Harsh Karmarkar (Director of Solutions Consulting, Tealium) sat down with our friends at The Trade Desk (Natalie Kansteiner, Director of Ecosystems & Data Partnerships) and Snowflake (David Wells, Industry Principal Media & Advertising) to discuss the modern customer journey. In our recap blog, we key in on the role of Artificial Intelligence (AI) in understanding and modifying the customer data landscape. 

One key point to start off our discussion: you’ll have an easier time getting started with AI if you have a clean data set with fewer dimensions. This supports quicker insights and model development. It reduces the complexity and noise that often comes with larger, more comprehensive data sets, making it easier to identify key patterns. This focused approach helps in refining models efficiently before scaling up to the full dataset.

How Can AI Transform The Customer Experience?

AI can transform the customer experience. To use AI to transform the customer experience, you’ll need clean, high-quality data. In the best case (and most effective) scenario, this means combining a real-time data strategy with your AI models to produce the best outcomes.

All three panelists echoed the importance of AI in effective data management and better customer experiences. Good data can support AI-driven customer insights in several impactful ways. Here are two examples:

Act In Real-Time

Harsh expressed how crucial it is to act in the moment and understand the context of consumers’ actions. From his standpoint, implementing AI in combination with a real-time data strategy (see our blog What Is Real-Time Data?) helps you meet the customer in the moment, leading to improved KPI achievement.

Personalized Customer Experiences

AI and real-time data play a crucial role in supporting personalized customer experiences by enabling businesses to tailor interactions based on the most current information about customer behavior, preferences, and needs. 

Real-time data allows AI systems to gather immediate information from various touchpoints, such as website interactions, mobile apps, social media, and customer service interactions. For instance, as a customer browses an online store, AI can track which products they view, how much time they spend on each page, and what items they add to their cart.

Real-time data combined with Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the way businesses work. Advanced AI tools are making data analysis more accessible to non-technical users by automating routine tasks and providing timely, granular insights.

How Do AI and Customer Data Work Together?

AI and customer data work together in key ways to create powerful insights and personalized experiences. Let’s explore the ones our speakers noted!

Data Collection

Businesses collect vast amounts of data from various sources such as CRM systems, social media interactions, website behavior, purchase history, and customer feedback. However, it is critical to collect this data with consent and care. All three speakers emphasized the importance of a data-privacy-first mindset to future-proof your business (especially as regulations and policies like third-party cookie deprecation shift).

Customer Segmentation

AI uses techniques like clustering to group customers into segments based on similarities in their data, such as buying habits, demographic information, or engagement levels. Natalie called out the importance of customer segmentation to help businesses target each segment with more relevant marketing strategies or customer experiences.

Customer Insights

David accentuated the value of customer insights, underscoring the need for accessibility. AI can help marketers interpret raw customer data into actionable insights. This innovation helps technical and non-technical marketers alike to understand customer preferences and trends at a granular level. Customer insights can inform product development or optimizations.

Next Steps

To watch the full webinar, explore our discussion here.