---
title: Data-Driven Segmentation
summary: How to segment customers by behavior, run RFM analysis, and apply service-business frameworks that predict value.
est_minutes: 20
sort: 2
---

#### LESSON 1: Building Customer Segments Based on Actual Behavior Patterns

Segmentation is not about creating categories. It is about finding patterns in behavior that predict value.

Most businesses segment wrong. They create categories based on what feels right, not what the data shows.

Here is how to segment based on behavior.

**The Segmentation Principle**

**Bad segmentation:** Based on who customers are (demographics, firmographics)

**Good segmentation:** Based on what customers do (behaviors, actions, patterns)

**Why behavior matters:**

Past behavior predicts future behavior. Someone who bought from you three times in the last month is more likely to buy again than someone who bought once two years ago.

Demographics tell you nothing about this. A 40-year-old who bought once and a 40-year-old who bought ten times are not the same customer. But demographic segmentation treats them identically.

Behavioral segmentation separates them. And gives you different strategies for each.

**The Three Types of Behavioral Data**

**1. Transactional Behavior**

What customers buy and how often:

- Purchase frequency (how often they buy)
- Purchase recency (how recently they bought)
- Purchase value (how much they spend)
- Product preferences (what they buy)
- Cart behavior (add to cart but don't purchase)

**2. Engagement Behavior**

How customers interact with your brand:

- Website visits (frequency, duration, pages viewed)
- Email engagement (opens, clicks, replies)
- Content consumption (blog reads, video watches)
- Social media interaction (likes, comments, shares)
- Support interactions (tickets, calls, chats)

**3. Lifecycle Behavior**

Where customers are in their journey:

- New customer (first 30-90 days)
- Active customer (regular engagement/purchases)
- At-risk customer (engagement declining)
- Churned customer (no activity for X months)
- Reactivated customer (returned after churn)

All three types of behavior predict value. The best segmentation uses all three.

**The Behavioral Segmentation Framework**

**Step 1: Define Your Behavioral Metrics**

Choose 3-5 metrics that predict value in your business.

**E-commerce example:**

1. Purchase recency (days since last purchase)
2. Purchase frequency (purchases in last 12 months)
3. Monetary value (total spend in last 12 months)
4. Product breadth (number of different product categories purchased)
5. Return rate (% of orders returned)

**SaaS example:**

1. Login frequency (logins per month)
2. Feature adoption (% of features used)
3. Team size (number of active users)
4. Tenure (months as customer)
5. Support ticket volume (tickets per month)

**Service business example:**

1. Project frequency (projects per year)
2. Project value (average project size)
3. Retention (years as client)
4. Referrals (number of referrals sent)
5. Payment speed (days to pay invoices)

**Step 2: Score Customers on Each Metric**

Assign scores (1-5 or 1-10) for each metric.

**Example: Purchase Recency (E-commerce)**

- Score 5: Purchased in last 30 days
- Score 4: Purchased 31-60 days ago
- Score 3: Purchased 61-90 days ago
- Score 2: Purchased 91-180 days ago
- Score 1: Purchased 180+ days ago

**Example: Login Frequency (SaaS)**

- Score 5: 20+ logins per month
- Score 4: 10-19 logins per month
- Score 3: 5-9 logins per month
- Score 2: 1-4 logins per month
- Score 1: 0 logins per month

**Step 3: Create Composite Scores**

Combine individual metric scores into overall behavioral score.

**Simple method:** Add all scores

Customer A:

- Recency: 5
- Frequency: 4
- Monetary: 5
- Breadth: 3
- Returns: 5
- **Total: 22 out of 25**

Customer B:

- Recency: 2
- Frequency: 1
- Monetary: 2
- Breadth: 1
- Returns: 3
- **Total: 9 out of 25**

Customer A is clearly more valuable than Customer B.

**Weighted method:** Weight metrics by importance

If recency and monetary value matter more than others:

- Recency: 5 x 2 (weight) = 10
- Frequency: 4 x 2 (weight) = 8
- Monetary: 5 x 2 (weight) = 10
- Breadth: 3 x 1 (weight) = 3
- Returns: 5 x 1 (weight) = 5
- **Weighted total: 36 out of 60**

Use weighting when some behaviors predict value much better than others.

**Step 4: Create Segments Based on Score Ranges**

Group customers into segments based on total scores.

**Example 5-tier segmentation (out of 25):**

- **Champions (21-25 points):** Best customers, highest value
- **Loyal (16-20 points):** Solid customers, good value
- **Promising (11-15 points):** Growing customers, potential
- **At-Risk (6-10 points):** Declining customers, need attention
- **Lost (0-5 points):** Inactive customers, likely churned

**Or 3-tier segmentation (simpler):**

- **High-Value (17+ points):** Top 20% of customers
- **Medium-Value (9-16 points):** Middle 60% of customers
- **Low-Value (0-8 points):** Bottom 20% of customers

Fewer segments means simpler to manage. More segments means more precise targeting.

Start with 3-5 segments. Add complexity only when you can act on it.

**Behavioral Patterns That Predict Value**

Beyond scoring, look for behavioral patterns that indicate high or low value.

**High-Value Behavioral Patterns:**

**Pattern 1: Increasing Engagement**

- Purchases increasing over time (bought monthly, now buying weekly)
- Usage increasing (logged in 5X/month, now 20X/month)
- Expansion (started with 1 user, now 10 users)

**Why it predicts value:** Customer finding more value over time. Likely to stay and spend more.

**Pattern 2: Consistent Behavior**

- Purchases on regular schedule (every 30 days like clockwork)
- Engagement stable (always opens emails, always logs in)
- Predictable pattern (always buys same products)

**Why it predicts value:** Customer has formed habit. Switching costs are high. Churn risk is low.

**Pattern 3: Multi-Product Adoption**

- Started with Product A, added Product B, then Product C
- Uses multiple features in SaaS product
- Buys across multiple categories in e-commerce

**Why it predicts value:** Customer is invested. More surface area = harder to leave. Higher LTV.

**Pattern 4: Advocacy Behavior**

- Leaves reviews
- Refers others
- Engages on social media
- Responds to surveys

**Why it predicts value:** Emotional investment beyond transaction. Brand loyalty indicator.

**Low-Value Behavioral Patterns:**

**Pattern 1: Declining Engagement**

- Purchases decreasing (was monthly, now quarterly)
- Logins decreasing (was daily, now weekly, now monthly)
- Email engagement dropping (opened every email, now opens none)

**Why it predicts churn:** Customer finding less value. Likely shopping competitors. High churn risk.

**Pattern 2: Inconsistent Behavior**

- Purchases sporadically with no pattern
- Random engagement (logs in, disappears for months, returns randomly)
- No predictable schedule

**Why it predicts lower value:** No habit formed. Not dependent on your product. Will churn when convenient alternative appears.

**Pattern 3: Single-Product/Feature Use**

- Only uses one product when you offer multiple
- Only uses core feature, ignores advanced features
- Narrow use case

**Why it predicts lower value:** Low switching costs. Easy to replace. Limited expansion opportunity.

**Pattern 4: High Friction Behavior**

- Frequent returns (e-commerce)
- High support ticket volume (SaaS)
- Payment issues (failed payments, late payments)
- Complaints on social media

**Why it predicts lower value:** High cost to serve. Low satisfaction. Likely to churn or damage brand.

**From Behavioral Patterns to Segments**

Once you identify patterns, create segments that combine multiple behavioral signals.

**Example E-commerce Segments:**

**Segment 1: "VIP Repeat Buyers"**

- Purchase frequency: 10+ purchases per year
- Recency: Bought in last 30 days
- Monetary: $1,000+ annual spend
- Pattern: Increasing engagement
- Size: 5% of customers
- Value: 40% of revenue

**Strategy:** VIP treatment, early access, exclusive offers, loyalty rewards

**Segment 2: "Loyal Regulars"**

- Purchase frequency: 4-9 purchases per year
- Recency: Bought in last 60 days
- Monetary: $300-$999 annual spend
- Pattern: Consistent behavior
- Size: 15% of customers
- Value: 35% of revenue

**Strategy:** Nurture into VIP segment, cross-sell, retention focus

**Segment 3: "Promising Newcomers"**

- Purchase frequency: 1-3 purchases
- Recency: Bought in last 90 days
- Monetary: $50-$299 total spend
- Pattern: Increasing engagement
- Size: 30% of customers
- Value: 15% of revenue

**Strategy:** Onboarding, education, second purchase campaigns

**Segment 4: "At-Risk Former Buyers"**

- Purchase frequency: 1-5 purchases historically
- Recency: Last purchase 90-180 days ago
- Monetary: Varies
- Pattern: Declining engagement
- Size: 25% of customers
- Value: 8% of revenue

**Strategy:** Win-back campaigns, special offers, feedback requests

**Segment 5: "One-Time Buyers"**

- Purchase frequency: 1 purchase only
- Recency: Bought 180+ days ago
- Monetary: Usually low
- Pattern: Never returned
- Size: 25% of customers
- Value: 2% of revenue

**Strategy:** Low-effort reactivation, exclude from expensive campaigns

**Notice:** The top 20% of customers (VIP + Loyal) drive 75% of revenue. Behavioral segmentation identifies them.

**The Segmentation-Strategy Matrix**

Different segments need different strategies.

| Segment | Acquisition | Retention | Expansion | Win-Back |
|---|---|---|---|---|
| VIP | Low priority | **HIGH** | **HIGH** | N/A |
| Loyal | Medium | **HIGH** | **HIGH** | Low |
| Promising | Medium | **HIGH** | Medium | N/A |
| At-Risk | Low | Medium | Low | **HIGH** |
| One-Time | **HIGH** (lookalikes) | Low | Low | Low |
| Lost | Low | N/A | N/A | Low |

**VIP Customers:** Invest heavily in retention and expansion. Do not lose them.

**Loyal Customers:** Nurture into VIP. Prevent downgrade to at-risk.

**Promising Customers:** Critical to retain early. They are deciding if they will become loyal.

**At-Risk Customers:** Win-back campaigns. Understand why they are declining.

**One-Time Buyers:** Low investment. Focus on acquiring more customers who look like your VIPs, not like one-time buyers.

**Lost Customers:** Minimal investment unless churn was recent and value was high.

**Avoiding Common Segmentation Mistakes**

**Mistake 1: Too Many Segments**

Creating 20 different segments because you can.

**Problem:** Cannot create 20 different strategies. Complexity kills execution.

**Solution:** Start with 3-5 segments. Only add more when you can act differently on them.

**Mistake 2: Static Segments**

Creating segments once and never updating them.

**Problem:** Customer behavior changes. VIP customers can become at-risk. Promising customers can become loyal.

**Solution:** Re-segment monthly or quarterly. Customers should move between segments based on behavior.

**Mistake 3: Demographic Segments in Behavioral Clothing**

Calling segments "Small Business" and "Enterprise" and claiming they are behavioral.

**Problem:** These are firmographic, not behavioral. Two small businesses can have completely different behavioral patterns.

**Solution:** Segment based on behavior first. Demographic patterns may emerge within behavioral segments, but do not lead with demographics.

**Mistake 4: Ignoring Segment Economics**

Treating all customers equally because "every customer matters."

**Problem:** Some customers cost more to serve than they are worth. Not all customers are profitable.

**Solution:** Calculate cost to serve and LTV by segment. Deprioritize or fire unprofitable segments.

**Mistake 5: No Segment-Specific Strategy**

Creating segments but using the same approach for all of them.

**Problem:** If you market to VIPs the same way you market to one-time buyers, segmentation is useless.

**Solution:** Every segment needs a different strategy. Different offers. Different channels. Different messaging.

#### LESSON 2: RFM Analysis - The Foundation of E-Commerce Segmentation

RFM analysis is the most powerful segmentation framework for businesses with repeat purchase behavior.

It works for e-commerce, subscription businesses, SaaS with usage patterns, and any business where you can track recency, frequency, and monetary value.

Here is how to use it.

**What Is RFM?**

RFM stands for:

- **Recency:** How recently did the customer purchase?
- **Frequency:** How often do they purchase?
- **Monetary:** How much do they spend?

These three dimensions predict customer lifetime value better than demographics, acquisition source, or any other single metric.

**Why RFM works:**

**Recency predicts:** Engagement and purchase probability

- Someone who bought yesterday is more likely to buy again than someone who bought a year ago
- Recent customers are "warm" - they remember you and are still in buying mode

**Frequency predicts:** Loyalty and habit formation

- Someone who has bought 10 times has formed a habit
- Frequent buyers have proven your product works for them
- They have higher retention and lower churn

**Monetary predicts:** Customer value and budget capacity

- Someone who spends $10,000/year is more valuable than someone who spends $100/year
- Higher spenders often have bigger budgets and are willing to pay for quality
- They are worth more investment to retain

Combined, these three metrics create powerful segments.

**How to Calculate RFM Scores**

**Step 1: Pull Customer Data**

Extract from your database:

- Customer ID
- Date of most recent purchase (for Recency)
- Number of purchases in last 12 months (for Frequency)
- Total spend in last 12 months (for Monetary)

**Example data:**

| Customer | Last Purchase | # Purchases | Total Spend |
|---|---|---|---|
| A | 5 days ago | 12 | $5,000 |
| B | 200 days ago | 2 | $300 |
| C | 30 days ago | 8 | $2,400 |
| D | 90 days ago | 1 | $150 |

**Step 2: Score Each Dimension (1-5 scale)**

Divide each dimension into 5 equal groups (quintiles).

**Recency Scoring (lower days = higher score):**

Calculate days since last purchase. Then score:

- Score 5: 0-30 days (most recent)
- Score 4: 31-60 days
- Score 3: 61-90 days
- Score 2: 91-180 days
- Score 1: 181+ days (least recent)

**Frequency Scoring:**

Count purchases in last 12 months. Then score:

- Score 5: 10+ purchases (most frequent)
- Score 4: 7-9 purchases
- Score 3: 4-6 purchases
- Score 2: 2-3 purchases
- Score 1: 1 purchase (least frequent)

**Monetary Scoring:**

Total spend in last 12 months. Then score:

- Score 5: $2,000+ (highest spend)
- Score 4: $1,000-$1,999
- Score 3: $500-$999
- Score 2: $200-$499
- Score 1: <$200 (lowest spend)

**Note:** Your score thresholds will vary based on your business. Analyze your customer distribution and set thresholds so each quintile has approximately 20% of customers.

**Step 3: Assign RFM Scores**

| Customer | Recency Score | Frequency Score | Monetary Score | RFM Score |
|---|---|---|---|---|
| A | 5 | 5 | 5 | 555 |
| B | 1 | 2 | 2 | 122 |
| C | 4 | 4 | 4 | 444 |
| D | 3 | 1 | 1 | 311 |

Customer A (555) is your best customer: Recent, Frequent, High-Value. Customer B (122) is at-risk: Not recent, infrequent, low value. Customer C (444) is very strong: Solid across all three. Customer D (311) is a one-time buyer: Some recency but no repeat behavior.

**The 11 Core RFM Segments**

Based on RFM scores, customers fall into predictable segments:

**Segment 1: Champions (555, 554, 545, 544, 455)**

RFM: High Recency, High Frequency, High Monetary

**Characteristics:**

- Your best customers
- Buy recently, buy often, spend the most
- Lowest churn risk
- Often brand advocates

**Strategy:**

- VIP treatment and exclusive access
- Early product launches
- Loyalty rewards programs
- Personal outreach and relationship building
- Ask for referrals and testimonials
- Upsell premium products

**Segment 2: Loyal Customers (543, 444, 435, 355, 354)**

RFM: High Recency, High Frequency, Medium-High Monetary

**Characteristics:**

- Consistent repeat buyers
- May not spend as much as Champions but very reliable
- Strong retention

**Strategy:**

- Nurture into Champions (increase monetary)
- Cross-sell and bundle offers
- Appreciation campaigns
- Loyalty programs
- Ask for reviews

**Segment 3: Potential Loyalists (553, 551, 552, 541, 542)**

RFM: High Recency, Medium Frequency, High Monetary

**Characteristics:**

- Spending well but haven't established frequency yet
- Could become Champions with right nurturing

**Strategy:**

- Onboarding and education
- Encourage second and third purchases
- Subscription or repeat purchase incentives
- Share customer success stories
- Build buying habit

**Segment 4: New Customers (512, 511, 422, 421, 412)**

RFM: High Recency, Low Frequency, Low-Medium Monetary

**Characteristics:**

- Recently made first purchase
- Too early to predict loyalty
- Critical retention period

**Strategy:**

- Strong onboarding experience
- Product education
- Quick wins and early value delivery
- Second purchase campaigns (offer within 30 days)
- Build relationship before they go cold

**Segment 5: Promising (525, 524, 523, 522, 521)**

RFM: High Recency, Medium Frequency, Medium Monetary

**Characteristics:**

- Showing positive signals
- Recently engaged and coming back
- Room to grow in both frequency and monetary

**Strategy:**

- Increase purchase frequency (subscriptions, bundles)
- Cross-sell complementary products
- Limited-time offers to accelerate next purchase
- Build habit with consistent engagement

**Segment 6: Need Attention (535, 534, 443, 434, 343)**

RFM: Medium Recency, Medium-High Frequency, Medium Monetary

**Characteristics:**

- Used to be more engaged (recency declining)
- Still valuable but showing warning signs
- Risk of sliding into at-risk category

**Strategy:**

- Personalized reactivation campaigns
- Special offers or discounts
- "We miss you" messaging
- Surveys to understand declining engagement
- Prevent further decline

**Segment 7: About to Sleep (331, 321, 312, 221, 213)**

RFM: Medium-Low Recency, Low-Medium Frequency, Low-Medium Monetary

**Characteristics:**

- Engagement declining significantly
- May not have purchased in 60-90 days
- Entering danger zone

**Strategy:**

- Aggressive win-back campaigns
- Deep discounts or incentives
- Product recommendations based on past purchases
- Time-sensitive offers
- Last chance messaging

**Segment 8: At-Risk (255, 254, 245, 244, 253)**

RFM: Low Recency, High Frequency, High Monetary

**Characteristics:**

- Were Champions or Loyal, now declining
- Haven't purchased recently despite history of high value
- High-priority save attempts

**Strategy:**

- High-priority win-back (they are valuable)
- Personal outreach (call or email from human)
- Understand why they stopped (survey, feedback)
- Exclusive offers to return
- Competitive analysis (did they switch?)

**Segment 9: Cannot Lose Them (155, 154, 144, 214, 215)**

RFM: Very Low Recency, High Frequency, High Monetary

**Characteristics:**

- Your most valuable customers who have gone silent
- Critical to save - highest lifetime value
- Likely already churned or close to it

**Strategy:**

- Emergency intervention
- CEO or leadership outreach
- Massive incentive to return
- Win-back guarantee or trial period
- Exit interview to understand failure

**Segment 10: Hibernating (332, 322, 231, 241, 251)**

RFM: Low Recency, Low-Medium Frequency, Low-Medium Monetary

**Characteristics:**

- Long time since purchase
- Were never highly engaged
- Low value historically

**Strategy:**

- Low-cost reactivation only
- Generic email campaigns (don't over-invest)
- Seasonal or clearance offers
- If no response, deprioritize

**Segment 11: Lost (111, 112, 121, 131, 141)**

RFM: Very Low Recency, Low Frequency, Low Monetary

**Characteristics:**

- One-time or very infrequent buyers
- Long time since last purchase
- Likely churned permanently

**Strategy:**

- Minimal investment
- Automated low-cost reactivation attempts
- Exclude from expensive campaigns
- Focus acquisition on finding customers who don't become this segment

**RFM Segment Size and Value Analysis**

After segmenting, analyze distribution:

**Example distribution:**

| Segment | % of Customers | % of Revenue | Avg LTV | Churn Risk |
|---|---|---|---|---|
| Champions | 5% | 35% | $8,000 | Very Low |
| Loyal | 10% | 25% | $3,500 | Low |
| Potential Loyalists | 8% | 15% | $2,500 | Medium |
| New Customers | 15% | 8% | $800 | High |
| Promising | 12% | 10% | $1,200 | Medium |
| Need Attention | 10% | 5% | $700 | High |
| About to Sleep | 8% | 1% | $300 | Very High |
| At-Risk | 7% | 1% | $200 | Critical |
| Cannot Lose | 3% | <1% | $150 | Lost |
| Hibernating | 12% | <1% | $100 | Lost |
| Lost | 10% | <1% | $50 | Lost |

**Key insights:**

- Top 15% of customers (Champions + Loyal) drive 60% of revenue
- 40% of customers (bottom 4 segments) drive <3% of revenue
- Focus retention on top 25%, reactivation on middle 30%, minimal investment on bottom 45%

**RFM in Action: Campaign Examples**

**Campaign 1: Champions Appreciation**

- Segment: Champions (555, 554)
- Message: "You're in our top 5% - here's exclusive early access to our new product line"
- Offer: VIP early access, special gift, personalized note
- Goal: Retention, advocacy, testimonials

**Campaign 2: At-Risk Save**

- Segment: At-Risk (255, 254)
- Message: "We noticed you haven't shopped with us in a while. Here's 25% off to welcome you back"
- Offer: Significant discount, free shipping, extended return period
- Goal: Reactivation before full churn

**Campaign 3: New Customer Conversion**

- Segment: New Customers (512, 511)
- Message: "Thanks for your first purchase! Here's 15% off your second order (valid for 30 days)"
- Offer: Time-limited discount to drive second purchase
- Goal: Move from one-time to repeat customer

**Campaign 4: Loyalist Upsell**

- Segment: Loyal Customers (444, 435)
- Message: "You love [product A]. Customers who bought [A] also love [premium product B]"
- Offer: Product recommendation with small bundle discount
- Goal: Increase monetary value

**Campaign 5: Cannot Lose Emergency**

- Segment: Cannot Lose Them (155, 154)
- Message: Personal email from founder: "I noticed you haven't ordered in 6 months. Can you tell me what happened?"
- Offer: 50% off return + personal phone call
- Goal: Understand churn, attempt save

#### LESSON 3: Service-Based Business Segmentation Frameworks

RFM works brilliantly for e-commerce and subscription businesses with repeat transactional behavior.

But service businesses need different frameworks because the buying pattern is different.

Here is how to segment service businesses.

**Why RFM Doesn't Work for Services**

Service businesses have different characteristics:

**Differences:**

- **Longer sales cycles:** Projects may take months to close
- **Irregular purchase patterns:** Might only need services once per year or when specific trigger occurs
- **Relationship-based:** Success depends on relationship quality, not product features
- **Variable project sizes:** One project might be $5K, next might be $500K
- **Retention measured differently:** Not by repeat purchase frequency but by years as client

**Example:**

Law firm client might hire you once every 2 years for a major transaction. They are a great client (high value, loyal, low churn risk) but RFM would score them as low frequency.

Consulting client might have 10 small projects per year ($20K each) and one huge project every 3 years ($500K). Their annual spend varies wildly.

RFM would misclassify both. You need service-specific frameworks.

**Service Business Segmentation Framework 1: Project Value and Frequency**

**Dimension 1: Project Value**

Score clients based on average project size:

- Score 5: $100K+ average project
- Score 4: $50K-$99K average
- Score 3: $20K-$49K average
- Score 2: $5K-$19K average
- Score 1: <$5K average

*Adjust thresholds to your business*

**Dimension 2: Project Frequency**

Score clients based on projects per year (or 2 years for longer-cycle businesses):

- Score 5: 10+ projects per year
- Score 4: 5-9 projects per year
- Score 3: 2-4 projects per year
- Score 2: 1 project per year
- Score 1: <1 project per year

**Dimension 3: Relationship Tenure**

Score clients based on years as client:

- Score 5: 5+ years
- Score 4: 3-4 years
- Score 3: 1-2 years
- Score 2: <1 year
- Score 1: New client (first project)

**Create segments from combined scores:**

**Anchor Clients (5-5-5, 5-4-5, 4-5-5):**

- High value, high frequency, long tenure
- Your most important relationships
- Strategy: White-glove service, strategic account management, proactive outreach

**Core Clients (4-4-4, 4-3-4, 3-4-4):**

- Solid value, regular work, established relationship
- The backbone of your business
- Strategy: Consistent delivery, relationship maintenance, expansion opportunities

**Growth Clients (3-2-2, 4-1-1, 5-1-1):**

- Good value but new or infrequent
- Potential to become Core or Anchor
- Strategy: Prove value, build trust, create opportunities for more work

**Transactional Clients (2-1-1, 1-1-1):**

- Small, infrequent, new
- May never become repeat clients
- Strategy: Efficient delivery, low overhead, qualify future fit

**Service Business Segmentation Framework 2: Engagement and Retention**

Better for ongoing services (agencies, consulting, fractional executives):

**Dimension 1: Active Engagement**

Current activity level:

- Score 5: Active project in flight + pipeline discussion
- Score 4: Active project in flight
- Score 3: Between projects but regular contact
- Score 2: Between projects, sporadic contact
- Score 1: No active project, no contact in 90+ days

**Dimension 2: Retention Indicators**

Signals of satisfaction and likelihood to continue:

- Score 5: Extended contract, expanded scope, referrals sent
- Score 4: Renewed contract, positive feedback
- Score 3: Neutral signals, no complaints
- Score 2: Complaints or concerns raised, delays in payment
- Score 1: Threats to leave, payment issues, negative feedback

**Dimension 3: Strategic Value**

Beyond revenue, how strategic is this client:

- Score 5: Brand name client, massive referral source, industry leadership
- Score 4: Good brand association, some referrals, respected in industry
- Score 3: Solid client, no special strategic value
- Score 2: Unknown brand, no referral value
- Score 1: Potentially harmful to brand reputation

**Segments:**

**Strategic Partners (5-5-5, 5-5-4):**

- Active, loyal, strategic
- Worth over-investing in
- Strategy: Partnership mentality, co-innovation, thought leadership collaboration

**Healthy Actives (4-4-3, 5-4-3):**

- Currently engaged, satisfied, standard value
- Should maintain and grow
- Strategy: Consistent delivery, scope expansion, check-ins

**At-Risk Actives (4-2-3, 3-2-3):**

- Currently engaged but showing dissatisfaction signals
- Risk of churn
- Strategy: Address concerns, improve delivery, recover relationship

**Dormant Satisfied (3-4-3, 2-4-4):**

- Not currently active but historically happy
- Likely to return when need arises
- Strategy: Stay top of mind, provide value, periodic outreach

**Dormant At-Risk (2-2-2, 1-2-1):**

- Not currently active, signs of dissatisfaction
- May never return
- Strategy: Exit interview, minimal follow-up

**Service Business Segmentation Framework 3: Account Health Score**

Comprehensive scoring across multiple dimensions.

**Revenue Health (0-25 points):**

- Current annual value: score out of 10 (higher = more points)
- Growth trajectory: score out of 10 (increasing = 10, flat = 5, declining = 0)
- Payment reliability: score out of 5 (on-time = 5, late = 0)

**Relationship Health (0-25 points):**

- Decision-maker access: score out of 10 (direct access = 10, none = 0)
- Stakeholder satisfaction: score out of 10 (NPS>9 = 10, NPS<7 = 0)
- Communication frequency: score out of 5 (weekly+ = 5, monthly = 3, quarterly = 1)

**Engagement Health (0-25 points):**

- Active projects: score out of 10 (multiple = 10, one = 5, none = 0)
- Pipeline visibility: score out of 10 (12+ months = 10, 3-6 months = 5, none = 0)
- Responsiveness: score out of 5 (immediate = 5, slow = 0)

**Strategic Health (0-25 points):**

- Referral activity: score out of 10 (active referrer = 10, none = 0)
- Brand value: score out of 10 (high = 10, low = 0)
- Expansion potential: score out of 5 (high = 5, none = 0)

**Total: 0-100 points**

**Segments:**

- **Tier 1 (80-100):** Anchor accounts, highest priority
- **Tier 2 (60-79):** Strong accounts, maintain
- **Tier 3 (40-59):** Acceptable accounts, monitor
- **Tier 4 (20-39):** Weak accounts, improve or exit
- **Tier 5 (0-19):** Problem accounts, exit

**Segmentation by Client Lifecycle Stage**

Alternative approach: Segment by where client is in journey.

**Stage 1: Prospect (Not Yet Client)**

- In sales process
- Strategy: Close deal, set expectations

**Stage 2: Onboarding (First 90 Days)**

- New client, learning about you
- Strategy: Quick wins, build trust, prove value
- This is THE critical period for retention

**Stage 3: Active Growth (Year 1-2)**

- Expanding scope, deepening relationship
- Strategy: Deliver consistently, expand services, build advocates

**Stage 4: Mature Partnership (Year 3+)**

- Established, stable, predictable
- Strategy: Maintain satisfaction, prevent complacency, strategic collaboration

**Stage 5: Declining (Warning Signs)**

- Engagement dropping, dissatisfaction signals
- Strategy: Intervention, recovery plan, address issues

**Stage 6: Churned (No Longer Client)**

- Relationship ended
- Strategy: Exit interview, win-back if appropriate, learn from loss

Different strategies for each stage, even if they have similar revenue.

**Choosing the Right Framework for Your Service Business**

**Use Project Value and Frequency if:**

- You work on discrete projects
- Project size varies significantly
- Frequency is irregular but meaningful

**Use Engagement and Retention if:**

- You have ongoing retainer relationships
- Satisfaction and engagement are primary churn predictors
- Strategic value matters beyond revenue

**Use Account Health Score if:**

- You need comprehensive view across multiple factors
- You manage large accounts with multiple stakeholders
- You want quantifiable prioritization

**Use Lifecycle Stage if:**

- Tenure and maturity drive strategy
- Different stages need radically different approaches
- You want simple, clear segmentation

**Or combine approaches:**

Primary: Account Health Score (quantitative). Secondary: Lifecycle Stage (qualitative context).

This gives you both numerical prioritization and strategic context.
