‘Data reflex, not just collection’: Marketers and publishers talk effective data usage

New Delhi: In today’s data-driven landscape, brands and publishers are harnessing advanced data analytics to gain deeper insights into consumer behaviour and preferences, allowing for more personalised and effective marketing strategies. 

By combining data from multiple sources—such as website interactions, app usage, CRM systems, and third-party data—companies are not only able to predict consumer trends but also respond in real-time to changes in behaviour. This approach, often referred to as a “data reflex” strategy, is helping brands enhance customer experience and drive loyalty. Publishers, too, are exploring data monetisation by leveraging their audience insights to offer advertisers highly targeted ad placements, turning vast datasets into new revenue streams and strengthening their market positioning.

A panel at the India Affiliate Summit 2024 discussed how organisations can make use of data to attract more revenue and provide better services to their customers.

Kickstarting the session was Deepak Oram, Senior Vice-President at HDFC Bank, who opened the floor by speaking about the modern data stack. Defining a data stack for the audience in the room, Oram said, “The modern data stack combines a brand's data, including website/app behaviour, retail data, usage data, CRM data, predictive information, and deterministic data from third-party sources. This comprehensive approach helps predict and understand customer behaviour both on and off the platform.

Another important aspect is data reflexes, which means using data science effectively to react quickly to customer behaviour. This involves integrating your channels with your data so you can respond rapidly to changes, not just relying on stored company data. It should also include reacting to customer actions on third-party websites. This comprehensive approach, which I call a bottom data stack, includes owned data, purchased data, and both predictive and deterministic data.”

Moving on further in the session, Rajat Mathur, Head of Consumer Marketing & Innovations at Dabur, provided an FMCG perspective on the modern data stack. “In FMCG, we usually interact with channel partners, distributors, and stockists, not directly with end consumers. With e-commerce and modern trade, we have closer consumer engagement but still lack first-party data. We rely on aggregated trends from sources like Nielsen and Kantar. Historically, we only had primary sales data from the company to the distributor, but now we can track secondary sales from the distributor to the store at a pin code level. Tertiary sales data remains a challenge.

Companies, especially D2C brands, are building their first-party databases, and we have started this journey as well. Access to data helps us understand categories and consumers better, leading to more informed business decisions. For example, we can predict which shampoo variant will sell well during monsoons in specific regions or which products will be popular on certain days. This data-driven approach allows us to plan our business more effectively,” said Mathur.

Passing the baton, Mathur handed it over to Sanjay Sidhwani, CEO of Indian Express, who expressed his views on the importance of first-party data for publishers. 

Speaking of how consumption patterns will evolve in the future, Sidhwani said, “Five years ago, intermediaries like Google sold ads on platforms, and publishers provided minimal data beyond reach numbers and third-party inputs. Publishers didn't have their own data. Recently, publishers have realised their vast reach is valuable to advertisers, but for subscriptions, they need a specific offering. Search optimisation attracts a broad audience, but not necessarily subscribers. Third-party tools don't empower publishers to build their businesses.

In the past, print revenue came from subscriptions, with newspapers monopolising household relationships. As media options grew, this control diminished. As subscriptions grow, consumers will likely subscribe to a few trusted news sources, leading to a different marketplace. These subscribers may represent a small percentage of reach but a significant portion of traffic. Owning this affluent audience with deep insights will change the marketing paradigm and improve campaign performance. Going forward, data will enable better campaign delivery, focusing on core audiences.”

Data is present left, right, and centre but as a publisher, how can you use that data to provide more value to the advertisers? Elaborating on this thought, Sidhwani said, “In the media perspective, interaction data is easily available, such as what articles consumers read, how often they visit, and what devices they use. This data can help infer intent: if someone reads auto articles, they might be interested in buying a car. However, not all reading indicates high intent. To filter this, additional data like age can be useful. For example, a 16-year-old reading about cars might be an influencer, but not a buyer. If the same IP shows a different device also reading about cars, it could indicate a family looking to buy, with the 16-year-old as the influencer and a 45-year-old as the decision-maker.

Capturing age data directly from consumers can be challenging, as there's no reason to ask for it just to read an article. Smart ways to capture this information include using third-party data, though this can be difficult due to data-matching properties like email or mobile numbers. Creating books and capturing emails and mobile numbers can help join the dots and make the data meaningful. This approach allows for better targeting and understanding of consumer behaviour.”

Pointing out the bigger challenge apart from data collection, Oram mentioned data reflexes as the key area of concern. 

In the words of Oram, “In today's industry, hiring a data engineer to build data pipelines is not difficult. However, organisations often lack complex data reflexes. For example, knowing a customer's preferred temperature data in the hospitality sector is not enough, you need to train the staff to act on that data. Similarly, logistics partners need to be trained to respond to inventory needs. This is where the industry often underestimates the importance of data reflexes.

Simply putting data together is not enough. It requires logistics, staff training, and enabling data points to be integrated into the marketplace. Many organisations miss out on this aspect. While hiring data engineers and building teams is possible, focusing on data reflexes is crucial for enhancing customer experience, increasing profits, and ensuring long-term customer loyalty.”

To do more with data Oram suggested publishers have a data monetisation department in their office. “Over the last decade, publishers have increasingly leveraged data monetisation departments. Today, apps with large user bases, such as UPI apps, D2C e-commerce, and quick commerce apps, have two departments: one to sell impressions and one to monetise data. 

The traditional media industry has lagged when it comes to data monetisation. Many rely on tools that don't provide transparency, leading to market frustration. Data monetisation for publishers can be a significant long-term win, not just by selling inventory but by effectively utilising data to enhance their offerings,” quoted Oram.

As of now, there are two types of people in the marketing industry: ‘hard-core’ engineers who do programming and design software on the one hand, whereas on the other are the old marketing folks who understand the business and the market. 

According to Mathur (Dabur), there needs to be an intersection of these two cohorts. From the horse’s lips himself, Mathur said, “In this space, there are two main groups: the hardcore tech experts who understand the engineering and stack, and the marketers who are more outward-facing and consumer-oriented. 

However, there's a missing link between these groups—people who understand both technology and data, as well as the business side of the ecosystem. This middle layer can bridge the gap by generating insights that drive the business, allowing the two silos to work together more efficiently.”

Wrapping it up in the words of Sidhwani, “When it comes to data, there is garbage coming in and going out but you need to see how you can cut across the clutter and gather reliable data, which can be then presented to brands to help them advertise with your offerings.”