In today's hyper-competitive retail environment, marketing success often depends on making the right decisions about ad spend. But with so many channels, platforms, and metrics to consider, it can be challenging for retailers to know where to allocate their budget for maximum impact. That's where AI-based decision intelligence platforms come in.
By analyzing data from a variety of sources, including social media, website traffic, and sales data, these platforms can help retailers predict outcomes, recommend actions, and optimize their ad spend in real time. In this article, we'll explore the benefits of using AI-based decision intelligence to make data-driven decisions about ad spend, and provide a step-by-step guide to implementing these platforms effectively. We'll also share some real-world examples of retailers who have successfully used decision intelligence to improve their marketing performance.
Chapter 4 - Real-world examples of ad spend optimization using AI-based decision intelligence platforms
Decision intelligence platforms such as Verteego are still very new on the IT landscape but there are already some success stories of retailers who have used AI-based decision intelligence platforms to optimize their ad spend and achieve better marketing results. Here are a few examples:
A leading online retailer for electronic goods and mobile devices used decision intelligence to optimize their paid search campaigns. By using the platform's recommendations for ad spend allocation and bidding, they were able to increase conversions by 2x and reduce their cost per acquisition (CPA) by 38%.
A travel booking app used an AI-based decision intelligence solution to optimize their Facebook and Instagram ad campaigns. By using the platform's recommendations for targeting, bidding, and creative, they were able to increase their return on ad spend (ROAS) by 40% and reduce their cost per install (CPI) by 50%.
A leading office supply retailer, used a retail decision intelligence platform to optimize their Google Shopping campaigns. By using the platform's recommendations for bid adjustments and product groupings, they were able to increase their ROAS by 20% and reduce their wasted ad spend by 25%.
These examples demonstrate the power of AI-based decision intelligence for ad spend optimization. By using data-driven insights to make better decisions about ad spend allocation, bidding, targeting, and creative, retailers can achieve significant improvements in ROI and overall marketing performance. When combined with the right data sources, machine learning models, KPIs, and metrics, decision intelligence platforms can help retailers stay ahead of the competition and achieve sustainable growth.
Conclusion
In summary, optimizing ad spend is critical for retailers to achieve their marketing goals and maximize their ROI. Traditional ad spend management methods can be challenging due to manual analysis, lack of data integration, and limited insights. AI-based decision intelligence platforms can help overcome these challenges by providing a data-driven approach to ad spend optimization, combining forecasting and optimization algorithms to anticipate various scenarios and select the best one.
By following best practices for using an AI-based decision intelligence platform, retailers can ensure they're making the most of their ad budget, set up machine learning models, define KPIs and metrics, and interpret the results. Real-world examples have shown that these platforms can help increase conversions, reduce CPA and CPI, and improve overall marketing performance.
If you're interested in exploring this topic further, we encourage you to get in touch with Verteego, to understand how our state-of-the-art AI-based decision intelligence platform can be configured for ad spend optimization. We can help you navigate the complex landscape of data sources, algorithms, and metrics, and tailor our solution to your specific needs.
