The Power of AI in Personalization
Artificial intelligence (AI) has become a hot topic across various industries, from consumers to business leaders and marketers. While the more eye-catching aspects of AI, such as content or creative generation, often steal the spotlight, the real value frequently lies in traditional applications—especially personalization. This is where AI can make a significant impact by enhancing customer experiences and making communications more relevant.
To unlock this potential, it's crucial to have the right setup. For AI, this means creating a strong, centralized data platform that combines both structured and unstructured datasets. This allows brands to better tailor their interactions with customers and improve overall engagement.
Accuracy and Governance Are Fundamental
Whether you're working on a basic customer segmentation model or a complex lifetime value analysis, the importance of solid data foundations remains consistent. Ensuring that data from marketing, CRM, websites, and apps is clean and accurate is essential for building confidence in your outputs.
This accuracy extends to unstructured data as well, which plays an increasingly vital role in AI-driven personalization. For example, if you're using dynamic targeting for personalized, generative ads, the brand guidelines that shape your creatives must be up-to-date and reflect the desired tone and style accurately.
Understanding Context and Gaps
Beyond just ensuring data accuracy, it's equally important to understand the context of the data you've collected and what might be missing. This is particularly true when dealing with historical time-series data. If there are gaps—such as tracking outages or paused search spend—these need to be identified and addressed.
Similarly, spikes or dips in performance, like sales surges during Black Friday or sudden increases in competitor activity, should be noted upfront. Making necessary adjustments based on these insights can lead to stronger results.
Implementing Structured Data Management
Historically, setting up a structured, robust data platform has been a time-consuming and labor-intensive process. However, many brands are now turning to AI to streamline and scale this work.
Smarter Taxonomy Management
Taxonomies are critical for marketers and analysts, yet managing them is often seen as a tedious task. AI can provide real value by:
- Monitoring activity across platforms.
- Automatically flagging non-compliant naming conventions and suggesting the correct version.
- In some cases, automatically updating the platform itself.
For brands that prefer more control, an intermediary step—like having a person validate proposed updates before they go live—can still offer efficiency and accuracy.
Optimizing Product Feeds
AI also plays a significant role in managing product feeds used across channels like shopping ads and carousel formats. Traditionally, maintaining these feeds required substantial manual effort, especially for brands with large product catalogs and frequent updates.
AI can make this process more efficient by:
- Dynamically filling in missing or incorrect product fields—such as color, size, or description—based on product images or other data in the feed.
- Proactively optimizing product titles and descriptions, which significantly impact campaign performance.
By training AI solutions on past campaign results, brands can identify which types of descriptions perform best and apply those learnings across their existing feeds, improving both efficiency and outcomes.
Using the Right Tools
There are numerous AI solutions available that promise to simplify marketers' tasks while boosting performance. The key is to align your ambitions with your existing setup to determine which solution is right for you.
Start with Embedded AI
For most businesses, the best place to begin is with the embedded AI features already built into adtech and marketing platforms. Tools like Google Ads, Adobe Analytics, and Meta Business Manager include a wide range of AI-powered capabilities—from bid strategies and automated insights to creative generation.
Most of these features don’t require specialist AI expertise, making them an excellent entry point for brands starting their AI journey.
When to Consider Applied AI
Some brands eventually reach the limits of embedded AI and require more advanced or customized applications. In these cases, using a centralized data platform to build bespoke applied AI solutions can deliver more tailored results.
For example, a leading high-street electronics retailer developed a custom abandoned basket pipeline within Google Cloud. By training an AI model on historical customer activity, the brand could send personalized emails instead of relying on a less effective CRM tool. The result was a 72% increase in revenue from abandoned basket emails, along with reduced ongoing costs and licensing fees.
Setting Your Brand Up for Success
AI can feel overwhelming, and knowing where to start isn't always easy. Despite the vast opportunities it offers, the foundations of success remain similar to any other technology: a clear view of use cases, robust data foundations, and a practical approach.
Personalization is a natural fit for AI, and there are many areas for brands to explore. Whether you begin with embedded AI features or move toward more advanced applied solutions, confidence in the underlying data that powers them will always be the key to stronger performance and more meaningful customer experiences.
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