AI Can’t Fix a Poor Data Foundation: Brands Need to Get Their House in Order First

At this point, your brand has likely implemented an artificial intelligence tool, or five, in hopes of achieving better campaign performance, predictive targeting or dynamic creative, just to name a few. It’s clear that AI is dominating industry conversations and has proven to be a valuable asset to marketing teams. But AI tools are only as good as the data they’re built on.

What does that mean? AI tools need the right data and brand context to model from to provide actionable intelligence. Without the right inputs, marketers may just be mistaking AI for automation. Many platforms boast high-quality data, but that alone may not be enough to draw significant insights and recommendations that will ultimately drive profitable growth.

Key Data Considerations

From Meta and TikTok to Shopify and Amazon.com, some of the biggest marketing and commerce platforms offer AI-powered marketing tools designed to streamline campaign management from start to finish. While that's a helpful start, it only considers its own siloed data. Can we truly trust AI’s outputs without clean, enriched and owned data as inputs? AI doesn’t create magic and is only as good as the data you feed it. Therefore, it’s time to evaluate if your tools are being properly nourished in order to provide the most comprehensive view of your customers.

Why First-Party Data Matters More Than Ever

First-party data is information collected directly from your brand’s customers through interactions on owned channels such as websites, apps, or email. First-party data reflects real, direct engagement with a brand, making it the most accurate and reliable input for marketing decisions. E-commerce brands can gather this data through channels like purchase history, on-site behavior and customer surveys.

To unlock deeper insights, brands can enrich their first-party data by layering in select third-party attributes, such as demographics, lifestyle indicators or household data, to fill in the gaps. This enriched data provides a fuller view of customer segments, empowering marketers to craft highly personalized, effective strategies that go beyond surface-level targeting. However, not all third-party data is created equal.

Where Brands Often Miss the Mark

Marketers have to conduct due diligence to ensure the third-party data they’re using is reliable and accurate. Some providers rely on AI bots to gather information, resulting in customer data that's scraped from public-facing sites and often pulls in inaccurate or outdated information. The most effective way to utilize third-party data is to partner with an ethical provider that offers demographic, psychographic and behavioral attribute enrichment. These providers source their data from self-reported information, government records, or data that customers have explicitly agreed to allow brands to access. Data should be regularly refreshed to maintain accuracy and remain current.

It’s understandable that pressure is mounting as marketing teams face smaller budgets and leaner teams. So when a platform boasts revenue-boosting AI tools, many are quick to jump on board without taking a step back to consider the bigger picture. By relying solely on black-box algorithms that don’t consider a brand’s own customer data, marketers may find themselves mistaking campaign automation for real business strategy. AI can be an extremely powerful asset when utilized to its full potential.

Building a Solid Data Foundation

The good news is that brands probably already have a lot of the information they need to start building a strong data foundation with first-party data. If a customer has purchased a product, you most likely already have details like their name, shipping address, and purchase history.

Some platforms roll first-party data enrichment, analytics and AI capabilities into one, making it simple to maintain and execute on data in one location. Others provide just the enrichment layer — or third-party data sources. When using this option, it’s important to keep in mind the ethical use of data, reliability of the data, security, and compliance. Ultimately the goal is to power AI agents with a combination of purchase data, marketing data, and enrichment attributes to provide the fullest view of your customers.

Driving Long-Term Profitability and Growth

Short-term gains don’t always contribute to the long-term health of a brand. By incorporating enriched first-party data, purchase data, and marketing data, you can ensure you're training the AI agent effectively. Additionally, leveraging machine learning to help identify and target high lifetime value customers will be the key to securing profitable growth. Now is not the time to be intimidated by adopting and leveraging the power of AI. It plays an important role in analyzing your customer data to generate personas quickly, enable better segmentation, more personalized marketing, enhanced acquisition, and improved overall customer experience.

Take Control of Your Data

The harsh reality is if you don’t have a strong data foundation, AI isn’t reaching its potential or might even lead to false conclusions. These tools can be extremely beneficial for marketers, but the more robust the data and models you’re able to provide to train the AI agent, the better the outcomes. Now is the time to invest in ensuring that your brand has a solid data foundation and treating it like the competitive advantage it is.

Cary Lawrence is the CEO of Decile, a customer data and analytics platform whose mission is to help e-commerce brands grow profitably.