Wiland Blog

Today’s marketing teams are under increased pressure to grow revenue and contribute to their organizations’ bottom line. The solution? Better use of data.

We recently hosted a webinar about the ways second- and third-party data collaboration can accelerate and sustain your organization’s revenue growth. Bruce Biegel from Winterberry Group and Wiland’s Emma Nicoletti and Dennis Smith discussed the ways that enhancement data can inform predictive modeling to enable better targeting, personalization, segmentation, and customer experience.

If you’re a marketer tasked with driving measurable results but you missed the live event, you can watch this webinar now to hear the latest thinking about data collaboration and practical ways to make it work for your organization. Here are some of the key takeaways:

1. Privacy regulations are driving change.
   Winterberry Group’s Bruce Biegel described how concerns about privacy and the current hodgepodge of state-level regulations are the main factors driving the shift towards first-party data and permissions-based consent management solutions. As a result, investments in secure data infrastructure are surging.

   

2. The “data layer” of a marketing tech stack is not monolithic.
   It’s composed of a complex array of interconnected data sources and management solutions, from CDPs and data cooperatives to data warehouses and everything in between. While some organizations have the resources to manage a number of these solutions in house, most are working with partners to fulfill vital parts of the data layer function.

3. First-party data is essential—but not enough.
   Data partners like Wiland provide insights based upon trillions of dollars of spending behavior across 255 million Americans—including their spending in your category with you and your competition and across the consumer’s wallet. That’s like supercharging your own first-party data. Nothing is more predictive than real spend data.

4. Modeling and AI are transformative—but still require data.
   When you work with a data partner, the first step is to understand how well that partner’s third-party data aligns with your own first-party data. That allows you to collaboratively build models that are far more predictive than either dataset alone could allow.