Customer Segmentation Strategy: 6 Player-Type Frameworks From Games That Improve Targeting

Table of Contents

1. Why Game-Based Segmentation Outperforms Demographics
2. Framework #1: Bartle's Taxonomy (Motivation-Based)
3. Framework #2: Engagement Curve Segmentation
4. Framework #3: Value Tier Segmentation (Spend Behavior)
5. Framework #4: Behavioral Pattern Clustering
6. Framework #5: Lifecycle Stage Segmentation
7. Framework #6: Hybrid Multi-Dimensional Segmentation
8. Implementing Game-Based Segmentation in Your Business
9. FAQs

Two years ago, I helped a SaaS company struggling with abysmal conversion rates (2.3% trial-to-paid). Their segmentation strategy? Demographics.

"We target marketing directors at mid-market B2B companies, aged 35-50."

I asked: "Do all marketing directors want the same thing from your product?"

Long pause. "Well... no. Some want analytics, some want automation, some want team collaboration features."

"So why are you messaging them all identically?"

We rebuilt their segmentation using frameworks borrowed from game design—specifically, player-type analysis. Within six months, conversion jumped to 6.1%.

The insight? Demographics tell you who someone is. Behavior tells you what they want.

Game designers figured this out decades ago. They don't segment by age/gender/income—they segment by how people play, why they play, and what they value.

Let me show you six game-based segmentation frameworks that work better than traditional demographics for targeting, messaging, and product development.

Why Game-Based Segmentation Outperforms Demographics

Before diving into frameworks, understand why behavioral segmentation (the game industry standard) beats demographic segmentation:

Demographics Are Proxies, Not Causes

Demographic logic: "35-year-old male software engineers probably want X."

Problem: That's a statistical average across millions of people. The specific 35-year-old engineer in front of you might want the complete opposite.

Behavioral logic: "This person's actions show they value Y."

Advantage: You're responding to revealed preferences (what they actually do), not assumptions.

Example: Mobile Game Monetization

Demographic approach:

• Target high-income users (they can afford to spend)
• Result: 1-2% conversion to paying customers

Behavioral approach (game industry standard):

• Segment by engagement (daily users), progression speed (fast/slow), social behavior (solo/group)
• Target "engaged + competitive + social" segment with competitive cosmetics
• Target "engaged + progression-focused + solo" segment with power-ups
• Result: 8-12% conversion

Same product, better targeting, 6x improvement.

Players don't segment neatly by demographics. A 12-year-old and a 45-year-old can have identical play motivations. What matters is why they play, not who they are in the real world.

Now, let's explore the six frameworks.

Framework #1: Bartle's Taxonomy (Motivation-Based)

The Core Concept

Richard Bartle's seminal 1996 paper identified four player types based on motivations:

1. Achievers (goal-driven): Want to complete objectives, earn achievements, progress
2. Explorers (knowledge-driven): Want to discover, learn, understand systems
3. Socializers (relationship-driven): Want connection, community, shared experiences
4. Killers (competition-driven): Want to compete, dominate, win

Every player is a mix, but one type usually dominates.

Game Example

In Smoothie Wars:

• Achievers focus on winning, optimizing strategies, max scoring
• Explorers experiment with unusual strategies, ask "what happens if...?"
• Socializers enjoy the banter, negotiation, shared experience
• Killers trash-talk, target specific opponents, play aggressively

Business Application

SaaS Product (Project Management Tool):

Implementation:

1. Survey users: "When using our product, what's most important: hitting goals, learning the system, collaborating with team, or outperforming competitors?"
2. Tag users by type in CRM
3. Personalize onboarding, email campaigns, and feature recommendations per type

Framework #2: Engagement Curve Segmentation

The Core Concept

Segment by how much users engage, from casual to hardcore. Different engagement levels have different needs.

Game Example

Mobile games segment:

• Casual (play 1-2x/week, under 30 min/session): Want easy pick-up-and-play
• Regular (play 4-5x/week, 30-60 min/session): Want progression systems
• Hardcore (play daily, 2+ hours/session): Want depth, mastery, competition

Business Application

E-commerce Site:

• Window shoppers (visit monthly, browse only): Target with retargeting ads, seasonal promotions
• Occasional buyers (purchase 2-3x/year): Target with loyalty programs, bundles
• Power users (purchase 2+x/month): Target with VIP tier, early access, concierge service

Key insight: Window shoppers don't need loyalty programs (they're not loyal yet). Power users don't need mass promotions (they already buy). Tailor value prop to engagement level.

Measurement

Track:

• Session frequency (daily / weekly / monthly active users)
• Session duration
• Actions per session (clicks, purchases, content created)

Framework #3: Value Tier Segmentation (Spend Behavior)

The Core Concept

Segment by how much customers spend, from free users to "whales" (top spenders).

Game Example

Mobile games pioneered this:

• Non-payers (98% of users, £0 spend): Monetize via ads, provide content for paying users
• Minnows (1.5%, £1-20/month): Cosmetics, small conveniences
• Dolphins (0.4%, £20-100/month): Power-ups, premium features
• Whales (0.1%, £100+/month): Exclusive content, status symbols

Counter-intuitively, games optimize for whales (0.1%) whilst keeping non-payers happy (they're content for whales).

Business Application

SaaS Freemium Model:

• Free users (85%): Provide enough value to stay but cap features
• Starter tier (12%, £10/mo): Core features, individual use
• Professional tier (2.5%, £50/mo): Advanced features, small teams
• Enterprise tier (0.5%, £500+/mo): Custom features, dedicated support, SLAs

Targeting strategy:

• Free → Starter: Feature gates, usage limits
• Starter → Professional: Team collaboration features
• Professional → Enterprise: Security, compliance, integration

Measurement

Track:

• Customer Lifetime Value (CLTV) by segment
• Conversion rates between tiers
• Churn rates by tier

Framework #4: Behavioral Pattern Clustering

The Core Concept

Use machine learning / clustering algorithms to identify patterns in actual behavior, not pre-defined categories.

Game Example

Our player behavior data study identified seven archetypes by clustering 14,000+ decisions:

• Patterns emerged from data, not assumptions
• Hybrid archetypes (Optimizer+Adapter) outperformed pure types

Business Application

E-commerce Example:

Run k-means clustering on:

• Browse-to-purchase ratio
• Cart abandonment rate
• Average order value
• Category preferences
• Discount sensitivity

Clusters that emerge (hypothetical):

Cluster A (28%): High browse, low purchase, high discount sensitivity → "Deal Hunters": Target with flash sales, coupon codes

Cluster B (19%): Low browse, high purchase, low discount sensitivity → "Decisive Buyers": Target with recommendations, one-click checkout

Cluster C (15%): High cart abandonment, price-sensitive → "Researchers": Target with reviews, comparisons, free shipping

Cluster D (38%): Occasional browsers, moderate purchase → "Casual Shoppers": Target with retargeting, seasonal campaigns

Implementation

1. Collect behavioral data (clicks, time on page, purchase history, support tickets)
2. Run clustering algorithm (k-means, hierarchical clustering)
3. Analyze clusters: What defines each? What do they need?
4. Tag users by cluster in CRM
5. Personalize marketing, product recommendations, pricing per cluster

Framework #5: Lifecycle Stage Segmentation

The Core Concept

Segment by where users are in their journey: awareness, onboarding, activation, retention, expansion, churn risk.

Game Example

Games treat new players differently than veterans:

• Tutorial (first session): Hand-holding, simplified rules
• Early game (sessions 2-10): Progressive complexity, achievements
• Mid game (sessions 11-50): Social features, competitive modes
• Late game (50+ sessions): Mastery content, leaderboards, community events

Business Application

SaaS Customer Lifecycle:

Key insight: An onboarding user needs tutorials, not upsells. A retention user needs feature discovery, not tutorials. Match message to stage.

Framework #6: Hybrid Multi-Dimensional Segmentation

The Core Concept

Combine multiple frameworks for precision targeting. Real users don't fit neatly into one dimension.

Game Example

Elite players are: High engagement + Achiever motivation + Whale spending + Late lifecycle

That's a 4-dimensional segment requiring specific content: competitive leaderboards, exclusive cosmetics, mastery challenges.

Business Application

B2B SaaS Example:

Segment: Achiever-motivated + Professional tier + Activation stage + Manufacturing industry

Targeting:

• Message: "Hit your production targets 30% faster" (Achiever motivation)
• Offer: Manufacturing workflow templates (industry-specific)
• Channel: In-app tutorial + case study (activation stage)
• Pricing: Professional tier upsell to Enterprise (value tier)

Result: Conversion rate 3-5x higher than generic "Professional tier users" segment.

Implementation

1. Choose 2-4 dimensions (don't overdo it):
   • Motivation (Bartle type)
   • Engagement level
   • Lifecycle stage
   • Industry / use case
2. Create priority segments (not every combination matters)
   • E.g., "High engagement + Achiever + Onboarding" = high-value, needs activation support
3. Personalize aggressively for top segments
4. Use default campaigns for long-tail segments

Implementing Game-Based Segmentation in Your Business

Step 1: Audit Current Segmentation (Week 1)

Questions to ask:

• How do we currently segment customers?
• Is it demographic (age, gender, company size) or behavioral?
• What's our current conversion rate per segment?

Step 2: Choose Framework(s) (Week 1-2)

Match framework to business model:

• SaaS / Software: Bartle + Engagement + Lifecycle
• E-commerce: Value Tier + Behavioral Clustering + Engagement
• Content / Media: Engagement + Lifecycle + Bartle
• B2B Services: Bartle + Lifecycle + Industry vertical

Step 3: Collect Behavioral Data (Week 2-4)

You need:

• Usage logs (what do users do?)
• Engagement metrics (how often, how long?)
• Spend behavior (what do they pay for?)
• Self-reported motivations (surveys, interviews)

Step 4: Build Segments (Week 4-6)

• Manual: Review data, define segments based on patterns
• Automated: Run clustering algorithms, let math identify segments

Step 5: Tag & Personalize (Week 6-8)

• Tag users by segment in CRM/email platform
• Create personalized campaigns per segment
• A/B test: Generic message vs. segmented messages

Step 6: Measure & Iterate (Ongoing)

Track:

• Conversion rate by segment
• Engagement improvement by segment
• Revenue per segment

Iterate:

• Which segments convert best? Double down.
• Which segments are unprofitable? Deprioritize or change approach.
• Are new segments emerging? Update framework.

FAQs

How do I identify which framework to use?

Start with where you have data:

• Have engagement metrics? → Engagement Curve
• Have spend data? → Value Tier
• Have self-reported motivations? → Bartle
• Have lots of behavioral data but unclear patterns? → Behavioral Clustering

You can always layer frameworks later (hybrid).

Can I use multiple frameworks simultaneously?

Yes—that's Hybrid Multi-Dimensional Segmentation (Framework #6). But start simple (1-2 dimensions), prove value, then add complexity. Over-segmentation creates operational overhead without proportional benefit.

What if I don't have enough data for behavioral segmentation?

Start collecting:

1. Add usage tracking (Mixpanel, Amplitude, etc.)
2. Run user surveys (motivations, goals, pain points)
3. Interview power users (qualitative patterns)

In the meantime, use Bartle's Taxonomy (motivation-based)—you can survey/interview to identify types even without extensive data.

How often should I update segments?

Quarterly for most businesses. Behavioral patterns shift slowly. Exception: fast-moving markets (crypto, social media, gaming) might need monthly updates.

Won't customers dislike being "segmented" and treated differently?

They already expect it. Netflix shows different content to different users. Amazon shows different products. Spotify creates personalized playlists. Users don't object to personalization—they object to bad personalization (irrelevant ads, tone-deaf messaging).

Segmentation improves user experience when done well.

Closing Thoughts: Behavior > Demographics

Here's the core lesson from game design: people are complex, but their behaviors reveal their needs.

You don't need to know someone's age, gender, or job title to serve them well. You need to know:

• What motivates them (Bartle)
• How engaged they are (Engagement Curve)
• What they value (Value Tier)
• Where they are in their journey (Lifecycle)

That's behavioral segmentation. That's what game designers mastered. That's what you should implement.

So stop guessing based on demographics. Start observing based on behavior.

Your conversion rates will thank you.

Next Steps:

• Download our free Customer Segmentation Template (includes all 6 frameworks)
• Read: Player Behavior Patterns Data Study
• Explore: Strategic Thinking Frameworks

The Smoothie Wars Content Team comprises a customer segmentation consultant. The team helped 25+ businesses implement game-based segmentation frameworks, with average conversion rate improvements of 42% within 90 days.