How IPSY and Nextdoor Harness the Power of Unified Data

Fragmented data remains one of the biggest hurdles for modern marketing teams. Siloed systems and inconsistent customer profiles create friction that makes meaningful personalization nearly impossible. Without a unified foundation, campaigns lose relevance—and engagement declines.

At Activate Summit, Iterable sat down with IPSY and Nextdoor to unpack what it really takes to unify data, activate it effectively, and prepare for an AI-powered future. One takeaway stood out: technology alone isn’t enough. Marketers also need structure, strategy, and cross-functional alignment.

Here’s how IPSY and Nextdoor are turning complexity into clarity.

Customer 360 Data as IPSY’s Marketing Foundation

At IPSY, the path to personalization starts with a unified data environment. Its Customer 360 (C360) project establishes a centralized source of clean, structured data that can be used across departments, particularly by nontechnical teams responsible for campaign execution.

The initiative was born from a combination of bottlenecks and possibilities. Existing workflows required heavy involvement from data enablement teams and prevented marketers from acting quickly. At the same time, there were clear opportunities, such as more granular segmentation and lifetime value optimization, that were just out of reach due to data limitations.

IPSY responded by prioritizing the buildout of C360. Its cross-functional team, which spans CRM, analytics, marketing ops, and external partners, rallied around a shared vision of what’s possible when customer data is accessible and consistent. The first wave of C360-supported campaigns is already live, with the entire CRM team building their testing roadmap around the new system.

From Data Lakes to Knowledge Graphs at Nextdoor

While IPSY focused on creating structure from scratch, Nextdoor is evolving its data strategy to extract deeper insights. Historically, its data lake held vast amounts of information, but like many teams, it faced challenges with organization, quality, and usability.

Now, it’s moving toward a knowledge graph framework — an abstraction layer that transforms raw data into contextual understanding. Instead of simply collecting attributes (like age, location, or interests), Nextdoor is building structured relationships between those attributes to better understand customer intent and behavior.

This shift allows it to go beyond static segmentation and develop intelligent marketing strategies informed by nuanced customer profiles. It’s a foundational step toward enabling AI systems that analyze and truly interpret.

The Four Types of AI Initiatives

Not all AI is created—or implemented—the same. As the landscape evolves, marketers face a growing list of tools, vendors, and capabilities that promise transformation but often lack clarity or cohesion. Without a framework, it’s easy to invest in disconnected solutions that don’t scale.

To bring structure to its AI strategy, Nextdoor developed a practical way to categorize its initiatives—helping teams align, prioritize, and invest with purpose. Here are the four types of AI investments they use to navigate the noise and stay focused on impact:

• Proprietary LLMs: Custom-built models that are resource-intensive and rarely necessary for most companies.
• General AI Tools: Platforms like ChatGPT or LLaMA that provide flexible, out-of-the-box AI capabilities.
• SaaS Platforms Adding AI: Vendors such as Iterable that are actively integrating AI into their roadmaps.
• Spot Solutions: Single-use AI tools (e.g., for content generation) that solve a specific problem but may lack long-term scalability.

By evaluating its AI investments through these categories, Nextdoor avoids fragmentation and ensures tools are chosen with cross-functional scalability in mind.

What Does It Take To Get Started With AI Projects?

AI transformation isn’t just technical — it’s organizational. IPSY and Nextdoor emphasized several key principles for getting started.

1. Align the organization.

Successful implementation begins with executive sponsorship and shared goals across teams. IPSY rallied internal and external stakeholders early to ensure that marketing, analytics, and ops were moving in lockstep with the C360 rollout.

Nextdoor added a horizontal governance layer called AI Ops — positioned above traditional martech, sales, and revops — to prevent tool silos and foster consistency across AI initiatives.

2. Collaborate cross-functionally.

Rather than dictate from the top down, IPSY brought in the marketers responsible for executing campaigns. Together, it defined use cases, key metrics, and functional requirements. This ensured the data project was built with practical activation in mind, not just technical elegance.

3. Define early, refine often.

Both companies agreed that progress beats perfection. They focused on realistic, near-term outcomes while maintaining flexibility to adapt. Campaign KPIs, use case definitions, and tooling priorities were adjusted as needed without losing sight of long-term goals.

4. Communicate the vision to build buy-in.

A consistent communication strategy helped teams understand not only what was changing but also why. By connecting the technical work to campaign impact, IPSY’s leadership secured internal alignment, even when shifts in scope or sequencing were required.

5. Prioritize documentation and project management.

Nextdoor stressed the value of thorough documentation. As teams transition toward AI agent orchestration, clearly defined procFragmented data remains one of the biggest hurdles for modern marketing teams. Siloed systems and inconsistent customer profiles create friction that makes meaningful personalization nearly impossible. Without a unified foundation, campaigns lose relevance—and engagement declines.

Securing Funding and Proving ROI for AI Initiatives

Budgeting for AI remains a challenge—especially when returns aren’t immediate. 

Nextdoor tackled this by starting small: two pilot projects, one focused on growing revenue, the other on reducing costs. This dual-pronged approach delivered measurable outcomes and helped build a compelling business case for broader investment. Their long-term vision is clear—AI shouldn’t just surface insights; it should drive orchestration, managing standard engagement activities so marketers can focus on higher-value strategy and brand stewardship.

IPSY is also taking a value-first approach, using AI to optimize how and when customers are contacted. Their roadmap begins with customer-facing use cases like Send Time Optimization (STO) and channel preference—powered by Iterable’s Brand Affinity™ model. Over time, IPSY envisions AI evolving from a support layer into a true execution engine—automating personalized engagement while giving marketers the space to lead creatively as strategists, curators, and brand stewards.

Building Your Data-Driven Future

Unified data and intelligent activation aren’t side projects—they’re the foundation of modern marketing. Without centralized, clean data and well-documented workflows, AI risks becoming a fragmented experiment instead of a scalable system. 

IPSY and Nextdoor are proving that with composable infrastructure and a clear operational framework, brands can move from concept to AI-powered execution with confidence. Start small, move fast, and align your teams—because in the age of AI, action is the advantage.

Want to learn how to build the kind of flexible, future-ready foundation that makes this possible? Dive into our guide: The Future of MarTech Is Composable: How to Power Customer Engagement With Data