AI, MarTech & Data: The New Power Trio For Marketing Success

AI, MarTech and data are redefining the rules of modern marketing. As AI reshapes customer engagement, personalisation and campaign efficiency, companies must leverage advanced MarTech tools and robust data practices to stay competitive. But many marketing leaders struggle to use MarTech as effectively as they should be. Ineffective use of MarTech tools and reliance on synthetic data can expose businesses to security vulnerabilities. Having a comprehensive set of strategies and aligning AI, MarTech and data effectively is necessary to drive the best marketing results. 

How Combining AI, MarTech & Data Helps 
CMOs are in pole position to shape the technology infrastructure and investments within their organisations. In Infosys’s CMO Radar report, two-thirds of respondents said that the CMO's influence over technology is increasing.  

Combining AI with MarTech and data has several benefits for marketers. In the preplanning stage of a campaign, it helps marketers assess the positives and negatives of competitors’ campaigns and build a campaign that drives the most impact. This combination of technology stacks can help marketers analyse historical data and suggest which ad channels to spend on to reach their target audiences effectively. It assists them with creating and disseminating marketing campaign-related content including email, newsletters and social media posts. It helps personalise promotional messages to audiences based on their data analysis of their past purchases. For example, Dentsu and Merkle has introduced a generative AI solution that businesses can use with Salesforce Einstein GPT to help brands create personalised marketing to connect with their customers.  

It also analyses the effectiveness of the campaign after execution, to help with improving future campaigns. A poll for marketers shows that according to them, the greatest benefit of using AI in their work was time savings.  

However, a Gartner survey shows that MarTech use reduced from 42 per cent in 2022 to 33 per cent in 2023. As per a Nasscom study, factors in AI execution that demand immediate attention include execution discipline and use case selection. Having a modular MarTech stack makes it difficult for marketers to integrate the wider range of AI applications into it and thus to reap the benefits of AI. Their IT infrastructure being too basic to enable integration is also a disadvantage. Marketers also fail to do away with overlapping tools, which causes process inefficiencies. There is a lack of training on the usage of tools and awareness on measuring their impact. As per the MMA Global India report, 69 per cent of respondents said that skilling and training are among the top challenges for AI adoption in marketing. 

A further challenge for marketers is that they tend to use synthetic data, which is artificially created data that is used instead of real data, either because real data isn’t available or isn’t suitable for the task. Synthetic data can be useful in many circumstances but it also brings its own challenges including not capturing the diversity and nuance of real-life datasets, which in turn can lead to incomplete inferences being drawn.   

CMOs anticipate that AI tools will drive effectiveness, efficiency and elevate customer experience. But achieving this depends on a reliable technology and data infrastructure, along with an overall strategy and risk management that is up to the task. Following certain practices can solve their challenges and help them accomplish these factors.  

Solutions For Marketers 

• The first step is forming a robust AI strategy that aligns marketing goals with business goals. Marketers should make certain their MarTech stack is cloud-native and scalable to allow  a wide range of AI tools and use cases. It needs to include access to components such as customer data platforms, marketing automation tools, and analytics workbenches. This helps increase customer responsiveness and achieve cost reduction related to system changes, to get optimal business value.  
• Simply procuring the tools is not enough. Companies must invest in reskilling and training programs to educate their marketing teams in the usage to increase technology adoption.  
• Marketers need to define data governance for suitable data use in their AI-driven MarTech. Establishing a team to assess data quality and adherence to AI ethics and protocols is recommended. Having a unified data platform ensures the data is easily accessible and actionable. 
• Marketers need to incorporate risk management into their AI implementation in the preliminary stages, to drive customer trust. With data and AI use tied to compliance requirements and regulations, setting up a risk management office for fulfilment of these aspects is critical. The office will remain vigilant against potential risks, addressing issues in advance, mitigating the risk of non-compliance, and reducing penalty costs associated with regulatory violations.  

This holistic approach to using AI, MarTech and data can help marketers maximize their use of the technology to get the results they desire. 

John Premkumar

Guest Author The author is the Vice President & Head of Delivery – Digital Experience at Infosys