When I first imagined the use of data in Automotive aftermarket, I painted a picture where the vehicle enters the service station, and the camera captures its registration number and then the system pulls out data from thousands of sensors in the vehicle to prepare a job card which tells which part or system of the vehicle needs attention. I am nowhere close to that as of today but it’s realistically possible.
However, today I want to share some use cases where data can be very helpful, and this is based on my own personal experience of data utilization where my team was able to turn around a declining business by accelerating the decision making.
Good Product needs to be complemented by great service even at the fag end of the product age.
Professionals working in the automotive sector can understand how the new vehicle has been glamourized and is the centre of attention for the organization whereas aftermarket, which makes a significant portion of profit almost 50% (depends on maturity of markets & OEM strategy) for an OEM, is often the second claimant to the resources. A good product is only half the journey, to truly be the first choice of the customer the service must be great as well. OEMs do understand this; however, it is only the first few years which are the focus period for OEMs. This is reflected not only in internal KPIs but also external customer satisfaction ratings which take the initial period with more weightage on evaluating the brand’s satisfaction index. However, the fag end of the vehicle age must not be ignored for 2 reasons. Firstly, as the vehicle gets old many parts and systems are completing their scheduled life hence it needs more maintenance and replacement which gives an opportunity for more revenue.
Secondly, since it’s the fag end of the vehicle age and the customer may be in a repeat purchase evaluation cycle and the experience at this stage does play an important role in helping him decide if he is going to be a repeat customer or will switch to another brand.
Different estimates say the cost of retaining an existing customer vs cost of acquisition of a new customer is 1:5-7 depending on OEMs and Markets
Now I want the managers in the Automotive aftermarket domain to ask themselves how many dedicated KPIs they have for customer service for vehicles that are above 5 or 8 years of age.
It’s not just OEMs who are causing this gap but it’s also the customer behaviour that has shaped it. As the vehicle gets old, the customer’s love for his vehicle also tapers off and he is not willing to spend on expensive Genuine spare parts anymore. There are two underlying reasons which aggravate this customer behaviour. As soon as the warranty gets over, the incentive for the customer to stick to authorized service stations and use genuine spare parts vanishes.
Secondly, as mentioned before the rising maintenance costs leads to customer venturing out for alternate cheaper options. This is also reflected in declining Customer loyalty. Depending on the market & OEM it may fall from 90% in the first year to 10-20% in the 5th or 6th years
What can OEMs do to capture this customer segment?
What can OEMs do to make sure that they don’t lose these customers and retain them for a longer period? They need to address both the issues which cause the customer to venture out. Firstly, maintain the incentive for the customer to keep coming back to authorised workshops by offering him commercial downstream products like Extended Warranty. Secondly, the increased cost of maintenance for the customer can be brought down by having a portfolio of the second line of parts.
Extended warranty in essence is an insurance kind of product, which serves the dual purpose of generating a profit by itself and locks in the customer thus ensuring visits to Authorized service station and giving more touchpoints and hence more part & service sales. However, developing Extended warranty as a commercial product needs lot of historical data churning at a very granular level. This is where data analytics can be of great help. Extended warranty can be offered at a full vehicle level or at a system level. However, in all cases, historical data on Warranty Expense and Field failure costs must be analysed to develop a commercially viable product. This is where proper capturing and management of data becomes very important. Also, the data system should be designed in a way to run multiple combinations of Vehicle model, Vehicle age, Vehicle KM, System (Engine, transmission, HVAC, full vehicle, or a mix of multiple systems).
However, as the vehicle gets older the higher costs of repair per Km make this product less attractive & riskier thus OEMs need to look for additional options to retain their customer.
Second line of Parts
This is the time when the second brand line can become an effective tool. However, it’s not an easy task to launch the second line of parts, developing a part under the second brand may take anywhere between 3 months to 1 year or longer depending on how rigorously the brand tests their products. That translates to time and money. Choosing the right product needs an analysis where you need to understand the market opportunity, current penetration of Genuine parts, Sales trend of Genuine parts, Current margins, Price sensitivity, and many other data points need to come together to help you pick the right product for launching the second brand part. Identification of the part is only half the story. Sometimes despite the right pick of parts Sales team often struggle in giving an estimate of how much they will be able to sell, hence a forecasting tool with Machine learning on which the business can rely becomes an absolute must to accelerate the part development process.
Many OEMs have entered the second brand of parts however the way the parts are chosen is still based on experience and lacks statistical support. In my previous experience, we were able to bring down the part identification time from roughly 3-4 months to 4-5 weeks. It’s still an untapped area where proper data-based tools can bring this time down to a week.
The insights-driven automotive aftermarket
I hope the article was helpful for business managers to understand how data is becoming increasingly important. If OEMs can transform data into insights that feeds decision-making and aids in the introduction of relevant products or services, they can achieve great results.
This year, we will see the automotive segment evolving from businesses that utilise insights selectively to becoming insight-driven organisations - the organizations which produce and act on insights across the businesses, throughout the lifecycle of the customers.