RFM analysis for festivals - advanced segmentation Nevent

Frequently Asked Questions about RFM

What is RFM for festivals?

RFM is a scoring model that classifies fans across 3 dimensions:

Recency — When did they last purchase?
Frequency — How many times have they purchased?
Monetary — How much have they spent in total?

Applied to festivals and events:

Dimension	Poor	Fair	Good	Excellent
Recency	+365 days	180-365 days	90-180 days	<90 days
Frequency	1 event	2-3 events	4-6 events	7+ events
Monetary	<€100	€100-300	€300-800	€800+

Combining R+F+M generates 11 automatic segments:

Champions (High R + High F + High M): Best fans, buy frequently and recently
Loyal (High R + High F + Medium M): Loyal fans, buy regularly
Potential Loyalists (High R + Medium F + Medium M): Promising, nurture towards Loyal
New Customers (High R + Low F + Low M): Recent first purchase
Promising (Medium R + Low F + Medium M): Potential, need engagement
Need Attention (Medium R + Medium F + Medium M): At risk of losing, reactivate
About to Sleep (Low R + Medium F + Low M): Inactive, last chance
At Risk (Low R + High F + High M): CRITICAL — High value but inactive
Can’t Lose Them (Low R + High F + High M): VIPs at extreme risk
Hibernating (Very Low R + Medium F + Medium M): Dormant, difficult win-back
Lost (Very Low R + Low F + Low M): Churned, consider sunsetting

Advantage vs manual segmentation:

Nevent calculates RFM automatically (no configuration required)
Updated in real time (every purchase recalculates scoring)
11 ready-to-use segments (no need to think about criteria)

Example — Identifying Champions:

Manual filter (traditional):
Spend: ≥€800
AND Events: ≥4
AND Last purchase: ≤90 days

VS

RFM filter (Nevent):
RFM Score: Champions

Identical result, but RFM is 1-click.

How do recency modifiers work?

Recency modifiers filter fans by “how long ago they purchased their last ticket”.

Syntax in Nevent:

Days since last purchase: ≥X, ≤Y

X = Minimum (at least this long ago) Y = Maximum (at most this long ago)

Strategic ranges and recommended actions:

A. Ultra-recent fans (<30 days) — Hot upsell

Days since last purchase: ≥0, ≤30

Repurchase probability: 40-55% (very high)
Action: Immediate upsell (VIP upgrade, merchandise, related event)
Email: “Thank you for your purchase — Complete your experience with…”
Timing: 3-7 days post-purchase
Do not offer a discount (they just paid full price)

B. Recently active fans (<90 days) — High engagement

Days since last purchase: ≥0, ≤90

Repurchase probability: 25-40%
Action: Cross-sell similar events, early bird for next festival
Email: Direct announcement without discount (they do not need an incentive)
Subject: “New events you will love”
Expected conversion: 8-15%

C. Lukewarm fans (6-12 months) — Need incentive

Days since last purchase: ≥180, ≤365

Repurchase probability: 15-25%
Action: 10-15% discount + additional benefit (priority access, exclusive content)
Email: “We miss you — Come back with 10% OFF”
Urgency: Code expires in 7 days
Expected conversion: 5-10%

D. Dormant fans (1-2 years) — Aggressive win-back

Days since last purchase: ≥365, ≤730

Repurchase probability: 8-15%
Action: Win-back campaign with 20%+ discount and nostalgia
Email sequence:
1. Nostalgia: “Do you remember seeing [Artist] in [Year]?”
2. Incentive: “Special 20% discount for former fans only”
3. Urgency: “Last chance — Code expires tomorrow”
Expected conversion: 3-8%
If they do not buy: Move to “Lost”

E. Lost fans (>2 years) — Last attempt

Days since last purchase: ≥730

Repurchase probability: 2-5%
Action: Final win-back email or sunset (remove from active list)
Email: “We miss you — 25% discount, last chance”
If they do not open/buy within 30 days: Clean from list (improves deliverability)

Recency visualisation and repurchase probability:

Days Since Purchase	Recency	Repurchase Probability	Action	Incentive
0-30	Ultra fresh	40-55%	Upsell	0% discount
31-90	Fresh	25-40%	Cross-sell	0-5% discount
91-180	Medium	15-25%	Engagement	5-10% discount
181-365	Low	8-15%	Reactivation	10-20% discount
366-730	Very low	3-8%	Win-back	20-25% discount
730+	Lost	2-5%	Sunset	25-30% discount

Pattern: The longer since purchase, the larger the discount needed to reactivate.

What are frequency modifiers?

Frequency modifiers filter fans by “how many times they purchased / attended events”.

Syntax in Nevent:

Number of events attended: ≥X, ≤Y

Segmentation by loyalty (frequency):

Frequency	Classification	Typical % of Audience	Average LTV	Strategy
1 event	New customer	45-60%	€120-180	Convert to repeat buyer
2-3 events	Repeat buyer	25-35%	€350-600	Nurture towards loyal
4-6 events	Loyal fan	8-15%	€800-1,500	Retention and VIP upsell
7+ events	Super fan	2-5%	€2,000-8,000	Advocacy and maximise LTV

Differentiated strategies by frequency:

1. New Customers (Frequency = 1)

Number of events: 1
AND Days since purchase: ≤90 (purchased recently)

Objective: Convert to repeat buyer (critical window: 6-12 months)

Email #1 — Welcome (Post-event +7 days):

Subject: “Thank you for coming to [Festival] — How was your experience?”
Survey: NPS score + feedback
Incentive: “10% discount on next event if you complete the survey”

Email #2 — Cross-sell (30-45 days post-event):

Subject: “Similar events you might enjoy”
Content: 3 events from the portfolio that match the genre of the first event
Offer: “15% discount 2nd event — Code COMEBACK15”

Repeat rate target: 30-50% of New Customers within 12 months

2. Repeat Buyers (Frequency = 2-3)

Number of events: ≥2, ≤3

Objective: Nurture towards Loyal (increase engagement)

Tactics:

Monthly email with exclusive content (behind-the-scenes, artist interviews)
Early access to announcements (12-24h before general public)
Moderate 10% discount (less than New, more than Loyal)
Invitation to special events (listening parties, meet & greet draw)

Conversion for next event: 40-60%

3. Loyal Fans (Frequency = 4-6)

Number of events: ≥4, ≤6

Objective: Retention (keep them engaged) + VIP upsell

Tactics:

NO discounts (already convinced)
Early bird access 48h before (vs 24h for repeat buyers)
VIP upgrade with 15% discount
Exclusive merchandise
Invitation to loyalty programme (points, tier status)

Emails:

Subject: “Exclusive VIP access for you — [Name]”
Content: VIP benefits in detail, testimonials
Offer: “Upgrade to VIP for just €80 more” (no discount on the ticket, only on the upgrade)

VIP upgrade rate: 8-14% Retention rate: 75-85%

4. Super Fans (Frequency = 7+)

Number of events: ≥7

Objective: Advocacy (referrals) + Maximise LTV

Profile:

Typical age: 28-45
Historical spend: €2,000-8,000
Behaviour: Buy without discounts, tolerate price increases, refer friends

Extreme VIP tactics:

Ambassador programme invitation (free tickets to small event in exchange for promotion)
Guaranteed artist meet & greet
Backstage access
Personal call from account manager (large festivals)
Personalised gifts (signed merchandise, limited vinyl)

Email:

Subject: “Exclusive invitation: Platinum VIP Programme”
Content: “You are in the top 2% of our fans — we want to recognise that”
Benefits: List of 10 exclusive benefits
CTA: “Accept Platinum VIP invitation” (free, not a sale)

Retention rate: 90-95% (very high) Referral rate: 40-60% bring at least 1 friend Incremental LTV: +30-50% vs Loyal

Frequency → Revenue correlation:

Segment	Fans (of 50k)	% Audience	Revenue/Fan	Total Revenue	% Total Revenue
Super fans (7+)	1,500	3%	€400	€600,000	31%
Loyal (4-6)	5,000	10%	€250	€1,250,000	64%
Repeat (2-3)	12,500	25%	€150	€1,875,000	…
New (1)	25,000	50%	€120	€3,000,000	…

Extreme Pareto: 13% of audience (Loyal + Super fans) generates 95% of long-term revenue.

Can I combine recency and frequency?

Yes. Combining Recency × Frequency is the foundation of the RFM model and generates the most powerful segments.

Simplified RFM matrix (2×2):

	High Frequency (≥4 events)	Low Frequency (≤3 events)
High Recency (<180 days)	🏆 Champions (8-12% audience) Action: VIP upsell, no discounts	🌱 Promising (15-20% audience) Action: Nurture to loyal
Low Recency (≥180 days)	⚠️ At Risk (5-8% audience) Action: URGENT win-back	😴 Lost (50-60% audience) Action: Mass reactivation or sunset

Champions segment (High R + High F):

Days since last purchase: ≤180
AND Number of events: ≥4
AND Total spend: ≥€500

Champions profile:

Purchased 4-10 events
Last purchase <6 months ago
Spent €500-3,000 in total
Email open rate: 55-70%
Conversion rate: 18-28%

Champions email strategy:

Timing: First in line for early bird (48h before public)
Discount: 0% (they do not need it, they buy regardless)
Incentives: Early access, VIP upgrades, meet & greet, backstage
Frequency: Tolerate 2-3 emails/week (high engagement)
Ask for favours: Reviews, referrals, user-generated content

Champions conversion: 18-28% (vs 2-4% general audience) = 7-14x baseline

At Risk segment (Low R + High F):

Days since last purchase: ≥180, ≤730
AND Number of events: ≥3
AND Total spend: ≥€300

At Risk profile:

Historically purchased 3-8 events (were loyal)
Last purchase 6-24 months ago (ALERT)
Spent €300-2,000 (high value at risk)
Open rate has dropped: 10-25% (vs 50-65% when active)

Why they are At Risk:

Fatigue (went for many years in a row)
Changed circumstances (partner, children, work)
Competition (discovered another festival)
Lineup does not appeal (artists they like are no longer performing)

Urgent win-back (3-email sequence):

Email #1 — Nostalgia (Day 0):
- Subject: “We miss you [Name] 💔”
- Content: Photos from past events, “You were part of [X] incredible editions”
- Soft CTA: “View upcoming events” (no direct sale)
Email #2 — Incentive (Day +5):
- Subject: “20% VIP discount — For former fans only”
- Content: Code VIPBACK20, expires 7 days
- Testimonial: “I came back after 2 years and it was better than ever”
Email #3 — Urgency (Day +10):
- Subject: ”⏰ Your discount expires tomorrow”
- Content: Countdown, last chance
- Guarantee: “100% refund if not satisfied”

Win-back rate: 15-25% of At Risk ROI: 15-30x (low cost, high value if recovered)

Promising segment (High R + Low F):

Days since last purchase: ≤90
AND Number of events: 1-2

Promising profile:

Purchased recently (engaged)
But only 1-2 times (not yet loyal)
Potential to become Loyal if nurtured well

Nurture strategy:

Educational email: “10 reasons to come back”
Exclusive content: Artist interviews, behind-the-scenes
Moderate discount: 10% (soft incentive)
Social proof: “15,000 fans return every year”

Conversion for next event: 35-50% Objective: Move them from Promising → Loyal in 6-12 months

Complete RFM matrix (11 segments):

See full table in the “What RFM segments exist?” section of this document.

Key takeaway: Recency + Frequency together predict future behaviour 10x better than each dimension alone.

How do I identify at-risk VIPs?

At-risk VIPs = High Frequency + High Monetary + Low Recency = “At Risk” or “Can’t Lose Them” segment in RFM.

Exact “VIP At Risk” segment:

Total historical spend: ≥€500
AND Number of events: ≥3
AND Days since last purchase: ≥180, ≤730
AND RFM Score: At Risk, Can't Lose Them

Translation: Fan who spent a lot (≥€500), attended repeatedly (≥3 events), but has not purchased in 6-24 months.

Additional red flags that confirm risk:

Email open rate <20% in the last 90 days (vs 55-70% when active)
Did not visit the website in 90 days
No social media interaction
Email bounce (changed email without updating)

Typical segment size:

Total VIP base (≥€500): 8-12% of audience
At Risk VIPs: 5-10% of total VIPs
Example: Out of 10,000 VIPs, 500-1,000 are At Risk

Value at risk:

Average At Risk VIP LTV: €800-2,500
500 VIPs × €850 LTV = €425,000 at risk
Without action: 60-70% churn in next 12 months = ~€300k lost

Impact of NOT acting:

Metric	Without Win-back	With Win-back Campaign
Churn rate (12 months)	60-70%	30-40%
VIPs lost (of 500)	300-350	150-200
LTV lost	€255k-298k	€128k-170k
Revenue recovered	€0	€75-125k (15-25% win-back)
Delta	—	€130k-220k saved/recovered

Win-back campaign for At Risk VIPs:

3-email sequence:

Email #1 — Nostalgia + Recognition:
- “You were part of [X] editions since [Year]”
- Personalised photos from events they attended
- Soft CTA: No direct sale
Email #2 — VIP Incentive:
- 20% exclusive discount code
- “Only 680 former VIPs have this code”
- Expires 7 days
Email #3 — Final urgency:
- Countdown timer
- “Last chance before losing VIP benefit”
- Refund guarantee

Expected performance:

Open rate: 35-45% (nostalgia works)
Win-back rate: 15-25%
Revenue recovered: €75-125k (from 500 At Risk VIPs)
ROI: 25-50x (low campaign cost vs high LTV recovered)

Prevention > Recovery:

Best strategy: Detect BEFORE they reach At Risk.

Early warning signals (intervene when you detect):

Champion who did NOT buy early bird (first time in 3 years)
Open rate dropped 50%+ in 3 months (e.g. 60% → 30%)
Did not visit website in 60 days (vs monthly visits before)

Preventive action:

Email: “We noticed you did not buy the early bird — everything OK?”
Survey: “What can we improve?”
Call (large festivals): Account manager contacts VIP personally
Soft incentive: 10% discount (not 20%, reserve that for actual At Risk)

Prevention success rate: 70-80% retention vs 15-25% At Risk recovery

Conclusion: Preventing Champions → At Risk is 3-4x more effective than recovering At Risk → Active.

Modifiers & RFM Analysis

RFM analysis for festivals with Nevent: modifiers are enhancers that transform basic criteria into ultra-specific dynamic filters. Implement RFM analysis (Recency, Frequency, Monetary) on your festival and event attendee base, identifying VIP super fans, habitual early bird buyers and attendees at risk of churning.

What Are Modifiers?

Modifiers are “additional layers” that you apply to a criterion to make it more specific.

Without modifier:

Criterion: "Attended an event"
Result: Fans who attended (regardless of how many times or when)

With modifiers:

Criterion: "Attended an event"
+ Frequency modifier: "At least 5 times"
+ Recency modifier: "In the last 90 days"
Result: Fans who attended 5+ times in the last 90 days

The 2 Main Modifiers

1. Recency Modifier (Time Range)

What does it do? Filters actions that occurred within a specific time period.

Available options:

In the last X days (e.g. last 30 days)
In the last X weeks (e.g. last 2 weeks)
In the last X months (e.g. last 6 months)
In the last X years (e.g. last year)

Example 1: Recent Fans

Criterion: Ticket purchase date
Modifier: In the last 30 days

Result: Fans who purchased in the last month → Active audience

Example 2: Inactive Fans

Criterion: Purchase date
Modifier: More than 180 days ago (NOT in last 180 days)

Result: Fans who have NOT purchased in 6 months → Target for reactivation

2. Frequency Modifier

What does it do? Filters based on how many times the fan performed an action.

Available options:

Exactly X times (e.g. exactly 3 purchases)
At least X times (e.g. minimum 5 events attended)
At most X times (e.g. maximum 2 clicks)

Example 1: Super Fans

Criterion: Event attended
Modifier: At least 10 times

Result: Fans who attended 10+ events → Loyalists

Example 2: New Buyers

Criterion: Ticket purchase
Modifier: Exactly 1 time

Result: Fans who have only bought once → Target for second purchase

Example 3: Occasional Buyers

Criterion: VIP ticket type
Modifier: At most 2 times

Result: Fans who bought VIP 1-2 times → Upsell to annual VIP membership

Combining Both Modifiers

The real power comes from combining recency + frequency in the same criterion.

Example: Recently Engaged Fans

Criterion: Campaign opened
Modifiers:
  - Frequency: At least 5 times
  - Recency: In the last 60 days

Result: Fans who opened 5+ campaigns in the last 2 months → Ultra engaged

Example: Habitual Early Birds

Criterion: Days in advance of purchase
Value: Greater than 30 days
Modifiers:
  - Frequency: At least 3 times
  - Recency: In the last 365 days

Result: Fans who purchased 30+ days in advance 3+ times in the past year → Target for exclusive early bird

What Is RFM Analysis?

RFM is a marketing framework that segments customers across 3 dimensions:

Dimension	What It Measures	Key Question
Recency	How recent the last interaction was	When was their last purchase?
Frequency	How often they interact	How many times have they purchased?
Monetary	How much they spend	How much money have they spent in total?

Why RFM Is Powerful

The best customers are those with:

✅ High Recency (purchased recently)
✅ High Frequency (purchase often)
✅ High Spend (spend a lot)

RFM lets you identify:

🏆 Champions (High RFM across the board) → Your most valuable audience
⚠️ At Risk (Historically high RFM, low recency) → Reactivation campaign
🌱 Promising (High recency and frequency, low spend) → Upsell opportunity
💤 Hibernating (Low on all) → Aggressive win-back or discard

RFM with Nevent Modifiers

How Modifiers Map to RFM

RFM Dimension	Nevent Modifier	Example Criterion
Recency	✅ Recency Modifier	Purchase in last 30/60/90 days
Frequency	✅ Frequency Modifier	Attended 5+ events
Monetary	✅ Direct criterion (no modifier)	Total spend > €500

Ready-to-Use RFM Segments

Segment 1: Champions (High RFM)

Profile: Your best customers. They purchase recently, frequently and spend a lot.

Segment:

GROUP A: High Recency
├─ Purchase in last 60 days

AND

GROUP B: High Frequency
├─ Event attended: At least 5 times

AND

GROUP C: High Monetary
├─ Total spend: Greater than €500

Campaign: VIP access, exclusive pre-sale, invitation to private events Expected: 5-10% of your base, 40-60% of your revenue

Segment 2: At Risk Customers (Were Champions, Now Inactive)

Profile: Used to be good customers but are drifting away.

Segment:

GROUP A: Low Recency (have not purchased for a while)
├─ Last purchase: More than 180 days ago

AND

GROUP B: High Historical Frequency
├─ Event attended: At least 8 times (without recency modifier)

AND

GROUP C: High Historical Spend
├─ Total spend: Greater than €400

Campaign: Win-back with 25% discount, emotional message (“we miss you”), urgent incentive Expected: 15-25% reactivation with a strong offer

Segment 3: Promising (High Recency + Frequency, Low Spend)

Profile: Engaged customers who buy often but spend little. Upsell opportunity.

Segment:

GROUP A: High Recency
├─ Purchase in last 45 days

AND

GROUP B: High Frequency
├─ Campaign opened: At least 3 times in last 90 days

AND

GROUP C: Low Spend
├─ Total spend: Less than €150

Campaign: Bundle offer (3 events for the price of 2), VIP upgrade with discount, payment plan Expected: 20-35% conversion to premium ticket

Segment 4: New Customers (Recent First Purchase)

Profile: Purchased for the first time recently. Needs to be nurtured.

Segment:

GROUP A: High Recency
├─ Purchase in last 30 days

AND

GROUP B: Low Frequency
├─ Event attended: Exactly 1 time

Campaign: Onboarding with benefits, discount for second purchase, referral programme Expected: 30-40% purchase again within 90 days

Segment 5: Hibernating (Low RFM Across the Board)

Profile: Inactive customers with little history. Reactivation is difficult.

Segment:

GROUP A: Very Low Recency
├─ Last purchase: More than 365 days ago

AND

GROUP B: Low Frequency
├─ Event attended: At most 2 times

AND

GROUP C: Low Spend
├─ Total spend: Less than €100

Campaign: Ultra-aggressive win-back (40%+ discount), last chance Decision: If they do not respond → clean from list (better deliverability)

Criteria that Support Modifiers

With Recency Modifier (13 criteria)

Criterion	Example Use
Event attended	Attended in last 90 days
Purchase date	Purchased in last 30 days
Total spend	Spent in last 6 months
Campaign opened	Opened in last 2 weeks
Campaign with click	Clicked in last 14 days
Cashless top-up	Topped up in last year
Add-on purchased	Bought merchandise recently

And 6 more…

With Frequency Modifier (9 criteria)

Criterion	Example Use
Event attended	At least 5 times
Ticket type	VIP exactly 2 times
Campaign received	At least 10 campaigns
Campaign opened	At least 7 opens
Campaign with click	At most 3 clicks
Short link clicked	At least 2 times

And 3 more…

Advanced Use Cases

Case 1: Recent Festival Repeat Buyers

Objective: Sell an annual pass to fans who return to festivals.

Segment:

Criterion: Event attended (type Festival)
Modifiers:
  - Frequency: At least 3 festivals
  - Recency: In last 365 days
AND
Average spend per event: Greater than €80

Result: Fans who attended 3+ festivals last year with ticket > €80

Campaign: Annual pass offer with 30% discount vs buying individually

Case 2: Email Engagement Score

Objective: Identify fans who are ultra-engaged with your communications.

Segment:

GROUP A:
├─ Campaign opened
│  └─ Frequency: At least 10 times
│  └─ Recency: In last 90 days

AND

GROUP B:
├─ Campaign with click
│  └─ Frequency: At least 5 times
│  └─ Recency: In last 90 days

Result: Fans who opened 10+ emails and clicked 5+ in the last quarter

Campaign: Beta programme invitation, focus group, brand ambassadors

Case 3: Churn Prediction

Objective: Detect fans who used to buy regularly but are drifting away.

Segment:

GROUP A (Good history):
├─ Event attended: At least 6 times (without recency)
└─ Total spend: Greater than €300

AND

GROUP B (Poor recency):
├─ Last purchase: More than 120 days ago
└─ Campaign opened in last 60 days: 0 times (no engagement)

Result: Historically good customers who have gone 4+ months without buying or opening emails

Campaign: Urgent reactivation, survey (“why did you leave?”), irresistible offer

Best Practices with Modifiers

1. Start with Broad Ranges, Refine Later

❌ Bad — Too specific from the start:

Campaign opened: Exactly 7 times in last 23 days

✅ Good — Start broad:

Step 1: Campaign opened: At least 5 times in last 30 days
Step 2: Measure results
Step 3: Adjust to "at least 7 times" if you need more qualification

2. Combine Modifiers with Nevent Temperature

Event attended: At least 3 times in last 90 days
AND
Nevent Temperature: Is "Hot" OR "Very Hot"

Why: Double engagement validation (behaviour + predictive score)

3. Use Recency to Detect Behavioural Changes

Example — Fans who have gone cold:

Spend in 2023: Greater than €500 (without recency modifier)
AND
Spend in last 180 days: Less than €50 (with modifier)

Result: Historical VIP fans who have stopped spending → Urgent win-back

4. Create Temporal Cohorts

Q1 2024 Cohort:

Purchase between: 01-Jan-2024 and 31-Mar-2024
AND
Frequency: At least 2 purchases in that period

Use: Compare behaviour Q1 vs Q2 vs Q3 (cohort analysis)

Limitations and Considerations

Performance

Modifiers with frequency require GROUP BY → more computationally expensive
Segments with 3+ modifiers may take 5-10 seconds to calculate
Recommendation: Use preview first before executing on 100k+ fans

Availability

How to tell if a criterion supports modifiers:

In the UI, a ”+ Add modifier” button will appear
If it does not appear, the criterion does not support modifiers

RFM Scoring Table

Use this table to classify your fans into RFM segments:

What RFM segments exist and how should I act on each?

Segment	Recency	Frequency	Monetary	Recommended Action
Champions	< 30 days	5+ events	> €500	VIP treatment, early access
Loyal Customers	< 90 days	3-5 events	€200-500	Rewards programme, upsell
Promising	< 45 days	2-3 events	< €200	Bundle offers, payment plans
At Risk	> 180 days	5+ events	> €400	Aggressive win-back, incentives
Can’t Lose	> 120 days	8+ events	> €600	Maximum priority reactivation
New Customers	< 30 days	1 event	Any	Onboarding, second purchase
Hibernating	> 365 days	1-2 events	< €100	Last chance, list clean-up

Next Steps

Now that you understand modifiers and RFM:

Apply in Use Cases — See practical examples with modifiers
Operators & Logic — Combine modifiers with AND/OR logic
Best Practices — Optimise your RFM segments

6 use cases with real numbers: from early bird to VIP win-back →