Building Custom Audience Segments from Social Data

Your marketing team just finished a campaign targeting "young professionals interested in sustainability." The results? Mediocre engagement, wasted ad spend, and the nagging suspicion that you were talking to the wrong people entirely.

The problem isn't your message—it's that "young professionals interested in sustainability" isn't really an audience. It's a demographic assumption wrapped in a keyword.

Real audience segmentation from social data looks different. It starts with actual behavior, actual conversations, and actual patterns of engagement—not personas dreamed up in a conference room.

Introduction

Audience segmentation has always been the backbone of effective marketing. But the traditional approach—demographics, surveys, and purchased lists—produces segments that feel more like census categories than communities of real people with shared interests and behaviors.

Social data changes this equation entirely. Every day, millions of people broadcast their interests, opinions, professional identities, and purchasing intentions through their social media activity. This isn't just demographic data with a timestamp. It's a living record of what people actually care about, who influences their thinking, and how they engage with content.

The challenge? Turning this firehose of social signals into actionable audience segments requires both the right methodology and the right tools. In this guide, we'll walk through a practical framework for building custom audience segments using social data—segments based on real behavior rather than assumptions.

Why Traditional Segmentation Falls Short

Traditional audience segmentation typically relies on three pillars: demographics (age, location, income), firmographics (company size, industry, job title), and declared interests (survey responses, preference centers).

Each of these has fundamental limitations.

The Declaration Problem

When you ask someone about their interests, you get their idealized self-image. People say they read The Economist, but their browser history tells a different story. Survey data captures aspiration, not behavior.

Social data captures what people actually talk about, share, and engage with—unprompted and unfiltered.

The Snapshot Problem

Traditional segments are frozen in time. Your CRM knows someone downloaded a whitepaper about cloud security in 2023. It doesn't know they've spent the last six months posting about AI governance and now consider themselves primarily an AI ethics advocate.

Social behavior updates in real-time. Interests shift, professional focus evolves, and new communities form around emerging topics.

The Isolation Problem

Demographics tell you nothing about who influences a person's decisions or what communities they belong to. A 35-year-old product manager in San Francisco could be part of dozens of different professional and interest communities—and which ones matter to your message depends on context you simply don't have.

Social network data reveals these connections directly.

The Behavioral Segmentation Framework

Effective audience segmentation from social data works across four dimensions: what people say, what they share, who they follow, and how they engage. Let's break each down.

Dimension 1: Content Creation Patterns

What topics does someone consistently create content about? This is stronger signal than engagement alone because creation requires effort. Someone who regularly posts about supply chain optimization isn't casually interested—they're invested in that topic as part of their professional identity.

Look for:

• Recurring topics and keywords in original posts
• The language and terminology they use (expert vs. novice vocabulary)
• How frequently they create content (occasional vs. consistent)
• The ratio of original content to shares

Dimension 2: Engagement Fingerprints

Not everyone creates content, but everyone engages. Engagement patterns—what posts someone likes, comments on, retweets, or quotes—reveal interests that don't make it into their bio.

The key insight: engagement is contextual. Someone might engage with content about machine learning and venture capital and climate tech. The intersection of these engagement patterns defines their unique position in the landscape of interests.

Dimension 3: Network Topology

Who someone follows—and who follows them—places them within a social graph that reveals community membership. Following ten prominent AI researchers puts you in a different segment than following ten cryptocurrency traders, even if your bio mentions "technology enthusiast" in both cases.

Beyond simple follower analysis, look at:

• Mutual follow relationships (stronger ties)
• Following clusters (groups of related accounts)
• Bridge positions (connecting otherwise separate communities)

Dimension 4: Interaction Quality

A like is cheap. A thoughtful reply is expensive. The quality of engagement matters as much as the quantity.

Users who consistently leave substantive comments on posts about enterprise software represent a different segment than those who occasionally like the same content. Depth of engagement correlates with genuine interest and potential influence within that topic area.

Building Segments: A Practical Workflow

With the framework established, here's how to actually build custom audience segments from social data.

Step 1: Define Your Segmentation Hypothesis

Start with a question, not a conclusion. Instead of "Find me young professionals interested in sustainability," ask "Who is actively discussing corporate sustainability initiatives, and what other topics do they care about?"

Good segmentation questions are:

• Open to unexpected answers
• Focused on behavior, not demographics
• Specific enough to be actionable

Step 2: Identify Seed Conversations

Find the conversations where your target audience naturally participates. This means searching for keywords and phrases that indicate genuine interest, not just casual mentions.

For instance, searching for users discussing "carbon footprint reporting" or "ESG compliance" yields a more qualified audience than searching for "sustainability"—the specificity filters for genuine engagement rather than virtue signaling.

Step 3: Map the Participants

For each conversation, identify the participants and gather their broader profiles. You're looking for patterns across multiple data points:

• What else do they post about?
• Who do they follow?
• What's their engagement pattern?
• What communities do they bridge?

Step 4: Cluster by Behavior

Look for natural groupings. You might find that "corporate sustainability" participants cluster into distinct segments:

• Compliance-focused: Heavy engagement with regulatory content, follows government agencies and audit firms
• Innovation-focused: Engages with cleantech startups, follows VCs and founders
• Advocacy-focused: Shares activist content, connected to NGO networks
• Operations-focused: Discusses supply chain, procurement, concrete implementation

Each of these represents a meaningfully different audience segment, even though they all showed up in the same initial keyword search.

Step 5: Validate and Refine

Test your segments against reality. Do the people clustered together actually behave similarly? Are there outliers that suggest a segment needs splitting or merging?

The best segmentation is iterative. Your first pass won't be perfect, but it will be better than demographic assumptions.

How Xpoz Enables This Workflow

This kind of behavioral audience segmentation requires access to social data at scale—not just API access, but the ability to search across content, analyze networks, and aggregate engagement patterns efficiently.

Xpoz provides the infrastructure for each step of this workflow through its social intelligence tools.

Finding Seed Conversations

The getTwitterUsersByKeywords and getInstagramUsersByKeywords tools let you find users who have actually created content matching your target topics—not just accounts with keywords in their bio, but people who actively discuss these subjects.

For example, to find users discussing corporate sustainability:

Query: "carbon footprint reporting" OR "ESG compliance" OR "sustainability metrics"
Fields: username, name, followersCount, relevantTweetsCount, relevantTweetsLikesSum

The aggregation fields (relevantTweetsCount, relevantTweetsLikesSum) help you identify not just who's talking, but who's generating engagement when they do—a proxy for influence within that topic area.

Mapping Networks

Once you've identified seed users, getTwitterUserConnections and getInstagramUserConnections reveal their network topology. Who do they follow? Who follows them?

This data exposes community membership that isn't visible from content alone. A sustainability professional who primarily follows supply chain experts represents a different segment than one who follows climate activists, even if their content looks similar.

Analyzing Engagement Patterns

The getTwitterPostInteractingUsers tool flips the analysis—instead of starting with users, you start with high-engagement posts in your target topic and see who engaged with them.

This surfaces passive members of your target audience: people who consistently engage with relevant content but don't create much themselves. These audience members are often invisible to content-based segmentation but may be equally valuable.

Scaling the Analysis

Xpoz's pagination and CSV export capabilities make this analysis practical at scale. You're not limited to API rate limits or manual data collection. The dataDumpExportOperationId returned by most tools lets you export complete datasets for offline analysis, clustering algorithms, or visualization.

Practical Examples

Example 1: B2B SaaS Audience Segmentation

A developer tools company wants to find their ideal customer profile within the broader "DevOps" conversation.

Step 1: Search for users posting about specific DevOps pain points:

Query: "CI/CD pipeline" AND (slow OR bottleneck OR frustrating)

Step 2: Analyze the network connections of high-engagement participants. Three distinct clusters emerge:

• Platform engineering leaders (follow infrastructure thought leaders, discuss architecture)
• Individual contributors (follow tutorial accounts, discuss specific tools)
• Consultants (follow multiple vendor accounts, share broadly)

Step 3: For the platform engineering segment, analyze what non-DevOps topics they engage with. Discovery: heavy overlap with "developer experience" and "engineering productivity" conversations.

Result: A qualified segment defined not just by job title but by demonstrated interest in engineering productivity outcomes.

Example 2: Consumer Brand Community Mapping

A sustainable fashion brand wants to understand their Instagram audience beyond basic demographics.

Step 1: Use getInstagramPostInteractingUsers on their highest-engagement posts to identify their most active audience members.

Step 2: Analyze these users' content creation patterns with getInstagramPostsByUser. What else do they post about?

Step 3: Clusters emerge:

• Fashion-first (primarily outfit content, follows influencers)
• Values-first (sustainability content, follows activists and NGOs)
• Lifestyle-integrated (mixes fashion with wellness, parenting, home content)

Result: Three distinct audience segments requiring different messaging approaches, identified through actual behavior rather than survey responses.

Example 3: Thought Leadership Audience Building

A fintech founder wants to build an audience for content about embedded finance.

Step 1: Find users already engaging with embedded finance content:

Query: "embedded finance" OR "banking as a service" OR "BaaS"
Fields: username, followersCount, relevantTweetsCount

Step 2: Analyze who these users follow to understand the existing thought leader landscape.

Step 3: Identify gaps—topics these users care about that current thought leaders don't address.

Result: A content strategy targeting an underserved segment of an existing audience, based on actual behavioral data.

Key Takeaways

• Behavior beats demographics: What people do on social media reveals more than what they claim about themselves
• Segments are intersections: Real audience segments exist at the intersection of multiple behavioral signals—content topics, engagement patterns, and network membership
• Iteration is essential: Your first segmentation hypothesis will be wrong; build a workflow that lets you refine quickly
• Scale matters: Manual analysis works for small samples, but meaningful segmentation requires data infrastructure that can handle thousands of profiles and posts
• Networks reveal community: Following patterns expose community membership that content analysis alone misses

Conclusion

Building custom audience segments from social data isn't about finding a better way to slice demographics. It's about shifting from assumed characteristics to observed behavior—from "who we think they are" to "what they actually do."

This shift requires both a methodological framework (the four dimensions of content, engagement, network, and interaction quality) and tools capable of extracting and analyzing social data at scale.

The result is segments that actually predict behavior: who will engage with your content, who will convert, who will advocate. Not because they match a demographic profile, but because they've already demonstrated the interests and engagement patterns that matter.

Start with a question about behavior, follow the data to natural clusters, and let the segments reveal themselves. The audience you discover will be more valuable than any you could have imagined in a planning meeting.