When it comes to AI and Customer Experience, too many brands chase the shiny outcome before laying the groundwork. Experts from Future Marketing and Tealium walked us through the not-so-glamorous, but absolutely essential, foundation needed to make AI-powered personalization more than just a buzzword.
Start with the Basics: Your Data Is Probably Not as Ready as You Think
Sascha from Future Marketing pulled no punches: “Everyone wants to jump to AI, assuming the data is already there. But in reality, most companies haven’t set the foundation.” He shared how, in a real-world customer project, they hit pause on CDP implementation and spent six months cleaning up the data layer, restructuring CRM databases, standardizing tracking, and aligning event definitions, just to make sure the data could be trusted and used across channels. Not sexy, but crucial.
No Foundation, No Success
“If your data isn’t right, your AI won’t be either,” Sascha emphasized. From misaligned purchase tracking to inconsistent customer interaction data, the team uncovered foundational flaws that could have derailed the entire personalization strategy. His advice? Align cross-functional teams, get executive buy-in, and treat foundational work as a program, not a side project.
AI That Actually Works—Because the Data Does
Tim and Simon from Tealium built on that theme, showing how Tealium Predict enables brands to run propensity models without a full-blown data science team. With clean data and a CDP in place, a few UI clicks are enough to launch powerful predictive models that refine audience definitions and drive targeted actions. No heavy lifting required.
Simon also gave an inside look at Vodafone’s journey. By integrating real-time behavioral data with AI-based scoring models, Vodafone could prioritize call center queues based on purchase intent. “The result? A tangible boost in conversion—measured, not assumed,” Simon said. He added that proper data taxonomy and governance were key to making it all work.
Build, Measure, Iterate
Both teams stressed the importance of a “crawl, walk, run” approach. AI models rarely deliver gold on day one. Start small, measure relentlessly, and evolve. “It’s not a failure if your first test doesn’t work,” Sascha reminded. “It’s a learning opportunity.”
Simon summed it up best: “AI doesn’t magically fix bad data. But with a solid foundation, real-time capabilities, and the right mindset, it becomes a powerful engine for smarter, faster, and more human customer experiences.”
5 Key AI Implementation Insights
- Importance of Data Foundations: Getting the data foundation right is critical before implementing AI or advanced technologies like Customer Data Platforms (CDPs). Without proper data taxonomy and identity resolution, insights derived from AI systems may be incorrect, leading to failed implementations.
- Start Small and Scale Gradually: The ease of initiating AI projects without extensive resources, reinforces the idea of starting small with experiments and iterating to refine models. This approach minimizes risk while gradually gaining the expertise and buy-in necessary for scaling larger initiatives.
- Align AI Initiatives with Business Goals: AI projects must be aligned with specific business goals, whether it’s customer acquisition, retention, or enhanced experiences. Establishing clear metrics and measuring tangible outcomes is vital to attribute success and avoid projects becoming “science experiments.”
- Case Studies and Tangible Benefits: Real-world examples were shared, including a case where AI modeling prioritized calls in a call center, resulting in a 52% uplift in converted sales calls. Another highlighted the use of prediction data by Vodafone to boost sales conversions through a CDP, showcasing the power of personalized experiences driven by AI.
- Ease of Integration and Iterative Improvement: The simplicity of refining audience definitions and deploying models like “A Team Predict” with minimal technical expertise has been reinforced. The session underscored the ease of integrating AI models into various systems for real-time optimization, allowing businesses to achieve impactful results without needing a massive data team upfront.
→ If you are curious to hear more,
you can watch the full session on-demand.
