AI is a trending topic among consumers, business leaders and marketers. While the newer, sexier elements — like content or creative generation — may seem the obvious places to focus, the greatest value often comes from more traditional use cases, especially personalization.
As with anything, having the correct setup is critical. For AI, that means establishing a robust, centralized data platform — combining both structured and unstructured datasets — so brands can improve the relevance of their communications and enhance customer experiences.
Accuracy and governance are fundamental
Whether you’re setting up a simple customer segmentation or a complex lifetime value model, the core tenets of strong data foundations remain the same. Ensuring that inputs across marketing, CRM, websites and apps are clean and accurate is essential to having confidence in your outputs.
Accuracy across all data
Accuracy is just as crucial for unstructured data, which plays an increasingly central role in AI-driven personalization. For instance, if you’re dynamically targeting users with personalized, generative ads, the brand guidelines that inform your creatives must be up to date and accurately reflect the tone and style you want to convey.
Understanding context and gaps
Beyond the accuracy of the data you’ve collected, it’s equally important to understand its context and what may be missing. This is especially true when modeling historical time-series data. If there are gaps (e.g., tracking outages or paused search spend), those gaps need to be identified and accounted for.
Accounting for spikes and anomalies
Likewise, if there are spikes or dips in performance, such as sales surges during Black Friday or a sudden increase in competitor activity, noting them upfront and making the necessary adjustments will enable far stronger outputs.
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Implementing structured data management
Historically, setting up a structured, robust data platform has been a lengthy and often manual process. However, brands are increasingly turning to AI to scale and streamline this work.
Smarter taxonomy management
Every marketer and analyst knows how critical taxonomies are — and how damaging they can be to personalization efforts if misapplied. Yet taxonomy management is rarely anyone’s favorite task and it’s often deprioritized or left unmanaged.
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, there can be an intermediary step — such as having a person validate proposed updates before they go live — while still gaining efficiency and accuracy.
Optimizing product feeds
AI-driven personalization also plays a significant role in managing product feeds, which are used across channels like shopping ads and carousel formats. Traditionally, maintaining these feeds required substantial manual effort, especially for brands with extensive product catalogs and frequent updates.
AI can make the process much 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.
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Using the right tools
There’s no shortage of AI solutions that promise to make marketers’ lives easier while boosting performance. As with any martech investment, 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 martech 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 at the start of 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, we built a custom abandoned basket pipeline for a leading high-street electronics retailer within Google Cloud. By training an AI model on historical customer activity, the brand could send personalized emails instead of a less effective CRM tool.
The result? Ongoing costs and licensing fees were reduced, and revenue from abandoned basket emails increased by 72%.
Dig deeper: Why AI-powered relevance is replacing personalization in B2B marketing
Setting your brand up for success
AI can feel daunting, and knowing where to start isn’t always easy. Despite the seismic opportunities it offers, the foundations of success remain the same as with any other technology:
- A clear view of use cases.
- Robust data foundations.
- 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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Author: Nick Yang