Data is the new oil. It is helping companies reshape their entire existence and adapt to the changing consumer demands more quickly. The aftermarket is no different. With data analytics, OEMs can unlock better value for their customers, streamline operations, and boost their profits. A real-life case study of this is recorded by McKinsey.

A leading player wanted to identify and capitalize on growth opportunities with the help of data, but was restricted due to limited data shared by distributors. In the end, the player worked with its sales team to build analytics and collect data. With this approach, not only was the player able to collect sufficient data for business growth, but also put it to action and increase revenue by 15-25%.

While this was a single instance of an OEM benefiting from data analytics, actual benefactors are many. The next can be you as well if you implement a data strategy in time.

Why Data is the New Engine Driving Growth in the Aftermarket

Data is highly relevant in 2026 and the years to come. Based on market dynamics, OEMs may need to update and refine their strategies. After conducting thorough research, they may find more use cases specific to their organization where data can help.

1. Next-Level Automation

We cannot emphasize automation enough. While digital transformation simply enables automation, data takes automation to the next level. You can collect data and create AI models for every task and workflow. For example, part identification and ordering can be simplified with AI capabilities. By analyzing the parts ordering history data, AI can also predict the spare parts demand with accuracy.

Similarly, some other areas that can be automated are:

• Parts lifecycle tracking
• Spare part diagnosis
• Barcoded warehousing & logistics
• Workshop planning
• Voice search

The best part about data-based automation is that the more data you collect, the better your model gets. With richer and complex data, AI learns to fix underfitting and overfitting, ensuring higher accuracy in every search you perform. 

2. Predictive Maintenance

With the help of Internet-of-Things (IoT) enabled devices, OEMs can collect wear and tear data to predict and even prevent breakdowns. AI can conduct various checks on the equipment, including usage levels, fuel levels, vibration patterns, and engine temperature. It can also check maintenance history to identify parts that will require replacement faster. Based on this data, dealers can call consumers to provide personalized offers and services. 

Along with predictive maintenance, IoT data can help OEMs in demand forecasting. OEMs can figure out which spare parts and consumables will need to be replenished faster in their dealer network and prevent stockouts. With this approach, they will also get ample time for manufacturing, distribution, and other logistics.

Lastly, predictive maintenance means OEMs can predict which challenges their technicians are going to face in servicing and repair. They can timely update their technical documentation to help technicians in servicing and reduce equipment downtimes.

3. Fraud Detection

Fraudulent claims are quite common in the aftermarket, especially in the warranty management process. While all OEMs try to adhere to a strict process to prevent such fraud, due to some loopholes or manual errors, it becomes difficult to completely avoid them. On the downside, such claims cause OEMs heavy losses every year. As per statistics, OEMs in the automotive and industrial equipment industry have to spend 3-15% of their warranty budgets on fraudulent claims. 

By analyzing previous data, OEMs can identify common patterns and trends in claims to distinguish genuine claims from fraudulent ones. With AI-powered warranty fraud detection, they can make their fraud prediction even more accurate and fail-proof the entire system. Previously, claims were handled manually, which used to have up to 40% inaccuracies. Along with fraud detection, data-backed claims processing can help reduce this number as well. 

4. Personalization

In 2026, customers are aware that brands are collecting their data and, in return, expect a more personalized experience. This stands true in the aftermarket as well. Customers want their aftermarket experience to be streamlined to a point where they should already be aware of what customers are after. According to research, companies that offer personalized experiences are also able to drive 40% higher revenue than average.

While personalization is important, we understand that it can be difficult to identify the scope of personalization in the aftermarket. Here are a few areas that you can consider.

• Hyperlocal Search Experience: Help customers find and purchase parts from their local dealers. You can have an option on your website that works as a ‘nearby dealer finder.’ To further improve the experience, let dealers upload their sales inventory on the website. After this, customers can search as well as order spare parts directly from their nearby dealers from the comfort of their locations.‍
• Personalized Ads: Social media ads work on keywords. When customers accept cookies on their browsers, the cookies start tracking their search behavior, including their keywords and search terms. OEMs can launch their own ad campaigns to sell accessories and services. For example, they can promote protective coatings, off-roading customizations, etc. After targeting, customers won’t have to opt for third-party dealers.
• ‍Product Improvement: Based on consumer data, such as trending features, purchasing behavior, and buyer preferences, OEMs can revamp their products. An automotive OEM can introduce new features like a heads-up display on the dashboard, add a 360-degree parking camera, add ADAS, and even launch limited edition vehicles with limited color options.

5. Create Better Pricing Strategies

The OEM industry follows cut-throat pricing. Due to fierce competition, OEMs have to keep their margins low. While previously, they used to follow cost-plus-margin pricing, in today’s competition, it is not effective anymore. Thus, modern OEMs need data-driven pricing to help them balance out sales and profit margins. Instead of doing manual market research or buying research data, they can deploy AI to figure out a pricing that works.

By analyzing historical pricing records, OEM contracts, and profit margins, manufacturers can come up with a pricing strategy that works over a wide geographical area. This strategy will account for variable factors, such as cost differences, market demand, seasonal fluctuations, competitor pricing, and scarcity of parts.

With the help of smart spare part management software, you can easily update product pricing across the entire catalog and display the same to dealers.

How to Implement Data Strategy in Your Aftermarket Operations?

OEMs can decide whether they want to implement the data strategy themselves or hire a consultant. In case they want to hire a consultant, we have mentioned the benefits of doing so below. However, in both situations, OEMs will need to invest in AI-enabled aftermarket tools.

1. Consult Aftermarket Industry Experts

To implement any strategy, OEMs first need to collect and analyze their business metrics. They need to identify the key areas in their business where digital transformation is possible. An aftermarket business consultant is already aware of all such areas and can help the OEM collect relevant business metrics faster.

A consultant can also give direction to their strategy by identifying even more areas for optimization while keeping the budget in check. The only shortcoming of this approach is that the OEM may need to share its data with the consultant firm. A strict NDA clause would be required to protect OEM data. 

2. Invest in AI-Enabled Solutions

Today, data is synonymous with AI. With these two clubbed together, the analysis is faster, relevant, and cost-effective. However, instead of investing in small AI tools for every purpose, OEMs can take a holistic approach and invest in a complete AI-powered ecosystem developed for aftermarket operations.

The Intellinet System perfectly fulfills this criterion. Intellinet Systems is a leading aftermarket solutions provider and has a broad range of products to cater to the various requirements of OEMs. With AI-powered aftermarket software solutions, Intellinet Systems can help OEMs adopt a digital and effective data strategy fairly quickly.

Hire In-House Data Scientists

Lastly, hiring an in-house team of data science experts is also a solution. However, this is the most costly and time-consuming approach. The only benefit of this approach is that OEMs get full control over the data and can customize their AI models as much as they want. They can also deploy the same team for competitor search and coming up with relevant pricing models.

Conclusion

Data-driven strategy is going to become the backbone of all OEMs in the future. From product innovation, demand forecasting, and fraud prevention to marketing, pricing strategy, and market penetration, everything is going to be based on AI and backed with data. Even thought leaders like McKinsey, Deloitte, and BCG cannot stress enough the importance of data.

If your organization is planning to switch to a data-driven strategy, experts recommend using reliable AI systems that can accurately collect and analyze data across your entire dealership network. With such a solution, OEMs only need to get in touch with a single vendor, who can deploy the solution for global operations. Moreover, certain solutions, like Intellient Systems' aftermarket software solutions, can also be directly integrated with your ERPs, DMS, and other business tools, making it easier to switch to a more advanced and powerful solution.

Book a free demo to experience how data transforms aftermarket operations.

FAQs

1. Why is data collection important for OEMs?

Data collection is important for OEMs as it helps them take measured approaches to streamline operations across the value chain. From demand forecasting, predictive maintenance, and improving customer satisfaction rates to managing dealers, reducing equipment downtimes, and coming up with pricing strategies, data collection can help in numerous aspects.

In brief, for making data-driven business, marketing, and management decisions, data management is important for all OEMs.

2. How to improve aftermarket services using data?

With data, OEMs can improve aftermarket services in several ways:

1. Automate key operations such as parts lifecycle tracking, diagnostics, logistics, and part search.
2. Use fraud detection to prevent fraudulent claims.
3. Personalize customer experience at different stages and levels
4. Create a better pricing strategy that is more relevant for market dynamics.
5. Predict sales demand and do timely restocking to prevent shortages.

3. Why is predictive maintenance important in the aftermarket?

Predictive maintenance uses real-time data and data monitoring to predict breakdowns and schedule maintenance. It can be used for both OEM machinery and customer vehicles. OEMs can track when the customer last replaced parts and when they will require another replacement. Likewise, they can use AI or IoT sensors to predict breakdowns and call customers for timely repairs.

4. What is the best way to collect and manage data in the aftermarket?

For optimum data management, get dedicated AI-powered aftermarket solutions. These solutions are purposely built considering various aftermarket use cases in mind and collect real-time data to help you streamline logistics, repairs, invoicing, and other operations. For AI-based solutions, look at Intellinet software solutions that have AI-integrated part identification, demand forecasting, knowledge base search, voice search, and several other AI features.