AI revolution in advertising Part 2: Showcasing AI-powered optimisation strategies

In the landscape of digital advertising, the integration of artificial intelligence (AI) has become a pivotal force, reshaping strategies and redefining success metrics. But what are the intricacies of AI-driven predictive analytics in the realm of ad spend optimisation? In the second part of this two-part report, Adgully focusses on the key performance indicators (KPIs) that ad agencies prioritise to gauge the effectiveness of these sophisticated algorithms. By delving into the metrics that showcase tangible returns on investment (ROI), the report aims to shed light on the transformative power of AI in shaping the future of advertising, where data-driven decisions are not just a luxury, but a strategic imperative. 

Gautam Reddy, Founder & CEO, PAD, gives a thorough examination of the KPIs that advertising companies pay special attention to in order to evaluate how well AI-driven predictive analytics work for optimising ad spend: 

The return on advertising spend (ROAS) metric calculates the amount of money made for every dollar invested in advertising. It’s a critical sign of how profitable AI-powered ad optimisation tactics are. A greater ROAS shows that the AI system is distributing ad spend wisely in order to increase income. 

Cost Per Lead (CPL): This statistic illustrates how much it typically costs to obtain a new lead via advertising. A reduced cost per lead (CPL) means that the AI system is more successful in finding and pursuing high-quality leads, which translates into more economical client acquisition. 

Customer acquisition cost (CAC): This statistic calculates the entire cost of bringing on a new client, taking into account sales and marketing expenditures. A lower CAC suggests that the AI system is more effectively optimising ad spend to gain new clients. 

Conversion rate: This statistic expresses the proportion of website visitors who complete a desired activity, such buying something or subscribing to a newsletter. An increased conversion rate suggests that the AI system is presenting interesting and pertinent advertisements that encourage click-throughs. 

Click-through rate (CTR): This metric calculates the proportion of clicks that occur from ad impressions. A higher CTR shows that the AI system is producing attention-grabbing and click-worthy ad creatives. 

ROI (campaign return on investment): This statistic calculates the total return on investment for a marketing campaign. It takes advertising expenditure and revenue into account. A higher campaign return on investment (ROI) suggests that the AI system is making the most of ad spend in order to maximise return on investment. 

By closely monitoring these KPIs, ad agencies can assess the effectiveness of AI-driven predictive analytics in optimising ad spend and demonstrate a tangible ROI to their clients. AI-powered optimisation strategies that consistently deliver positive results on these metrics can solidify the value proposition of AI in the advertising landscape. 

Gaurav Gupta, Head of Performance, Data & Martech at Oplifi, emphasises that the KPIs in AI-driven predictive analytics, considered as leading indicators, should seamlessly transition into tangible business metrics. A significant determinant of AI predictive analytics adoption hinges on rigorous A/B testing. Consequently, he adds, the success metrics revolve around evaluating the effectiveness of insights derived from AI compared to manual media efforts. The primary focus is on driving key leading metrics such as Average Order Value (AOV). 

“One of the major KPIs we keep track of when evaluating ad spend optimisation is the conversion rate,” says Himanshu Arora, Co-Founder, Social Panga. “The more precise the AI-driven ads are, the higher the conversion rate is. If we understand and tailor ads to every user’s taste and preference, then the chances of them clicking on it, engaging with it and buying the advertised product or service, are way higher. Higher conversion rates point to a tangible ROI.” 

Case studies

Let’s explore some case studies and practical examples that highlight how advertising agencies have not only adopted AI-powered analytics for optimising ad spend, but have also demonstrated a notable improvement in Return on Investment (ROI). This provides a clear understanding of the impact on their clients' advertising campaigns. 

Gautam Reddy provides the following noteworthy case studies that demonstrate the revolutionary effect of AI-powered analytics on ROI and ad spend optimisation for advertising agencies and their clients: 

Case Study 1:  Accenture Interactive and KFC

Global fast-food business KFC had to figure out how to best allocate its advertising budget among a variety of markets and media. To determine which channels were most effective and to adjust ad spend accordingly, they required a data-driven approach. 

The Solution: Accenture Interactive put in place a predictive analytics platform driven by AI to evaluate a tonne of data, including past sales information, client demographics, and online activity. The tool forecast the most lucrative ad placements and bidding tactics for every market using machine learning algorithms. 

The Impact: By leveraging AI-powered analytics, Accenture Interactive helped KFC achieve a 20% increase in ROI and a 15% reduction in ad costs. The AI system accurately predicted the most effective ad placements, maximizing engagement and conversions. 

Case Study 2: Procter & Gamble (P&G) and GroupM

Global consumer goods major P&G faced the challenge of optimising their advertising budget for the Pampers brand across different regions and platforms. Their objectives included minimising unnecessary ad expenses and enhancing campaign effectiveness. By considering online behaviour, interests, and demographics, the brand successfully generated precise audience profiles. 

They then delivered customised ad creatives to the appropriate viewers at the appropriate times using AI-powered ad placement optimisation. 

The Solution: Based on variables, including user profiles, auction dynamics, and conversion probabilities, GroupM deployed an AI-powered bidding platform to dynamically modify ad bids in real time. The platform made sure that advertising were positioned in the most advantageous locations by optimising ad budget for each impression using machine learning algorithms. 

These case studies, according to Reddy, show how AI-powered analytics may improve ROI and ad budget optimisation in real-world ways. Ad agencies now rely heavily on AI as a tool to help them make data-driven decisions, provide individualised ad experiences, and optimise ad budgets more precisely, all of which improve client outcomes. 

One example that Himanshu Arora cites often is that of Netflix. Elaborating on their strategy, he says, “They use AI-driven analytics to study consumer behaviour and provide content recommendations accurately. With your user history, their AI matches the genre, content type, and something as simple as your favourite actor to your next recommendation. They nail it each time. They harness the power of AI in their creative decision-making, and their ROI is the increased subscriptions they see year on year. Here, AI might be working in its ecosystem, but it is the creative direction of the people behind the OTT that breathes life into the final output.” 

Gaurav Gupta cites the case of a fashion brand leveraging AI for ad spend optimisation. “By analysing customer data, the agency identified the most profitable audience segments and optimised ad placements accordingly. This resulted in a 30% increase in ROAS, showcasing not only the successful integration of AI analytics, but also a substantial improvement in the client’s advertising campaign performance.”