AI: helping OEMs design discounts, hit long-term goals

This article was originally published in ET Auto and has been reproduced here with permission.

Linking new purchases to religious festivals is an integral part of Indian cultural ethos. Vehicle manufacturers, like all others, come out with various discounts and incentives to buyers to cash in on this sentiment.

These discounts and schemes are an expense item that has not yet been completely optimised. For the automotive industry that is driven primarily by value and is highly focused on cost, the amount, estimated to be around INR 50,000 to INR1 lakh per car, cumulatively runs into hundreds of crores.

Designing a discounting plan is like walking on thin ice. It is usually designed to boost sales volume. But, if not done rightly, it can badly impact profitability. If there ever was a time for vehicle manufacturers to move away from traditional marketing practices, such as offering large flat customer discounts, then this is it.

AI-based systems can help OEMs design their discounting strategy that balances price aggressiveness with margin preservation and achieves the twin objectives of target sales volumes and customer value retention.

With the aid of AI-based systems, discounting decisions can be made more scientifically and can help vehicle manufacturers increase sales within the same budget or optimise their discount budgets. Such systems enable decision-making consistent based on underlying data and improve the ability to respond to executive questions with agility and data-driven substantiation.

OEMs in key global markets have started building such AI-based systems to optimise their incentive budgets. These systems are delivering hard benefits, up to 5% in incremental sales over the plan. It can also help save 5%-7% of the discount budgets.

AI tackles the complexity in discounting

AI-based decision tools use several different types and large amounts of sales and economic data. Historical sales data for different models and their variants, along with that of competitors, is the critical input. This data, along with economic data such as interest rates, employment levels, oil prices, industrial activity, agriculture outputs and others, comprises the input data. Multi-stage analytical models powered by advanced algorithms then work on the input data to recommend the right discounts for different vehicle models and variants across geographies and time periods.

Brand managers can run scenario analysis and see the expected impact of any decision where they need to deviate from the AI recommendations. To increase effectiveness, customised dashboards can be built to visualise the impact. This enables sales and marketing executives to stay agile and make decisions based on changing market conditions.

For vehicle manufacturers lacking in strong data collection and management practices, these AI-based systems become critical as they trigger the process for such data-driven capability development. Delaying such capability development will only increase the gap between such vehicle manufacturers and the best-in-class OEMs that are already on this path.

Discount better… not that you can!

The significance of consumer incentives is in no way expected to decrease in the near future. With product life cycles decreasing, new competitors entering the Indian market, consumers adopting new mobility models, and online channels increasing product and price transparency, the role played by consumer incentives is expected to increase.

AI-based systems, in a nutshell, take the guesswork out of the discounting strategy. By establishing a scientific system to tailor offers, vehicle manufacturers can now prioritise customer conversions without losing sight of long-term business goals!

Ashish Sharma

I advise clients in improving business performance (business models, operations and customer experience) using digital tools and technologies.
With an extensive history of working in the automotive/ digital/ shared mobility/ start-up domains, I am experienced in the areas of business model transformation, operations management, business scaling, business strategy, performance improvement, operating model improvement, supply chain management, cost reduction and technology implementation.