# 6 Nevent segmentation categories - complete reference

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:::tip[Quick definition]
**What criterion categories exist in Nevent?**

6 main categories: (1) Attendance (past events, dates), (2) Attributes (age, gender, location), (3) Spend (expenditure, ticket type), (4) Engagement (opens, clicks, temperature), (5) Channels (email, SMS, web), (6) Nevent Score (automatic RFM).

**Example:** Combining Attendance ("Primavera Sound 2023") + Spend ("Spend ≥€300") identifies 4,200 loyal fans out of an 80k base.
:::

## Frequently Asked Questions about Segmentation Criteria

### What criteria can I use to sell festival tickets?

Nevent offers **6 main criterion categories** for segmenting attendees and ticket buyers:

1. **Attendance (Past events)** — Filter by purchase history
   - Specific events attended (e.g. "Primavera Sound 2023")
   - Attendance dates
   - Historical ticket type purchased (General, VIP, Early Bird)

2. **Attributes (Demographic data)** — Personal information
   - Age, gender
   - Location: country, region, city, km radius
   - Preferred language

3. **Spend (Purchase behaviour)** — Transactional metrics
   - Total historical spend (€)
   - Number of purchases
   - Average ticket value
   - Preferred payment method

4. **Engagement (Interaction)** — Activity with communications
   - Emails opened/clicks
   - Web pages visited
   - Nevent Temperature (0-100)
   - Last interaction

5. **Channels** — Origin and communication preferences
   - Acquisition channel (email, social, organic)
   - SMS, WhatsApp, push notification opt-in
   - Preferred device (mobile, desktop)

6. **Nevent Score (automatic RFM)** — Intelligent segments
   - Champions, Loyal, Promising, At Risk, Lost
   - Calculated automatically by Recency, Frequency, Monetary

**Most used combination:** Attendance + Spend + Engagement (70% of successful segments)

**Practical example:**
```
Criteria for early bird VIP:
- Past events: ≥2 (Attendance)
- Historical spend: ≥€500 (Spend)
- Temperature: ≥Hot (Engagement)
```

---

### How do I segment VIPs by spend?

Define VIPs by total historical spend with thresholds that vary by event type:

**Recommended VIP thresholds:**

| VIP Level | Total Spend | Nevent Criterion | Average Ticket | Typical Events |
|-----------|-------------|-----------------|----------------|----------------|
| **VIP Bronze** | €300-800 | `Total historical spend: ≥300, <800` | €100-150 | 2-5 events |
| **VIP Silver** | €800-2,000 | `Total historical spend: ≥800, <2000` | €150-250 | 4-8 events |
| **VIP Gold** | €2,000-5,000 | `Total historical spend: ≥2000, <5000` | €250-400 | 8-15 events |
| **VIP Platinum** | €5,000+ | `Total historical spend: ≥5000` | €400+ | 15+ events |

**Adjust based on your average ticket:**
- Budget festival (ticket ~€50): VIP = ≥€200 historical
- Mid-range festival (ticket ~€150): VIP = ≥€500 historical
- Premium festival (ticket ~€300): VIP = ≥€1,000 historical

**Critical tip — Combine with recency:**

Avoid including "dormant VIPs" who spent a lot years ago but are now inactive:

```
Active VIP segment:
Total spend: ≥€800
AND Days since last purchase: ≤365
```

This excludes VIPs who spent €1,500 in 2020-2021 but have not purchased since (probably churned).

**Performance by VIP tier:**

| Tier | % Audience | Open Rate | Conversion | Revenue/Fan |
|------|------------|-----------|------------|-------------|
| VIP Platinum | 0.5-2% | 65-75% | 18-28% | €300-600 |
| VIP Gold | 2-5% | 55-65% | 12-20% | €200-400 |
| VIP Silver | 5-10% | 45-55% | 8-15% | €100-250 |
| VIP Bronze | 10-15% | 35-45% | 5-10% | €50-150 |

**Differentiated action by tier:**
- **Platinum:** No discounts. Early access 48h before, meet & greet, backstage
- **Gold:** Maximum 10% discount, early access 24h before, VIP lounge
- **Silver:** 15% discount, exclusive content, priority support
- **Bronze:** 20% discount, personalised emails, upsell to Silver

---

### Can I filter by geographic location?

**Yes, 3 levels of granularity:**

**Level 1 — Country:**
```
Country: Spain
```
- Use case: National vs international festivals
- Example: Sónar (60% Spain, 40% international)

**Level 2 — Region/Province:**
```
Region: Catalonia
```
- Use case: Regional festivals, cross-sell nearby events
- Example: Cruïlla Barcelona → Fans from Catalonia receive public transport info

**Level 3 — City + Radius:**
```
City: Barcelona
Radius: ≤50km
```
- Use case: Ultra-specific geo-targeting, removing accommodation info for locals
- Example: Primavera Sound → Barcelona fans do not see the hotel section in emails

**Geo-segmentation use cases:**

**A. Local festival — Remove irrelevant information**
```
Locals segment:
City: Barcelona, ≤50km
```

**Personalised email:**
- ❌ Hotel section (they live there)
- ❌ Flight information
- ✅ Public transport (Metro L4, Bus H16)
- ✅ Venue car park (€20/day)
- ✅ Subject: "🏠 Neighbour early bird — From home to the festival"

**Performance:** Open rate +18% vs generic email (no hotel information noise)

---

**B. Festival with tourism — Hotel + ticket packages**
```
Tourists segment:
Country: NOT Spain
OR Region: NOT Catalonia (Spaniards outside Catalonia)
```

**Personalised email:**
- ✅ 5 recommended hotels near venue
- ✅ Barcelona flights comparison widget
- ✅ 48h tourist guide (Sagrada Família, Park Güell)
- ✅ Free shuttle bus hotel→venue
- ✅ Subject: "✈️ Hotel + ticket packs Barcelona from €280"

**Performance:** Full package conversion 9.2% vs 2.1% single ticket (high value)

---

**C. Multi-city tour — Personalise venue**
```
Madrid segment:
City: Madrid, ≤150km

Barcelona segment:
City: Barcelona, ≤150km

Valencia segment:
City: Valencia, ≤150km
```

**Email with dynamic content:**
- Hero image: Specific venue in THEIR city
- Google Maps: How to get there from the city centre
- Schedule: Specific date for THEIR city
- Cross-sell: "Want to see all 3 dates? Pack -20%"

**Performance:** Open rate 68% (ultra relevant: their city + their band)

---

**Geo-targeting metrics (Sónar Barcelona case):**

| Segment | Audience | Email Focus | Open Rate | Conversion | Revenue |
|---------|----------|-------------|-----------|------------|---------|
| **Catalonia locals** | 57,000 | Public transport | 35% | 6.8% | €562k |
| **Nationals outside Cat** | 18,000 | AVE train Barcelona | 31% | 8.5% | €275k |
| **International** | 20,000 | Hotel + flight packs | 29% | 12.1% | €691k |

**Total geo-segmented revenue:** €1,528k vs €980k mass email (+56%)

---

### How do I find habitual early bird buyers?

**"Early Bird Lovers" segment** identifies fans who ALWAYS buy in phase 1 (first 14-30 days after announcement).

**Criteria (3 AND filters):**
```
Number of past events: ≥3
AND Ticket type history: Early Bird, Pre-sale (≥2 purchases)
AND Average days between announcement and purchase: ≤14
```

**Translation:** Fans who purchased 3+ events, at least 2 were early bird/presale, and typically buy within the first 14 days.

**Typical Early Bird Lover profile:**
- Age: 25-38 years
- Events attended: 3-8
- % early bird purchases: 75-100%
- Average purchase time after announcement: 3-12 days
- Characteristic: They do NOT wait for the full lineup (they trust the promoter)

**Expected size:** 8-15% of total audience (niche but valuable segment)

**Competitive advantage:**

| Metric | Early Bird Lovers | General Audience |
|--------|-------------------|-----------------|
| **Early bird conversion** | 15-25% | 2-4% |
| **Conversion lift** | **5-7x** | Baseline |
| **Average time to purchase** | 5 days | 38 days |
| **Price sensitivity** | Low (buy regardless) | High (wait for discount) |
| **Loyalty** | Very high (NPS 9-10) | Medium (NPS 6-7) |

**Strategic email for Early Bird Lovers:**

**Subject:** "🔐 Exclusive early access — Just for you [Name]"

**Timing:** Send **24-48h BEFORE** public announcement

**Content:**
- "You are part of our VIP early bird group"
- "We are giving you access 48h before anyone else"
- Partial lineup (3-5 confirmed headliners)
- Pre-sale code: EARLYBIRD48 (expires in 48h)
- Additional discount: 5-10% (on top of the already reduced early bird price)
- Countdown timer: 48h until public announcement

**Expected result:**

| Metric | Value |
|--------|-------|
| Early Bird Lovers audience | 4,000 fans (10% of 40k base) |
| Open rate | 62% (2,480 opens) |
| Conversion in 48h pre-sale | 18% (720 tickets) |
| Revenue in 48h pre-sale | €324,000 (price €450) |
| **% of early bird target sold in 48h** | **45-60%** |

**Key insight:** You sell almost half your early birds in 48h with just 10% of your audience (Early Bird Lovers).

**Secondary benefit — FOMO for the rest:**

When you publicly announce the early bird 48h later:
- General subject: "Early Bird now available — 720 of 1,500 already sold!"
- Artificial (but real) urgency: Genuine scarcity
- Effect: +22% conversion in the general audience vs not using pre-sale

**Variation — Gamification:**

Offer an extra benefit to Early Bird Lovers to incentivise immediate purchase:
- "First 100 buyers: Exclusive meet & greet with [Artist]"
- "First 50: Free upgrade to VIP"
- Timer: "X spots remaining out of 100"

**Result:** Acceleration of purchase (8% conversion in the first 6 hours vs 18% in 48h)

---

### What happens if I combine too many criteria?

**Problem:** Over-segmentation = audience too small or too many segments.

**Symptoms of over-segmentation:**

1. **Segment too small (&lt;500 fans):**
   - Cannot reach sales target
   - Not statistically significant for A/B testing
   - Unfavourable effort/benefit ratio

2. **Too many segments (&gt;15):**
   - Operational fatigue (difficult to manage)
   - Overlap between segments (confusion)
   - Message dilution (loss of focus)

**Golden rule: Maximum 3-6 criteria per segment**

**Over-segmentation example (BAD):**
```
Temperature: Very Hot
AND Spend: ≥€800
AND Past events: ≥5
AND City: Barcelona
AND Genre: Electronic
AND Age: 28-35
AND Email opens: ≥10 (last 30 days)
AND Device: Mobile
```

**Result:** 12 fans (ridiculously small audience)

**Solution — Simplify (GOOD):**
```
Temperature: ≥Hot (not just Very Hot)
AND Spend: ≥€500 (lower threshold)
AND City: Barcelona, ≤100km (wider radius)
AND Genre: Electronic
```

**Result:** 2,400 fans (viable audience)

**Optimal balance:**

| Campaign Objective | Recommended Criteria | Target Audience | Example |
|-------------------|---------------------|-----------------|---------|
| **Enhanced mass email** | 1-2 criteria | 30-50% of base | Genre: Electronic |
| **Basic segmentation** | 3-4 criteria | 10-20% of base | Genre + Temperature + City |
| **Advanced segmentation** | 5-6 criteria | 5-10% of base | Genre + Temp + City + Spend + RFM |
| **Ultra niche (VIP)** | 7+ criteria | 1-5% of base | All of the above + specific past events |

**Optimal number of segments per campaign:**

| Campaign Type | Recommended Segments | Reasoning |
|---------------|---------------------|-----------|
| **Early bird** | 3-5 segments | Champions, Loyal, Main genre, Locals, New |
| **Lineup announcement** | 2-3 segments | By musical genre |
| **Last minute** | 2 segments | Engaged vs Inactive |
| **Win-back** | 1 segment | At Risk only |
| **Monthly newsletter** | 1 segment | Active subscribers |

**Test to validate if over-segmented:**

✅ **Segmentation is correct if:**
- Each segment has ≥500 fans
- Conversion of each segment &gt;5%
- You can explain the difference between segments in 1 sentence
- Setup time &lt;2 hours

❌ **Over-segmented if:**
- Any segment &lt;200 fans
- You have &gt;10 segments for 1 campaign
- You are not sure which email to send to each segment
- Setup takes &gt;4 hours

**Solution to over-segmentation:**

1. **Consolidate similar segments:**
   - VIP Gold + VIP Platinum → Premium VIP (if behaviour is similar)
   - Rock + Indie → Guitar-driven music

2. **Use dynamic content instead of separate segments:**
   - 1 "Engaged fans" segment with 3 content variants by genre
   - More efficient than 3 separate segments

3. **Prioritise criteria with the greatest impact:**
   - Temperature + RFM: 70% of the impact on conversion
   - Musical genre: +20%
   - Geo: +10%
   - Others: Marginal

**Pareto principle in segmentation:**
- 20% of criteria generate 80% of the conversion lift
- Focus on the 2-3 most important criteria, the rest is noise

---

# The 6 Segmentation Categories

Segmentation criteria for festivals: Nevent organises all filters into 6 categories designed for promoters. Each category lets you filter attendees and ticket buyers from a different perspective: attendance, demographics, spend, engagement, channels and predictive Nevent Score.

---

## 1. Attendance

Everything related to the events your fans have attended.

### Most Useful Criteria

| Criterion | What It Is | Example Use |
|-----------|-----------|-------------|
| **Event city** | Geographic location of events attended | Fans who went to events in Barcelona |
| **Specific event** | Filter by a specific event | Fans who attended "Primavera Sound 2024" |
| **Ticket type** | VIP, General, Early Bird, etc. | Fans who bought VIP tickets |
| **Number of events attended** | Number of unique events | Fans who have been to 5+ events (super fans) |
| **Days in advance of purchase** | How far in advance they bought | Fans who buy 30+ days in advance |
| **Favourite artist** | Artists they have seen live | Fans who saw "Bad Bunny" |
| **Musical genre** | Rock, Electronic, Pop, etc. | Electronic music fans |
| **Purchase date** | When they purchased | Fans who bought in December 2024 |
| **Event date** | When the event was | Fans who attended events in summer 2024 |
| **Event name** | Search by text in name | Events containing "Festival" |

### Use Cases

#### Example 1: Repeat Buyers at a Specific Festival

**Criterion:** Past events: "Sónar Barcelona" ≥2

**Use case:** Sónar 2026 announcement to loyal fans

**Typical segment:**
- Total base: 80,000 contacts
- Attended 2+ times: 8,500 fans (10.6%)

**Expected performance:**
- Open rate: 48-55% (vs 18% cold audience)
- Conversion: 15-22% (vs 2% cold audience)
- Revenue: €114,750-€168,300 (price €90)

**Best practice:** Send 48h before the public announcement with subject "🌟 Advance VIP access — For loyal fans only"

#### Example 2: Cross-Sell by Musical Genre

**Criterion:** Past events: Category "Electronic" ≥1

**Use case:** Promotion of a new EDM festival to fans with an electronic music history

**Typical segment:**
- Total base: 120,000 contacts
- Attended an electronic event: 28,000 fans (23%)

**Expected performance:**
- Relevance: 85% of the lineup matches preferences
- Conversion: 4-7% (vs 0.8% general audience)
- Cross-sell rate: 1,120-1,960 tickets

**Winning subject line:** "🎧 [New EDM Festival] — 80% of the lineup you love"

**Timing:** 90 days before the event (electronic fans buy earlier than average)

---

## 2. Fan Attributes

Demographic and personal information about your fans.

### Most Useful Criteria

| Criterion | What It Is | Example Use |
|-----------|-----------|-------------|
| **Age** | Fan's current age | Fans between 25-35 years |
| **Gender** | Male, Female, Other | Segment by gender |
| **City of residence** | Where the fan lives | Fans who live in Madrid |
| **Province** | Province of residence | Fans from the Community of Madrid |
| **Country** | Country of residence | International fans (France, Portugal) |
| **Language** | Preferred language | Fans who speak English |
| **Postcode** | Residential postcode | Fans with postcode 28001 |
| **Custom tags** | Tags you create | Fans with "VIP" or "Press" tag |
| **Custom fields** | Custom data you collect | Fan with "Customer Type" = "Business" |

### Use Cases

**Example 1: Young Local Campaign**
```
Criterion: Age
Operator: Between
Value: 18 and 28

AND

Criterion: Province
Operator: Equals
Value: Barcelona
```
**Result**: Young people from Barcelona → University festival offer

#### Example 3: Geo-Targeting Festival with Tourism

**Criterion:** Country: NOT Spain (Sónar Barcelona festival)

**Use case:** Promotion of hotel + ticket packages to international fans

**Typical segment:**
- Total base: 80,000 contacts
- International fans: 32,000 (40%)

**Breakdown by region:**
| Region | Fans | % | Typical Conversion |
|--------|------|---|--------------------|
| France | 8,000 | 10% | 6-9% |
| UK | 6,400 | 8% | 5-8% |
| Germany | 4,800 | 6% | 5-7% |
| USA | 3,200 | 4% | 3-5% |
| Rest of Europe | 9,600 | 12% | 4-6% |

**Email personalisation:**
- Subject: "✈️ Hotel + ticket packs Barcelona — Sónar 2026"
- Hero image: Barcelona cityscape
- CTA: "View packs from [Origin City]"
- Include: Recommended flights, hotels near venue, 48h tourist guide

**Expected performance:**
- Open rate: 28-35% (relevant information for tourists)
- Full package conversion: 8-12% (vs 3-5% single ticket)
- Average ticket: €290 (ticket €180 + hotel 2 nights €110)
- Revenue: 32,000 × 10% × €290 = €928,000

**Lift vs generic message:** +45% conversion (fans appreciate all-inclusive convenience)

---

## 3. Spend

Your fans' purchase behaviour and expenditure.

### Most Useful Criteria

| Criterion | What It Is | Example Use |
|-----------|-----------|-------------|
| **Total spend** | Total money spent | Fans who spent more than €500 (VIPs) |
| **Ticket spend** | On tickets only | Fans with high ticket spend vs merchandise |
| **Cashless top-up** | Whether they topped up a wristband | Fans who use cashless (more engaged) |
| **Amount topped up** | How much they topped up | Fans who topped up €100+ |
| **Add-ons purchased** | Merchandise, parking, etc. | Fans who bought parking |
| **Supplements** | Upgrades, VIP packages | Fans who bought a VIP upgrade |
| **Purchase date** | Last transaction | Fans who have not bought for 180+ days |

### Use Cases

**Example 1: VIP Re-engagement**
```
Criterion: Total spend
Operator: Greater than
Value: €500 (50000 cents)

AND

Criterion: Purchase date
Operator: More than X days ago
Value: 180 days
```
**Result**: Inactive VIPs → Win-back campaign with special discount

For mid-size festivals, we recommend classifying VIPs with spend ≥€500. This threshold varies based on average ticket; see our [best practices guide to calculate VIP thresholds by event type](./best-practices).

#### Example 4: VIP Segmentation by Historical Spend

**Criterion:** Total historical spend: ≥€800

**Use case:** VIP package upsell campaign for a large festival

**Typical segment:**
- Total base: 150,000 contacts
- VIPs (≥€800): 12,000 fans (8%)

**Breakdown by level:**
| Level | Spend | Fans | % |
|-------|-------|------|---|
| VIP Gold | €800-2,000 | 9,500 | 6.3% |
| VIP Platinum | €2,000-5,000 | 2,000 | 1.3% |
| VIP Diamond | €5,000+ | 500 | 0.3% |

**Expected performance:**
- VIP Gold open rate: 52-60%
- VIP Platinum open rate: 65-75%
- VIP upgrade conversion: 8-14%

**Recommended action:** Do not offer discounts. Focus on exclusivity (meet & greet, backstage access, limited merchandise).

**Incremental revenue:** 12,000 fans × 10% conversion × €150 average upgrade = €180,000

**Example 2: Cashless Upsell**
```
Criterion: Topped up cashless
Operator: Is true

AND

Criterion: Amount topped up
Operator: Less than
Value: €50
```
**Result**: Fans who use cashless but spend little → Incentive to top up more

---

## 4. Interaction

How your fans engage with your communications.

### Most Useful Criteria

| Criterion | What It Is | Example Use |
|-----------|-----------|-------------|
| **Campaign received** | Received a specific email | Fans who received "Black Friday 2024" |
| **Campaign opened** | Opened the email | Engaged fans who read your emails |
| **Campaign with click** | Clicked a link | Interested fans (considering purchase) |
| **Days to open** | How quickly they open | Fans who open within first 24h (very engaged) |
| **Days to click** | Speed of engagement | Fans who click immediately |
| **Short links clicked** | Trackable URLs | Fans who clicked a specific link |

### Use Cases

**Example 1: Abandoned Cart Recovery**
```
Criterion: Campaign received
Operator: Equals
Value: "Festival X Booking Confirmation"

AND

Criterion: Campaign opened
Operator: Equals
Value: "Festival X Booking Confirmation"

BUT NOT (exclude):

Criterion: Event attended
Operator: Equals
Value: "Festival X"
```
**Result**: Fans who reserved but did not buy → Reminder with urgency

**Example 2: Super Engaged Fans**
```
Criterion: Days to open
Operator: Less than
Value: 2 days

AND

Criterion: Days to click
Operator: Less than
Value: 3 days
```
**Result**: Fans who respond quickly → Target for exclusive flash offers

---

## 5. Channels

Your fans' presence on different platforms.

### Available Criteria

| Criterion | What It Is | Example Use |
|-----------|-----------|-------------|
| **Mobile app downloaded** | Has your app installed | Fans with app → Push notifications |
| **Instagram connected** | Linked their Instagram account | Instagram fans → Instagram contest |
| **TikTok connected** | Linked their TikTok account | TikTok fans → Viral campaign |
| **Verified phone** | Verified phone number | Fans reachable by SMS |

### Use Cases

**Example 1: Push Notification Campaign**
```
Criterion: Mobile app downloaded
Operator: Is true

AND

Criterion: City
Operator: Equals
Value: Madrid
```
**Result**: Fans with the app in Madrid → Push notification for a local event

**Example 2: Social Media Contest**
```
Criterion: Instagram connected
Operator: Is true

AND

Criterion: TikTok connected
Operator: Is true
```
**Result**: Fans active on social media → Multi-platform contest

---

## 6. Nevent Score®

Predictive analytics that measures the engagement level of each fan.

### Available Criteria

| Criterion | What It Is | Values |
|-----------|-----------|--------|
| **Nevent Temperature** | Engagement level in 6 categories | Frozen, Very Cold, Cold, Warm, Hot, Very Hot |
| **Score value** | Exact number from 0-100 | Precise score for granular segmentation |

### Temperature Scale

| Temperature | Score | Meaning | Recommended Use |
|-------------|-------|---------|-----------------|
| 🔥 **Very Hot** | 84-100 | Ultra-engaged super fans | VIP experiences, meet & greets, pre-sales |
| 🔥 **Hot** | 67-83 | Very active fans | Early birds, exclusive offers |
| 🌡️ **Warm** | 51-66 | Moderate engagement | General campaigns, newsletters |
| ❄️ **Cold** | 34-50 | Low engagement | Educational content, engagement campaigns |
| ❄️ **Very Cold** | 17-33 | Low engagement | Win-back with strong incentives |
| 🧊 **Frozen** | 0-16 | Inactive or new | Welcome (new) or aggressive reactivation |

### How Is It Calculated?

The Nevent Score® automatically analyses:
- Event attendance frequency
- Purchase recency
- Total historical spend
- Email engagement (opens, clicks)
- Platform interaction
- Purchase speed (early bird vs last minute)

**You do not need to do anything**: Nevent calculates and updates it automatically every week.

### Use Cases

**Example 1: Exclusive VIP Offer**
```
Criterion: Nevent Temperature
Operator: Equals
Value: Very Hot OR Hot

AND

Criterion: City
Operator: Equals
Value: Madrid
```
**Result**: Super fans from Madrid → Invitation to a private VIP event

**Example 2: Reactivation Campaign**
```
Criterion: Nevent Temperature
Operator: Equals
Value: Very Cold OR Frozen

AND

Criterion: Total spend
Operator: Greater than
Value: €200
```
**Result**: Former VIPs who have gone cold → Win-back with 30% discount

**Example 3: Segmentation by Exact Score**
```
Criterion: Nevent Score value
Operator: Between
Value: 70 and 90
```
**Result**: Fans in a specific range → Testing new offers

---

## Combining Categories

The real power comes from combining categories:

**Example: Local VIP Super Fan**
```
CATEGORY: Fan Attributes
├─ City: Madrid

AND

CATEGORY: Attendance
├─ Number of events: >= 5

AND

CATEGORY: Spend
├─ Total spend: > €500

AND

CATEGORY: Nevent Score®
├─ Temperature: Very Hot
```

**Result**: Ultra-engaged VIP super fans from Madrid → Your most valuable audience for premium offers.

---

## Most Popular Criteria

According to our clients, these are the most used criteria:

### Top 10 Criteria

1. **City of residence** (geo-targeting)
2. **Nevent Temperature** (engagement level)
3. **Number of events attended** (loyalty)
4. **Total spend** (VIP identification)
5. **Age** (demographic targeting)
6. **Purchase date** (recency)
7. **Campaign opened** (engagement tracking)
8. **Event city** (event location preferences)
9. **Mobile app downloaded** (channel availability)
10. **Ticket type** (ticket tier preferences)

---

## Next Steps

Now that you know all the categories:

1. **[Learn to Use Operators](./operators-and-logic)** — How to compare and combine criteria
2. **[See Real Use Cases](./use-cases)** — Inspiration with complete examples
3. **[Best Practices](./best-practices)** — Tips to optimise results

---

[RFM analysis: automatically identify Champions and at-risk VIPs →](./modifiers-and-rfm)