Using third-party data to train AI: A guide for marketers

Large language models and artificial intelligence are quickly becoming essential for Australian marketers looking to bring personalisation, efficiency, and scale to their omnichannel strategies. Whether it’s tailoring offers in real time or orchestrating the perfect mix of touchpoints, AI helps turn sprawling data into focused and ever-improving action.

To train these models, brands are increasingly relying on their own first-party data. It’s a strong starting point, but no single dataset can capture the full complexity of today’s customer journey. First-party data reflects what a brand already knows, but not always what it needs to know. This is where high-quality third-party data becomes critical: It fills the gaps, extends visibility, and ensures that AI-driven insights are complete and representative.

Here are three practical scenarios showing how third-party data plays a critical role in training AI to deliver better marketing outcomes.

Powering personalisation at scale

Personalisation is one of the biggest promises of AI in marketing, but delivering it at scale can be challenging. Brands can only personalise content as far as their customer understanding allows — and for many, that understanding is incomplete. AI models need broad, accurate data to fuel relevant, dynamic personalisation across every customer touchpoint.

Scenario: A major Australian bank is building an AI model to personalise digital content, email offers, and in-app messaging across its online banking and mobile app channels.

What the brand has: The bank has transaction histories, account types, and customer service interactions. It knows when customers pay bills or receive salaries but lacks insight into broader lifestyle shifts that influence financial needs.

The gap third-party data fills: By adding lifestyle, behavioural, and intent data from third-party sources, the bank can enrich its customer profiles with insights that indicate major life events — such as preparing to buy a home or start a family — that first-party data alone doesn’t reveal. This enables smarter segmentation and personalisation, helping the bank proactively offer relevant products or educational content to customers when they need them.

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Building and optimising the omnichannel mix

AI is also reshaping how marketers allocate budgets across fragmented channels. Knowing which channels deserve investment — and when — is critical for brands looking to maximise the effectiveness of every marketing dollar. Yet many brands struggle to optimise spend when data is siloed or overly retrospective.

Scenario: A leading hotel chain in Australia wants to use AI to dynamically allocate marketing budgets across programmatic, social, search, CTV and on-property promotions to drive bookings and maximise yield across its portfolio.

What the brand has: The hotel chain collects data on direct bookings, website interactions, and seasonal occupancy rates, but these insights are largely backward-looking, making it challenging to anticipate and act on emerging traveller demand.

The gap third-party data fills: By incorporating intent signals, geographical data, and cross-device behavioural insights from third-party providers, the hotel chain can train its AI models to predict where and when travellers are likely to engage next. For example, intent data might reveal a rise in interest for weekend getaways in specific regions. This empowers the AI to adjust spend proactively, helping the brand capture bookings before competitors and optimise occupancy across peak and shoulder periods.

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Improving product recommendations

Recommendation engines are one of the most recognisable uses of AI in marketing, but their effectiveness depends on the breadth and accuracy of the underlying data. In retail, AI-powered recommendations can enhance customer experiences and drive incremental revenue, but only if the system understands a customer’s real needs and context. This requires third-party data.

Scenario: A national retail chain in Australia is using AI to power its recommendation engine across its ecommerce platform and mobile app, aiming to increase average order value and repeat purchases.

What the brand has: The retailer has transaction histories and browsing data, enabling it to infer certain preferences and buying patterns. However, this first-party data provides only a partial view, limiting its ability to anticipate needs and personalise suggestions effectively.

The gap third-party data fills: By enriching customer profiles with lifestyle, intent, and behavioural data from third-party sources, the retailer’s AI can understand broader interests and potential upcoming needs. For example, third-party data might reveal that a customer who has recently purchased activewear is also researching wellness products, indicating an opportunity to promote related items such as fitness accessories or nutritional products. This additional layer of insight allows the retailer’s recommendation engine to surface offers and content that feel timely and relevant. When applied thoughtfully, third-party data can transform recommendations from simple upsells into valuable, curated experiences that build trust and encourage repeat business.

Even the best AI model is only as good as the data it learns from. Brands aiming to use AI effectively need breadth, accuracy, and transparency in their data. Eyeota, a Dun & Bradstreet company, offers the kind of high-quality, privacy-compliant third-party data that helps brands enrich customer intelligence, sharpen personalisation, and leverage AI as a practical advantage.