Authors: Lakshmi Vaideeswaran
In an age when data dictates decision-making, from cubicles to boardrooms, many auto dealers worldwide continue to draw insights from past experiences. However, the automotive market is ripe with opportunities to leverage data science to improve operational efficiency, workforce productivity, and consequently – customer loyalty.
Data challenges faced by auto dealers
There are many reasons why auto dealers still struggle to collect and use data. The biggest one is the presence of legacy systems that bring entangled processes with disparate data touchpoints. This makes it difficult to consolidate information and extract clean, structured data – especially when there are multiple repositories. More importantly, they are unable to derive and harness actionable insights to improve their decision-making capabilities, instead of merely relying on gut instincts.
In addition, the sudden growth of the BEV/PHEV market has proven to complicate matters – with increasing pressure on regulatory compliance.
But the reality is that future-ready data management is a must-have strategy – not just to thrive but even to survive today’s automotive market. The OEMs are applying market pressure on one side of the spectrum – expecting more cost-effective vehicle pricing models to establish footprints in smaller or hyper-competitive markets. On the other side, modern customers are making it abundantly clear that they will no longer tolerate broken, inefficient, or repetitive experiences. And if you have brands operating in different parts of the world, data management can be a nightmarishly time-consuming and complex journey.
Future-proofing the data management strategy
Now, it’s easier said than done for the automotive players to go all-in on adopting a company-wide data mindset. It is pertinent to create an incremental data-driven approach to digital transformation that looks to modernize in phases. Walking away from legacy systems with entangled databases means that you must be assured of hassle-free deployment and scalability. It can greatly help to prioritize which markets/OEMs/geographies you want to target first, with data science by your side.
Hence, the initial step is to assess the current gaps and challenges to have a clear picture of what needs to be fixed on priority and where to go from thereon. Another key step in the early phase should be to bring in the right skill sets to build a future-proofed infrastructure and start streamlining the overall flow of data.
It is also important to establish a CoE model to globalize data management from day zero. In the process, a scalable data pipeline should be built to consolidate information from all touchpoints across all markets and geographies. This is a practical way to ensure that you have an integrated source of truth that churns out actionable insights based on clean data.
You also need to create a roadmap so that key use cases can be detected with specific markets identified for initial deployment. But first, you must be aware of the measurable benefits that can be unlocked by tapping into the power of data.
– Better lead scoring: Identify the leads most likely to purchase a vehicle and ensure targeted messaging.
– Smarter churn prediction: Identify aftersales customers with high churn propensity and send tactical offers.
– Accurate demand forecasting: Reduce inventory days, avoid out-of-stock items, and minimize promotional costs.
– After-sales engagement: Engage customers even after the initial servicing warranty is over regarding repairs, upgrades, etc. as well an effective parts pricing strategy.
– Sales promo assessment: Analyze historical sales data, seasonality/trends, competitors, etc., to recommend the best-fit promo.
– Personalized customer engagement: Customize interactions with customers based on data-rich actionable intelligence instead of unreliable human instincts.
How we helped Inchcape disrupt the automotive industry
When Tiger Analytics began the journey with Inchcape, a leading global automotive distributor, we knew that it was going to disrupt how the industry tapped into data. Fast-forward to a year later, we were thrilled to recently take home Microsoft’s ‘Partner of the Year 2021’ award in the Data & AI category. What started as a small-scale project grew into one of the largest APAC-based AI and Advanced Analytics projects. And we believe that this project has been a milestone moment for the automotive industry at large. If you’re interested in finding out how our approach raised the bar in a market notorious for low data adoption, please read our full case study.
Tags: AI AI trends automotive industry Data Analytics