Leveraging generative AI for effective advertising and enhanced customer experiences

Finding the balance between scale and personalised effectiveness in advertising campaigns with generative AI tools requires a thoughtful approach. By combining the scalability of generative AI tools with validated, effective personalisation techniques, companies can create advertising campaigns that resonate with their target audience while reaching a wide audience at scale.

In this two-part series, Adgully seeks to navigate through the realm of AI-led personalisation ads, the risks and pitfalls, and more.

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Navigating AI: The pitfalls of ad personalisation that marketers must avoid

Balancing the scalability of generative AI tools with the need for proven, effective personalisation is an essential component of successful advertising campaigns, says Hiren Shah, Founder & Chairman, Vertoz.

“While generative AI allows huge scalability, companies must prioritize the quality and accuracy of personalised information. This includes investing in robust data quality controls, establishing rigorous testing techniques, and fine-tuning algorithms based on real-time input. Companies may leverage the scale of generative AI while ensuring that personalised content stays relevant and effective by maintaining a dynamic feedback loop and continuously optimising AI models,” he says.

According to him, many BigTech players are experimenting with these offerings around the globe. “Google most recently rolled out campaign and creative management options within Bard. Amazon has claimed to be leveraging AI in their ad tech stack. Meta and Apple will likely integrate AI within Oculus and Apple Vision, respectively. These are going to present major opportunities for brands globally,” says Shah.

Companies can strike the right balance between the scale of generative AI tools and validated, effective personalisation by prioritising data quality and ethical considerations, states Dhaval Gupta, MD, CMRSL, the parent company of CMGalaxy.

“While generative AI enables broad-scale content creation, ensuring that the data used is accurate and representative is vital. Rigorous validation processes using RLHF methodology, including regular audits and testing, help maintain precision. Balancing scale with validation demands a strategic approach that emphasises both the efficiency of generative AI and the accuracy required for effective personalisation in advertising campaigns,” says Dhaval Gupta.

AI is making its mark in app marketing, playing a role in things like personalized recommendations, self-service, virtual assistants, and predictive analytics, points out Adam Smart, Director of Product - Gaming, AppsFlyer.

“While it can predict user behaviour based on past data, it is important to keep a human touch to guide the outcomes. Companies are grappling with finding the right balance between AI tools and human resources. In my opinion, AI should be seen as an extra boost to the team, not a replacement. Picture gifting your team with two extra bionic arms that enlarge their reach and capabilities,” he says.

According to Smart, as marketing, growth, and monetisation teams take on more responsibilities in reaching, engaging, and monetising customers, the need for quick, informed decisions is on the rise. To tackle these challenges, he adds, marketers need to tap into the full potential of AI to amp up growth strategies, covering everything from attracting customers to monetising and retaining them. This means getting instant insights and timely alerts to tweak things like return on ad spend, media revenue, and retention.

“By constantly keeping an eye on vast datasets, a powerful AI can uncover crucial insights and predictions that would otherwise stay hidden or take weeks for teams of data analysts to dig up. A very good example of how AI, combined with the human touch, is making an impact in app marketing is the optimisation of creatives. We recently launched a tool that allows marketers to get insights for maximising ad spend and creatives’ effectiveness. Creative Optimisation automatically analyses creative assets, breaking them down into scenes, and provides performance data and guidance for effectively replacing under-performing elements. Marketing teams can make creative decisions that marry human intuition with the precision, speed, and scalability of the AI capabilities embedded within the product,” he adds.

Customer experiences

In what ways can AI-driven personalisation extend beyond advertising to enhance customer experiences, and what examples exist of successful implementations?

Customer service, sales coordination, data visualisation, and AI-assisted insights are just some of the areas where a personalised approach will enhance experiences, says Dhaval Gupta. Further in the future, personalised AR/VR experiences, gamification, and robotics are industries that AI will empower.

AI-driven personalisation extends beyond advertising to enhance user experiences by personalising interactions across several touchpoints, says Hiren Shah.

“Successful implementations in hospitality and retail include personalised recommendations, seamless user journeys, and predictive analytics. For example, in the hospitality industry, AI can tailor hotel experiences to guests’ tastes, from room amenities to eating choices. AI-driven personalisation in retail improves product recommendations, inventory management, and the overall shopping experience. The way forward lies in using AI to study customer behaviour, preferences, and trends, resulting in more tailored and pleasant interactions across industries,” says Shah.

Personalisation in games is a good example of how AI is able to design levels on the fly based on a player's ability in the game, says Adam Smart. “Procedural level generation has quickly become a go-to for game developers. Rather than defining everything up-front based on rules, the AI engine can now define as it moves through the game. I’m sure that we will see more examples coming from the gaming space in the near future!” concludes Shah.