be created once lifecycle, service and telemetry data are linked to the sales processes. Service Decision Intelligence translates these data patterns into concrete recommendations for action, such as triggers for contract extensions, modernization campaigns, spare parts offers or end-of-life programs. The result: a pipeline that improves through continuous feedback – each campaign becomes more precise than the last.

These findings can be used to prepare the start of a structured service sales process in a targeted manner.

Where to start

The first step, therefore, is not a sales tool, but an honest assessment: Which machine segments are currently generating which service revenue, and where are the biggest untapped opportunities—contract renewals, modernization, or spare parts? The Installed Base Assessment delivers exactly this data set and a concrete leverage report within 4–6 weeks. On this basis, service sales campaigns can be planned rather than relying on gut instinct. Those who already have a structured foundation and want to specifically assess where the first trigger takes effect within their own organization can schedule an initial consultation right away.