Data analysis has become a huge catalyst of performance marketing, according to experts in the field. Be it predictive analysis for consumer patterns or the extinction of third-party cookies, data will be the next big thing in the field of targeting the right customers.

The last couple of years have seen a significant shift towards the importance of data in the field of marketing, and with the way things are moving towards technology using AI, data mining, analysis and prediction are scoring big for advertisers to create their performance marketing campaigns.

We spoke to industry experts to understand how this technology is getting integrated into performance marketing, which industries are seeing an uptick in its use, and what challenges advertisers can come across while implementing huge data work.

Lakshmana Gnanapragasam, Senior Vice President, Analytics – Epsilon APAC opines that nowadays, organizations are increasingly using various data assets for their marketing and advertising campaigns.

“With the threat of third-party cookies being deprecated, organizations are doubling down on building their own data eco-systems that consist of their own first-party data on their customers and prospects, that are further enriched by highly relevant third-party datasets. Loyalty, CRM programs and Customer Data Platforms (CDPs) are the preferred ways for organizations to build their own data assets. Machine learning-based decision engines are increasingly emerging as a key component of a marketing and advertising infrastructure.”

Gnanapragasam also adds that organizations that are leading the charge and breaking away from the competition in terms of marketing sophistication are executing campaigns that leverage both their owned media and paid media channels thoughtfully to achieve exceptional ROI (Return on Investment) on their marketing spends.

Rajiv Dingra, Founder & CEO of Rebid says that performance advertising has seen a substantial uptick in the adoption of data analytics and AI features.

“This trend is driven by the need for more precise, measurable results in advertising by clients. Our journey at ReBid also points to this direction as we interact with businesses daily who are seeking to harness data and better-harmonised data insights for better advertising ROI. The rise of platforms similar to ReBid and the advent of generative AI which intertwines data analytics with advertising strategies, exemplifies this growth.

These platforms unveil insights across customer journeys from ad to acquisition, thus making performance advertising campaigns more relevant and actionable. The top 3 types of features that have seen growth in the last 2-3 years would be : The craving for real-time feedback has fueled the adoption of analytics, enabling advertisers to tweak campaigns on the fly based on live data;: To tackle the attribution maze, more firms are leaning on sophisticated analytics to trace conversion paths across multiple touchpoints and : These technologies have moved from buzzwords to being at the core of analytical tools, driving smarter, automated decision-making in advertising.”

Vishnu Sharma, Founder & CEO, Efficacy Worldwide says that e-commerce and tech companies are by far doing the better job in transforming data towards campaigns. For example, e-commerce companies like Amazon and Myntra use the data extensively for personalized product recommendations.

On the other hand, Santosh R, Co-Founder & CMO, Elever opines that the industries that have traditionally suffered the most from data-blind models have tended to adopt data analytics first. “For instance, the banking/BFSI industry has a high requirement of Fraud detection, Risk mitigation, Credit rating, etc. One notable industry that has innovatively applied data analytics is insurance. Here some companies, especially newer ones, both in India and abroad are beginning to utilise behavioural data to design their products. For instance, connecting your car's driving data can result in lower premiums if you are a safer driver, or if you just happen to drive a lot less than average (Digit Insurance is one attempting this in India).”

Ramasish Bhowmik, Co-Founder, Adbuffs gives examples of how retail brands use data analytics in their marketing strategies in various ways. “For instance, Sephora utilizes customer data to personalize experiences, offering tailored product recommendations, promotions, and exclusive offers. Pantene employs data analytics to understand customers' hair care needs, reaching them through personalized email campaigns and targeted advertisements. Scentbird, a subscription-based fragrance service, curates personalized perfume recommendations based on individual scent preferences and feedback.”

Despite the growing demand, experts agree that there needs to be a method for using such data and list out some challenges that may arise from these.

“The global market for marketing analytics software, which is set to grow from $32.5 billion in 2022 to an estimated $56.2 billion by 2027, underscores the growing demand for data-driven marketing and advertising campaigns. However, it is vital to handle data responsibly, respecting privacy and ethical considerations, to avoid pitfalls associated with data misuse like collecting or sharing data without consent. AI, such as that employed by Netflix for personalized content recommendations, plays a pivotal role in this by assisting in privacy compliance, data security, and ethical ad targeting,” says Amit Dhawan, Partner & CEO, Art-E.

Gnanapragasam points out examples where organizations either struggle or make mistakes when it comes to their customer data. “‘Identity’ is often not talked about and does not get the attention it deserves in building and using customer data. We have often seen organizations struggle to solve for the ‘identity’ of their customers and end up wasting a lot of promotion and media investments. An ‘Identity’ solution ensures that we are highly certain that this is one individual and one household we know of.

Another is customer privacy. Organizations should ensure that they have taken customer consent before capturing personally identifiable information (PII) of the customers. They should also inform the customers upfront on how their data would be used in the future, and give provisions to customers to decide how and where they should be reached. The other point is commingling PII and non-PII customer data. With more customer data becoming increasingly captured and made available, organizations should ensure that they don’t accidentally mingle the PII data and ID-based non-PII data.”

Dingra adds that “AI acts as a double-edged sword here. On one side, it augments data handling, offering automated, smart solutions for data analysis and campaign optimization. On the flip side, it could magnify errors if fed with flawed data, underscoring the critical importance of accurate, well-governed data.”