Navigating the retail renaissance: India embraces e-commerce advertising strategies

By Vinay A V,

Advertisement-led monetisation would be the most critical profitability lever for e-commerce platforms in India in 2024. With the digital ad spend market in India projected to reach $10 billion this year, all digital platforms are striving to gain a larger share of this flourishing and profitable ad market.

E-commerce platforms have a unique advantage in this growing market, with access to user preferences and transaction data while shopping on the platform. Additionally, they can provide transparent performance tracking of ad spends, giving them a competitive edge as brands increasingly prioritise measurable and targeted performance marketing.

On-site ads are particularly effective for achieving profitability for most e-commerce platforms. India’s retail media market now generates over $1.5 billion of ad revenue annually – a revenue stream characterised by high margins and low cost of operations. E-commerce giants like Amazon and Flipkart have experienced a significant approximately 40% increase in cumulative ad revenue, reaching Rs 8,705 crore in the fiscal year through March 2023 with ambitious growth targets for the future. Meesho’s strategic focus on ad monetization emerged as a pivotal decision, contributing to the platform’s profitability, even amidst its zero-commission model.

Major players in the food delivery and quick commerce sector, such as Zomato, Swiggy, and BigBasket, are also focusing more on expanding ad monetisation – a strategic move to increase profitability.

Even non-commerce platforms, such as Disney+ Hotstar, are shifting from a subscription-oriented model to one centred around ads, recognising the substantial revenue potential. JioCinema took a bold decision to show IPL without needing a subscription, monetising by ads instead. Netflix and Amazon Prime Video are also actively seeking to expand their market share through similar strategic manoeuvres. In this competitive market environment, e-commerce platforms of all sizes and stages of maturity have ventured into the ads business, drawing inspiration from the success of market leaders and a greater emphasis on profitability by investors.

The rise of personalised ads and machine learning

Historically, e-commerce platforms optimised ads by targeting a wide audience and implementing hard-coded rules. The lack of personalisation made it difficult to strike a balance between maximising ad revenue, fostering platform growth, and ensuring an engaging user experience. The lack of relevance in ads for users resulted in a three-sided problem: advertisers experienced lower return on investment (ROI), platforms fell short of their full potential revenue, and irrelevant ads bombarded users.

To address this issue, personalised advertising driven by machine learning (ML) algorithms is becoming increasingly crucial and is being adopted by many leading e-commerce companies globally. By leveraging first-party data and sophisticated ML algorithms, advertisers can tailor ads with unprecedented accuracy, leading to higher engagement and conversion rates.

ML-optimized smart bidding dynamically fine-tunes bids to optimise for every click based on multiple factors such as search terms, user-specific navigation, past purchase behaviour, time of the day, and location, to name a few. For instance, the bid for a women’s running shoe may differ if the user’s search query was just “shoes” rather than “women’s running shoe”. Dynamic bid adjustments ensure that the advertising strategy is fine-tuned to each unique scenario. From a ranking perspective, real-time optimisation further enhances ad performance, enabling platforms to adapt swiftly to changing contexts. For example, the ranking of a “top” can significantly differ based on the skirt that was purchased by a user before browsing for the tops. Incorporating such nuances is critical to providing the most relevant ads for the user.

As the industry evolves, the future of e-commerce advertising in India lies in hyper-personalization, where advanced AI techniques will play a pivotal role in delivering tailored ad experiences that resonate with individual preferences and aspirations.

Leveraging 1P data to enhance offsite retail media buying

Offsite retail media buying occurs when e-commerce sellers choose to advertise beyond the e-commerce platform, yet channel their spending through the platform due to its access to rich first-party data and reliable tracking mechanisms. Brands traditionally allocate substantial digital marketing budgets across various non-commerce platforms as well, but they often face challenges such as opaque tracking, unclear attribution, minimal data utilisation, and suboptimal performance. E-commerce platforms hold a unique position to manage digital marketing expenditures of transacting brands, extending beyond their platforms, with access to a wealth of resources, including first-party data and audience insights

By providing robust analytics and tracking capabilities, e-commerce platforms empower brands to monitor the real-time performance of offsite marketing campaigns. This data-driven approach enables brands to refine and optimise their campaigns for superior outcomes using the platform’s first-party data in a secure and compliant way. Brands typically allocate marketing budgets into brand and trade marketing categories, with on-site ads primarily targeting the trade marketing budget. However, offsite advertising also allows e-commerce platforms to capture a portion of the brand marketing budget. Moreover, facilitating offsite media buys not only helps manage brands’ digital marketing expenditures but also drives order growth for the platform, owing to the user clicks directed to the platform itself.

E-commerce advertisers shift gear: demanding guaranteed results and output-based ad models

Ads were traditionally sold using input metrics like impressions and clicks, even within e-commerce platforms, despite the availability of output metrics, such as sales and orders. However, advertisers have become increasingly dissatisfied with input-based pricing models and demand-output-based models, where they only pay for tangible sales outcomes. The desire for greater accountability and transparency in advertising investments drives this shift. Advertisers recognise that output-based advertising can be more cost-effective, and are willing to allocate disproportionately high budgets, particularly when they are directly tied to incremental orders. This allows advertisers to optimise budgets across products by accurately bidding based on the margin profile of each product, maximising the impact of their advertising spend.