AI to ROI: Revenue Growth with Artificial Intelligence

The internet is flooded with articles about AI's potential to transform business. But let's be honest - most of them are light on actual strategy and execution.

So, here's something different: a tested process showing how we used AI to deliver genuine revenue growth, and the steps we took to make it happen.

By combining our AI Growth Agent TAYA for message creation with Google's machine learning and smart bidding for delivery, we were able to achieve:

• Increased return on ad spend

• Increased volume of leads

• Decreased cost per lead

• Real revenue growth

The Challenge

Most marketing teams face two major issues:

1. Crafting impactful ad messaging
   Developing messaging that resonates with potential customers needs at every stage of their journey.

2. Delivering the right message, at the right time
   Ensuring precise targeting without draining budgets on irrelevant clicks.

Traditional approaches to these challenges usually involve:

• Hours spent writing and rewriting ad copy

• Manual bid adjustments and campaign management

• High costs per lead

• Limited ability to scale what works

But here's where AI changes the game.

The Solution

Instead of choosing between human expertise and artificial intelligence, we combined both:

1. Growth Agents for Customer Insights & Messaging

We started by using our Growth Agents to analyse customer needs at a granular level. This meant identifying specific pains, gains, and jobs to be done for each product or service. We extracted key pain points enabling us to create messaging that spoke directly to these issues.

By generating multiple iterations of targeted copy, TAYA fine-tuned our approach for each stage of the customer journey, ensuring our messaging was always on point.

2. Google's Hagakure Structure

source: Think with Google

Next, we implemented Google's machine learning and smart bidding via the Hagakure structure. Named after the Japanese concept meaning "hidden by leaves," this approach focuses on simplicity in campaign structure. Here's why it works:

Human marketers might optimise campaigns using 5-6 key metrics, but Google's machine learning processes 7 trillion data signals and adjusts in real time. Let that sink in.

This allows Google to:

• Spot patterns humans would never detect

• Process vast amounts of data in real-time

• Optimise performance and scale what works automatically

By simplifying our campaigns with the Hagakure structure, we let Google's machine learning focus on high-impact optimisations. The result? A reduction in cost per lead in just a few weeks, with highly targeted messaging reaching the right audience at the right time.

source: Think with Google

The Process

Want to try this approach yourself? Here are the key steps:

1. Detailed Customer Analysis: Start by diving deep into customer needs. Use AI to identify specific pains, gains, and jobs to be done for each segment. One of our prompts was: "Please list the most common user motivations when searching for [Product/Service] within [Business Type] [Industry]."

2. Create Targeted Messaging: Use generative AI to craft tailored ad copy for each offering, reflecting the insights gained. Then A/B test the messages with small audiences to refine what resonated most.

3. Structure Google Ads with Hagakure: Simplify your campaign structure by consolidating ad groups and focusing on broader, intent-driven keywords. The Hagakure structure allows Google's machine learning to make continuous optimisations, leveraging data signals beyond human capability.

Curious?

We’ve simplified the process used by our AI Growth Engine so that anyone can apply it using widely available generative AI tools.

Throughout the process, we found that success comes from balancing AI capabilities with human insight. While AI can drive performance, strategic guidance from experienced marketers is essential to steer the campaign in the right direction.

Tips for Success:

• Human-AI Collaboration: Don't leave it all to AI. Human insight is crucial for interpreting data and providing creative input.

• Monitor Automation Closely: Google's machine learning can handle trillions of data points, but human oversight is needed to align performance with overall campaign goals.

• Test and Iterate: A/B testing messaging is vital to understanding what works. Start with smaller ad sets, observe performance, and scale up what works best.

Results 

By adopting this dual approach of human expertise and AI, we not only delivered lower costs per lead and increased ad performance, but also generated real revenue growth.

Conclusion

The future of marketing isn't about choosing between human expertise and AI; it's about combining them intelligently. By using AI to enhance both your messaging and delivery, you can achieve results that neither approach could deliver on its own.