• SAP industry experience

    • Why can’t you see when your customer is leaving?

    The Australian context: dealers are already under pressure.

    Australian automotive dealers are entering one of the most compressed periods in recent memory. The New Vehicle Efficiency Standard came into force on 1 January 2025, with the first reporting period beginning 1 July 2025. According to the AADA, the financial consequences for Australia's 3,200 franchised dealers could reach between $1.1 billion and $2.1 billion between 2025 and 2029, depending on how OEMs choose to respond. Some OEMs are already responding in ways that hurt dealers: limiting volumes and pressuring retailers to push stock at unsustainable margins.
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    At the same time, a survey of nearly 4,400 Australians visiting Gumtree, CarsGuide, and Autotrader in 2025, shows that car buyers are increasingly abandoning brand loyalty driven in part by NVES-triggered portfolio shifts toward hybrid-only or fully electric lineups. The result: dealers are being squeezed from above by regulation, from below by margin pressure and from the side by eroding loyalty. In that environment, aftersales isn’t just a support function, it’s the last reliable profit lever. Australia’s automotive aftermarket is estimated at USD 13.98 billion in 2025, growing to USD 18.27 billion by 2032, largely because Australians are keeping vehicles longer, increasing the lifetime service value per vehicle. But longer vehicle ownership only converts to revenue if you keep the customer engaged across the service lifecycle.

    That’s exactly where most automotive distributors and dealer networks are blind.

    The data exists. The visibility doesn’t.

    Most organisations in automotive aftersales aren’t lacking data. They’re lacking the ability to connect that data to extract meaningful insights. That gap between data that exists and data that works is exactly where customer churn is buried.

    Churn is rarely a clean event. There’s no single “customer leaving” trigger in any system. Instead, it shows up as a pattern of small shifts:

    • a missed service cycle
    • a drop in parts purchased
    • an unresolved issue that went quiet
    • a connected service that stopped being used.

    Individually, none of these looks alarming. Together, they form an early warning signal, if you can connect them across systems and read them in context. Most organisations can’t. Not because the technology doesn’t exist, because the operations aren’t structured to support it.

    The illusion of visibility

    Here’s the uncomfortable truth: having data is not the same as having visibility. A large automotive group might capture vehicle service history in one system, warranty data in another, parts transactions in a third, and customer communication logs somewhere else entirely. Each system is reporting. None of them are talking to each other.

    The numbers tell that story clearly. According to a 2025 study of 600 US dealerships, 81% lose customer conversations or leads because their CRM, chat and inventory systems fail to communicate. 65% report that customer follow-ups are delayed specifically because of disconnected systems. A striking 92% of automotive sales go untraceable in CRM systems altogether.

    EY puts it plainly in their 2025 after sales analysis: the automotive aftersales ecosystem is still fragmented, underinvested in and operationally inefficient. Disconnected service portals and back-end systems erode trust and drive customers toward third-party providers often quietly, long before anyone notices.

    The result is a picture that looks complete, until a customer walks away and you realise the signals were there all along. This is the core aftersales churn problem: it isn’t about data. It’s about connectivity. No number of dashboards, reports, or analytics layers will fix it if the underlying operations remain fragmented.

    Why aftersales is the real battleground

    Churn doesn’t start in CRM, it starts in operations. The numbers here are stark. Industry-average brand retention sits at just 43.9% (Reynolds and Reynolds, 2024), meaning more than half of customers will purchase their next vehicle elsewhere. Cox Automotive calculates that a 1% improvement in retention would generate USD 700 million in additional annual revenue across the industry.

    The loyalty picture is deteriorating too. Data from VehicleLyfe shows that dealer customer retention dropped 12% in 2024, falling to its lowest level since tracking began in 2019. Only around 20% of dealership sales now come from repeat customers, well below the one-third considered healthy for long-term sustainability.

    Yet the financial stakes of getting this right are enormous. Service and parts contribute 49% of dealership gross profit while representing just 12% of total revenue (NADA, 2024). Research from Urban Science found that customers who service their vehicle at the same dealership are twice as likely to buy their next vehicle there too.

    The earliest signals of customer disengagement almost always originate in the aftersales lifecycle:

    • How is the asset being serviced — and how regularly?
    • Are parts being sourced through the dealer network or elsewhere?
    • When an issue was raised, how was it resolved — and how fast?
    • Is connected service data showing changes in usage patterns?

    These are operational signals and they exist somewhere in most organisation’s today. The question isn’t whether the data is there. The question is whether anyone can see it in time to act.

    What changes when data is connected

    In a traditional model, the flow looks like this:

    systems report → people analyse → decisions lag → actions are late.

    By the time someone identifies a pattern, the customer has already mentally checked out. The intervention, if it comes at all, arrives too late to be anything other than reactive.

    In a connected model, the flow is different:

    signals are detected continuously → AI interprets them in context → actions follow before the customer disengages.

    This is the shift that organisations such as SAP have been positioning toward with their Autonomous Enterprise vision. Not AI as a layer on top of operations, but AI embedded within them. When data flows freely across the business, the organisation can start to respond as one, without waiting for human coordination at every step.

    The principle matters less as a product announcement and more as a design philosophy: operations, data and decisions need to be connected before AI can add meaningful value. AI doesn’t fix fragmentation. It amplifies it.

    From customer-centric to vehicle/asset-centric

    There’s a subtler shift required here, one that changes how you frame the problem entirely.

    Most CRM thinking starts with the customer: Is this customer at risk? Is their satisfaction score declining? Have they opened our last email?

    But in aftersales, the more useful unit of analysis is the asset: the vehicle, the machine, the equipment.

    When you shift from asking “Is this customer leaving?” to asking “What is changing in how their asset is being used, serviced, or maintained?”, the signals come earlier, more specific, and more actionable.

    A customer who stops scheduling maintenance, reduces parts purchases or disengages from connected services is telling you something, not through a survey, but through operational behaviour. Recognising those patterns early and responding with targeted outreach or support before they disengage formally is what effective churn management looks like in this sector. It’s not about predicting sentiment, it’s about reading asset behaviour.

    The missing link: orchestration

    Even when organisations invest in analytics, CRM platforms or AI tools, the results are often disappointing. The reason is almost always the same: the systems feeding those tools remain disconnected.

    Without orchestration across operations:

    • signals get lost between systems
    • context is incomplete by the time it reaches anyone who can act
    • responses are manual, slow and often misdirected

    With orchestration, the dynamic changes:

    • data flows across the business in real time
    • signals become visible as patterns, not isolated events
    • responses can be coordinated and, in some cases, automated

    This isn’t a technology question at its core. It’s an operational design question. The technology exists. The harder work is mapping how your operations produce data, where that data breaks or disappears and what would need to change for it to flow end-to-end.

    Where to start

    Every transformation starts with clarity. Not a program, not a platform, not a new dashboard.

    Start here:

    Pick one asset, one vehicle, one machine and map its full aftersales lifecycle.

    Ask these question’s specifically:

    • Where is its service history stored?
    • Where are parts purchases tracked?
    • Where are warranty or issue signals captured?
    • Where does this data break or disconnect?

    That exercise will reveal where visibility is lost, where churn signals already exist and where your operating model is fragmented. It usually takes less than one day and the findings are almost always surprising. This is the foundation of the Operational Clarity Assessment: a structured way to identify what to fix first, before committing to any transformation.

    The point

    NVES didn’t create a data problem for Australian dealers. It exposed the lack of operational control that was already there. AI won’t fix that on its own.

    But when operations, data and decisions are connected, when the business can read asset behaviour in real time and respond before a customer formally disengages, you move from managing churn reactively to preventing it.

    That’s not a technology ambition, it’s an operational one.  For Australian dealers navigating NVES, margin compression, and rising Chinese competition, it’s becoming a commercial necessity.

    Ready to find out where your aftersales visibility breaks down?

    The Operational Clarity Assessment is a structured diagnostic designed for automotive organisations who want to understand their data gaps before investing in transformation.

     

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