Some key ethical considerations for using AI in marketing campaigns include data privacy and security, which require transparency in data management regarding collection, transformation, and usage, alongside consent management for every use case and a well-established data security layer.
We are somewhere in the final 100 days of 2024 and with technology at the cusp of its potential, it is no longer cool to work hard but smarter. As businesses strive to make every advertising paisa count, artificial intelligence has seemingly stepped in as the messiah to transform the entire martech game. By harnessing the power of data and automation, AI is helping marketers unlock new levels of efficiency, making the dream of delivering the right message to the right person at the right time a tangible reality. As the popular saying goes, with greater power comes greater responsibility, it is extremely important to keep an eye on AI’s growing role in marketing which can bring both unprecedented opportunities and challenges.
Optimising Performance Marketing with AI
70% of marketers plan to increase performance marketing spend this year at the expense of brand building, a report by Nielson revealed. “AI is revolutionising performance marketing by optimising campaign strategies through real-time data analysis and predictive modelling. At Admattic, we use AI to enhance targeting precision, identifying the most relevant audience segments based on behavioural patterns and preferences. Machine learning algorithms continuously refine these segments, maximising engagement and minimising wasteful spending,” Abhinay Tiwari, chief growth officer, Admattic told BrandWagon Online. AI-driven creatives personalise ad content dynamically, improving conversion rates by delivering tailored messages at scale. This automation allows marketers to focus on strategy, while AI enhances decision-making, creating a smarter, more efficient ecosystem for performance marketing, he added.
Furthermore, AI helps marketers adjust ad placements, bidding strategies, and audience targeting in real time. This not only ensures that campaigns are more efficient but also minimises wasted ad spend by automatically delivering ads to the right audience at the right time.
Measuring AI effectiveness in marketing
To effectively measure the impact of AI in performance marketing, a mix of traditional performance indicators and AI-specific metrics is essential. “While Return on Ad Spend (ROAS) and Cost Per Acquisition (CPA) remain foundational in assessing the financial return of campaigns, these metrics alone don’t capture the full picture of AI’s capabilities. Marketers should also focus on long-term metrics like Customer Lifetime Value (CLV), which shows how AI-driven personalisation impacts customer loyalty over time. Conversion Rate is another key metric, offering insight into how well AI improves the effectiveness of messaging and targeting,” Ranjit Thind, director-media and tech, Asymmetrique, said. Beyond these, AI introduces a new layer of measurement—predictive accuracy—which tracks how well machine learning models forecast future behaviour and outcomes. This continuous improvement in predictive performance allows marketers to fine-tune campaigns and unlock deeper insights from their data.
Key benefits of AI integration
The ability of AI to process large volumes of data at lightning speed is one of its greatest advantages. This allows for real-time decision-making and a deeper understanding of consumer behaviour. Integrating AI into performance marketing significantly enhances targeting and personalisation while optimising campaigns in real-time. “By leveraging predictive analytics, AI reduces costs through improved bid management and the prevention of ad fraud. This technology enables marketers to scale campaigns efficiently across various platforms and enhances customer experiences with personalised content,” Hitesh Nahata, director – data science and analytics, MiQ, opined. Furthermore, AI plays a crucial role in detecting and preventing ad fraud, ensuring more accurate campaign metrics and ultimately leading to greater return on investment (RoI). Additionally, AI reduces the need for manual campaign optimisation, freeing up marketers to focus on more strategic tasks.
The underlying challenges
With advantages, come challenges too. “Marketers face several challenges when implementing AI in their campaigns. First, data quality is critical—AI models require accurate, comprehensive data to deliver effective insights, and gaps or inaccuracies can skew results. Integration complexity is another hurdle, as legacy systems often struggle to align with AI tools,” Tiwari added. Additionally, cost can be a barrier, particularly for smaller companies with limited resources. Finally, there’s the challenge of skills: marketers must adapt to working with AI technologies, requiring training and collaboration between data scientists and marketing teams for optimal outcomes, he added. The learning curve for AI technology can also be steep, requiring marketers to invest in training and development to effectively utilise AI tools.
AI’s role in predicting consumer behaviour
AI offers an unprecedented ability to understand and predict consumer behaviour, providing marketers with insights that were previously out of reach. “Through machine learning and data analysis, AI can sift through vast amounts of customer data—everything from purchase history and browsing patterns to social media interactions—uncovering hidden patterns and trends. This deep analysis allows marketers to predict what customers are likely to do next, such as making a purchase, abandoning a cart, or engaging with a particular piece of content. By leveraging this predictive power, brands can create highly targeted campaigns that meet customers’ needs before they even realise them,” Thind commented. This proactive approach shifts marketing from being reactive to strategic, enabling brands to strengthen relationships with consumers and build loyalty over time. Furthermore, 90% of commercial leaders expect to utilise gen AI solutions “often” over the next two years, research by McKinsey revealed.
The ethical considerations
Some key ethical considerations for using AI in marketing campaigns include data privacy and security, which require transparency in data management regarding collection, transformation, and usage, alongside consent management for every use case and a well-established data security layer. “Algorithm bias must be prevented in outputs through appropriate checks and balances, including an audit mechanism to keep bias in check. Transparency and explainability are essential to make AI decisions and outputs understandable, thereby reducing ambiguity around data usage and the development of AI models,” Nahata cited. Consumer autonomy and control should be prioritised by allowing users to opt out of AI personalisation and data collection, as well as providing control over their data and privacy preferences. Lastly, accountability and responsibility involve maintaining human oversight of AI systems and establishing clear lines of responsibility for ethical AI use. “While AI enables hyper-targeted marketing, this mustn’t cross the line into manipulation or over-intrusion, which could alienate consumers rather than engage them. Marketers must navigate these ethical waters carefully to build trust and ensure the responsible use of AI,” Thind added.
AI is transforming performance marketing by optimising campaigns, predicting consumer behaviour, and improving targeting precision. While challenges such as data quality and ethical concerns remain, it seems as if the potential benefits far outweigh the hurdles.
