Audience
Segmentation
Insights Dashboard
Segmentation
Insights Dashboard
Who fills the venues — and where are they? A 10-segment audience analysis across ticket sales, revenue share, and geographic concentration that directly informed a targeted campaign strategy delivering a 5x return on ad spend.
🔒 Anonymized Mockup
This is a mockup version built on dummy data to represent an analysis I completed professionally. The original work informed a real campaign strategy — the methodology, segment taxonomy, and analytical approach shown here mirror what was built, with all client and proprietary data replaced with synthetic equivalents.
Power BI
Audience Segmentation
Marketing Attribution
Geographic Analysis
Campaign Strategy
Ticket Sales Analytics
Revenue Optimization
The Problem
Who's filling the venue
and where are they?
Collegiate sports properties generate ticket sales from dramatically different buyer types — from devoted season-ticket loyalists to last-minute casual buyers. Without a way to distinguish between them, every marketing dollar gets distributed the same way to everyone.
The challenge wasn't just identifying segments — it was understanding their relative contribution to revenue versus tickets, their geographic concentration, and how those patterns varied market by market. A segment that over-indexes in one region might be nearly absent in another.
Most reporting answered “how many tickets did we sell?” This analysis was built to answer: who bought them, where did those people come from, and which segments are driving the most value per transaction?
The output wasn't just a dashboard — it was the foundation for a targeted campaign that achieved a 5x return on ad spend by focusing creative and media on the highest-value segments in their highest-concentration markets.
Gap 01 · Undifferentiated Spend
Campaign budgets were being distributed evenly across audiences with no view into which segments generated the highest revenue per ticket or responded best to targeted creative.
Gap 02 · No Geographic Context
Ticket sales data existed at the transaction level but wasn't mapped to segment-level geographic concentration — making regional media buys impossible to optimize.
Gap 03 · Revenue vs Ticket Mismatch
High-volume segments weren't always high-revenue segments. Without separating ticket share from revenue share, the highest-value buyers were hidden in aggregate numbers.
Gap 04 · Market Variation Invisible
Segment mix varied significantly by market, but without a cross-market view, properties couldn't identify where specific segments over- or under-indexed relative to expectation.
My Role
From purchase data
to campaign strategy
🔬
What I Analyzed & Built
- Defined the 10-segment audience taxonomy — mapped behavioral and purchase patterns to named archetypes that were meaningful to both marketing and operations teams
- Analyzed purchase data across segments — calculated ticket share and revenue share separately to surface the delta between volume buyers and value buyers
- Built geographic concentration analysis — identified top states per segment to enable precise regional media targeting
- Created segment mix by market view — surfaced over- and under-indexing segments across regions, giving properties actionable insight into where each audience type concentrates
- Built the Power BI dashboard — designed for marketing and strategy stakeholders, not data teams; optimized for fast interpretation and campaign decision support
📋
Scope & Context
- Professional project — this analysis was completed as part of my work in collegiate sports marketing at Learfield; this version uses anonymized dummy data to protect client and campaign details
- Directly actionable — the output wasn't a research artifact; it was immediately handed to campaign managers as the targeting brief for creative and media deployment
- Built for non-technical stakeholders — every view in the dashboard was designed so a marketing director could make a decision in under 60 seconds without SQL access
- Geography was the unlock — the segment taxonomy alone was useful; knowing that The Loyalist concentrates in specific states and The Digital Native in others was what turned insight into action
- 5x ROAS outcome — the campaign informed by this analysis delivered a 5x return on ad spend by focusing on the highest-value segments in their highest-density markets
The Approach
From purchase data
to targeting brief
Five stages — from raw transactional data to a campaign-ready audience map.
01
Segment Design
Defined 10 audience archetypes based on purchase behavior, timing, frequency, and spending patterns.
02
Data Analysis
Calculated ticket share and revenue share separately per segment — surfacing the delta between volume buyers and value buyers.
03
Geo Mapping
Identified top states for each segment, mapping purchase data to geographic concentration to enable regional media targeting.
04
Market Mix View
Built a cross-market comparison to spot segments that over- or under-index by region, surfacing market-specific opportunity gaps.
05
Dashboard Build
Designed a Power BI dashboard for marketing decision-makers — fast to read, built around campaign decisions, not raw data exploration.
Audience Segments
Ten archetypes.
One complete picture.
Each segment was defined around a distinct behavioral profile — how they buy, when they buy, how much they spend, and what motivates them to show up. Naming the segments made them real to stakeholders in a way that raw cohort IDs never could.
The Loyalist
Season-ticket holder behavior. High frequency, high spend, geographically stable. The anchor segment for renewal strategy.
The Digital Native
Mobile-first buyer. Responds to digital creative, social proof, and last-minute deals. High acquisition potential via paid social.
The Family Planner
Plans ahead, buys in groups, values experience over price. Strong ticket volume, moderate revenue per head.
The Trend Seeker
Attracted to marquee matchups and cultural moments. Sporadic attendance but high spend when engaged.
The Business Casual
Corporate or client entertainment buyer. High per-ticket spend, premium seat preference, low frequency.
The Casual Listener
Infrequent buyer. Low engagement, price-sensitive. High volume segment with limited revenue contribution.
The Superfan
Deep team or athlete affinity. Drives merchandise and premium upsell. Most responsive to identity-based creative.
The Social Scene
Event-as-outing buyer. Group purchases, experience-led, social sharing behavior. Strong for mid-week and secondary events.
The Last-Minute Buyer
Day-of or same-week purchaser. Price responsive, mobile purchase pattern. High volume in sellout conditions.
The Local Explorer
Proximity-driven buyer. Strong in college towns and regional markets. Responds to local identity and community messaging.
Segment names and descriptions shown here are representative of the methodology — specific behavioral thresholds and scoring logic from the original analysis are not disclosed.
The Output
Revenue vs. tickets.
Where the delta lives.
A Power BI dashboard built for marketing decision-makers — each view answers one specific question a campaign manager actually asks before allocating budget.
Revenue & Ticket Share by Segment — the primary view. Separating ticket volume from revenue contribution reveals which segments are high-value buyers versus high-frequency but low-spend audiences — a distinction that changes where you put media dollars.
Power BI10 SegmentsAnonymized Data
Revenue Detail by Segment
Drill-down view — revenue contribution per segment with comparative context
Ticket Sales Breakdown
Ticket volume by segment — showing where volume and value diverge
Geographic & Market Mix
Top states per segment · over/under-indexing by market
Segment Mix by Market
Additional view not shown
Segment Mix by Market
Cross-market segment comparison · spotting regional over/under-indexing
Results
The stat sheet
5x
Return on Ad Spend
The campaign informed by this analysis delivered a 5x ROAS by concentrating spend on the highest-value segments in their highest-density markets.
10 seg
Audience Archetypes
Ten distinct behavioral segments mapped from purchase data — each with a named identity, revenue profile, and geographic signature.
3 views
Dashboard Dimensions
Revenue & ticket share by segment, top states by segment, and segment mix by market — each answering a different campaign question.
≥1 mkt
Market Variation Surfaced
Segment concentrations varied meaningfully across markets — making geographic targeting the key lever the analysis unlocked.
Analyst Lens
Key decisions & tradeoffs
The choices that shaped the analysis — especially the ones where the “richer” option was deliberately not the right one.
| Decision | Rationale | Tradeoff |
|---|---|---|
| Named segments over cohort IDs | Labeling segments with human names (The Loyalist, The Digital Native) rather than numeric cohorts made the taxonomy immediately usable by marketing teams who had no analytical background. Stakeholders could talk about segments in creative briefs and media plans without needing to reference a data dictionary. | Less technically precise — named archetypes carry implicit assumptions that raw cohort IDs don't. The tradeoff was deliberate: a segment that drives action is worth more than one that's methodologically pristine but unused. |
| Ticket share vs. revenue share separated | Keeping ticket volume and revenue contribution as distinct metrics was the analytical unlock. Many segments had inverted profiles — high ticket share but low revenue share (The Casual Listener) or low ticket share but high revenue share (The Business Casual). Blending them into a single metric would have obscured the delta entirely. | Cleaner campaign logic — separating the two allowed media budgets to target value segments rather than just volume segments, which directly contributed to the 5x ROAS outcome. |
| Geography as the primary action lever | The segment taxonomy alone told you who to target. The geographic concentration view told you where. Combining them was what turned the analysis from interesting to actionable — a media buyer could translate it directly into a regional paid social or programmatic strategy. | Immediately deployable — no additional data transformation required between dashboard and media plan. The geographic output was the targeting brief. |
| Designed for decision speed, not exploration | The dashboard was built for a marketing director making a budget call, not an analyst doing exploratory research. Every view answers one specific question and surfaces the answer in the top half of the screen. Filtering and drill-down were secondary to immediate readability. | Limited self-serve depth — the dashboard doesn't support ad hoc slicing by an analyst user. A deeper exploration layer was deliberately scoped out in favor of decision-speed for the primary audience. |
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