What Is Customer Segmentation?
A business that treats all customers identically is spending money on the wrong people. A high-value customer who buys every month gets the same re-engagement email as someone who purchased once three years ago. A customer who only buys during sales receives the same full-price offer as someone who buys at any price. Segmentation fixes that by grouping customers whose behavior is similar enough that one tailored approach makes sense for the whole group.
The word "segmentation" covers a lot of ground. A segment can be as simple as "customers who spent more than $500 last year" or as detailed as "women aged 25–34 in urban postcodes who bought twice in the past 90 days." What determines how fine-grained a segment should be is not ambition but data: you can only cut as fine as the variables you actually have, and the statistics of those variables tell you where the natural breaks are.
Types of Customer Segmentation
Segmentation frameworks use different types of customer data depending on what a business knows and what question it is trying to answer. The four classical types are demographic, geographic, behavioral, and psychographic. Value-based and purchase behavior segmentation build on these by adding transaction data.
| Type | Variables Used | Statistical Measures | Best For |
|---|---|---|---|
| Demographic | Age, gender, income, occupation, education | Mean, median, frequency distribution, cross-tabulation | Broad audience targeting, product positioning |
| Geographic | Country, region, city, postcode, climate | Frequency counts, mode, grouped means by location | Location-based offers, regional pricing |
| Behavioral | Purchase frequency, session count, cart abandonment, loyalty | Mean, median, standard deviation, quartiles, percentiles | Retention campaigns, loyalty programs |
| Psychographic | Lifestyle, values, attitudes, interests | Frequency distributions, cross-tabulation with spend | Brand messaging, content strategy |
| Value-based | Total revenue, CLV, average order value, profit margin | Percentile ranks, quartiles, standard deviation, RFM scores | VIP programs, resource allocation, churn prevention |
| Purchase Behavior | Recency, frequency, monetary value, category mix | RFM scoring, means per segment, frequency distributions | Targeted promotions, win-back campaigns |
In practice, these types are not mutually exclusive. A SaaS company might combine behavioral data (login frequency) with value data (subscription tier) to create a segment called "engaged mid-tier users," then use geographic data to refine the message by time zone. The statistics that build each layer remain the same regardless of which combination a team chooses.
What Are Descriptive Statistics?
When a marketing team downloads 50,000 customer records, the raw data is a spreadsheet. The statistics turn it into a picture: the average customer spends $142 per year, half spend less than $89, the top quarter spends more than $280, and a small group of outliers spends over $1,500. That picture, built from four numbers, already suggests at least three segments before any further analysis begins.
The full treatment of these measures, with formulas and calculation walkthroughs, lives in the Statistics Fundamentals descriptive statistics guide. What follows here is a business-focused explanation of how each measure translates into a segmentation decision.
Key Descriptive Statistics for Customer Segmentation
Mean (Average) — Finding the Baseline Customer
The mean is the sum of all values divided by the number of values. In customer analytics, the mean purchase value or mean visit frequency gives you the "average customer" benchmark. Every other customer is then measured above or below that line.
x̄ = mean (average)
Σx = sum of all values
n = number of customers
A retail chain calculates mean annual spend across 10,000 customers: $342. That number becomes the boundary between "below average" and "above average" customers. A loyalty campaign might then focus on customers spending $150–$300 (below average but close), since they represent the most achievable upgrade. Use the mean calculator to compute this for your own data.
The mean is sensitive to outliers. If 9 customers spend $100 and one spends $10,000, the mean is $1,090 — a number that describes nobody. In skewed customer data, the median is almost always a more honest picture of the typical customer. Use the mean alongside the median, not instead of it.
Median — The Typical Customer, Unaffected by Extremes
The median is the middle value when all customers are ranked by the variable in question. Half of customers are above it, half below. Because outliers (very high spenders, one-time bulk buyers) do not affect the median the way they affect the mean, it gives a more accurate picture of what a typical customer actually looks like.
If the mean annual spend is $342 but the median is $89, that gap tells you something important: a small group of high spenders is pulling the mean up. The median reveals that most customers spend far less than the average implies. Segmenting by the median rather than the mean produces a more honest boundary between casual and regular buyers. See the median guide for the calculation steps and the mean, median, and mode calculator to apply this to your data.
Mode — Finding the Most Common Behavior
The mode is the value that appears most often in a dataset. In customer analytics, the mode of purchase frequency might reveal that the most common behavior is buying exactly once. That single number has strategic weight: if one-time buyers are the modal customer, the primary marketing challenge is converting a second purchase, not retaining loyal buyers.
Mode is especially useful for categorical variables. The most common product category purchased, the most common acquisition channel, the most common payment method — each of those modal values can anchor a segment. A business that finds its modal customer acquired through organic search will allocate SEO resources differently than one whose modal customer arrived through paid social.
Standard Deviation — Measuring Customer Variability
Standard deviation measures how spread out customer values are around the mean. A low standard deviation means customers are similar; a high one means they vary widely. That spread directly affects how many segments are worth creating.
σ = standard deviation
x = each customer's value
x̄ = mean
n = number of customers
Consider two subscription businesses. Business A's customers all pay $50/month with a standard deviation of $2: everyone is almost identical, segmentation adds little value. Business B's customers average $50/month with a standard deviation of $40: one standard deviation captures the range $10–$90, meaning there are genuinely different customer types worth treating differently. See the standard deviation guide and standard deviation calculator for worked examples.
Variance — The Raw Measure of Spread
Variance is the square of standard deviation. It is used less often in direct business communication because its units are squared (dollars squared, days squared) and are harder to interpret. Its main role in customer segmentation is as an input to more advanced analyses and as a diagnostic: high variance in a segment is a signal the segment may be too heterogeneous and should be split further.
A customer group with a mean CLV of $800 and a variance of $640,000 (standard deviation of $800) contains customers ranging from near-zero to very high value. That group is not really a single segment; the statistics are telling you to cut it. See the variance guide for the full explanation.
Range — The Full Span of Customer Behavior
Range is the simplest spread measure: the maximum value minus the minimum value. If customer spend ranges from $0 to $12,000, that range sets the boundaries of the segmentation problem. A narrow range suggests a fairly homogeneous base; a wide range tells you segments will look very different from each other and will likely need very different marketing approaches.
Quartiles — Splitting Customers into Four Tiers
Quartiles divide ranked customer data into four equal groups. Q1 (25th percentile) separates the bottom quarter. Q2 (50th percentile) is the median. Q3 (75th percentile) separates the top quarter. The interquartile range (IQR = Q3 − Q1) captures the middle 50% of customers.
Quartile-based segmentation is one of the most common and practical methods in marketing analytics because it requires no assumption about the shape of the data. A four-tier system based on quartiles — low, mid-low, mid-high, high — gives every business a starting point that is grounded in its actual data rather than arbitrary thresholds. See the full breakdown at the five-number summary guide and the IQR guide.
Percentiles — Pinpointing Customer Tiers with Precision
Percentiles generalize quartiles to any cut point. The 90th percentile of annual spend, for example, identifies the top 10% of customers by revenue contribution. Many businesses run VIP programs at the 90th or 95th percentile — the customers whose loss would disproportionately hurt revenue.
Percentiles are also the backbone of RFM scoring (covered in detail below): when you score a customer's recency as 5/5, you are placing them in the top 20th percentile of the recency distribution. The percentiles guide explains how to compute these for any dataset.
Frequency Distribution — Seeing the Full Shape of Your Customers
A frequency distribution counts how many customers fall into each value range (called a bin or class). Instead of a single summary number, it gives a complete picture of the data's shape: where most customers cluster, whether there are multiple peaks (suggesting distinct natural groups), and whether the distribution skews left or right.
In practice, a frequency distribution of annual spend might show that most customers cluster between $0 and $100 (casual buyers), a smaller group clusters between $200 and $500 (regular buyers), and a small tail extends above $1,000 (premium buyers). Those three natural clusters are segments waiting to be labeled. A distribution with three visible peaks is telling you there are three meaningfully different customer groups in the data, even before you run any formal analysis.
Cross-Tabulation — Comparing Segments on Multiple Variables
Cross-tabulation (crosstab) shows how two categorical variables relate to each other in a matrix. In customer analytics, a crosstab might show average spend by acquisition channel and age group simultaneously, revealing which combinations drive the most value.
| Acquisition Channel | Age 18–34 Mean Spend | Age 35–54 Mean Spend | Age 55+ Mean Spend |
|---|---|---|---|
| Organic Search | $124 | $198 | $163 |
| Paid Social | $87 | $142 | $91 |
| Email Referral | $203 | $312 | $289 |
| Direct / Loyal | $341 | $487 | $398 |
Illustrative example: a crosstab revealing that direct/loyal visitors aged 35–54 generate the highest mean spend ($487), pointing to a high-priority retention segment worth dedicated investment.
Why Descriptive Statistics Improve Customer Segmentation
Segmentation without statistics is guesswork. A marketing team might decide to target "big spenders" based on intuition, but without knowing where the natural break in spending is, they draw the boundary arbitrarily. Statistics replace the arbitrary line with one grounded in the actual data.
There are four direct ways descriptive statistics improve segmentation quality. First, they reveal where natural customer groups exist, rather than requiring the analyst to assume them in advance. Second, they identify outliers that distort the picture of the average customer. Third, they provide the thresholds for segment boundaries that come from the data itself (quartiles, percentiles) rather than from assumptions. Fourth, they create the scores and composite measures, such as RFM scores, that allow a customer's total behavior to be reduced to a single number that determines their segment membership.
Descriptive statistics answer one foundational question for every segmentation decision: how different are these customers from each other, and in what way? Mean and median describe the typical customer. Standard deviation and variance describe how much customers differ. Quartiles and percentiles draw the boundaries. Frequency distributions show the full picture. Together, they turn a spreadsheet into a segmentation strategy.
Real Example 1: E-Commerce Customer Segmentation
An online clothing retailer has 8,000 active customers. The team wants to create three segments based on annual spend to run differentiated email campaigns and loyalty incentives.
| Statistic | Annual Spend (USD) | Purchase Frequency (per year) |
|---|---|---|
| Mean | $198 | 3.2 purchases |
| Median | $112 | 2.0 purchases |
| Standard Deviation | $234 | 2.8 purchases |
| Q1 (25th percentile) | $42 | 1 purchase |
| Q3 (75th percentile) | $287 | 4 purchases |
| 90th Percentile | $512 | 8 purchases |
Building three spend-based segments from descriptive statistics
Observe the mean–median gap: Mean ($198) is 77% above the median ($112). This tells the team the distribution skews right — a small group of high spenders pulls the mean up. Segmenting by the mean would classify too many customers as "low" value.
Set segment boundaries using percentiles: Use the 33rd percentile (~$65) and 75th percentile ($287) as boundaries. These come from the data, not from arbitrary round numbers.
Label the segments: Occasional Buyers (below $65, ~33% of customers), Regular Buyers ($65–$287, ~42% of customers), High-Value Buyers (above $287, ~25% of customers).
Validate with standard deviation: The SD of $234 relative to the mean of $198 means the coefficient of variation is 118% — very high variability, confirming that these three segments genuinely behave differently and deserve separate treatment.
✓ Result: Three data-grounded segments with clear boundaries. High-Value Buyers ($287+) get a VIP loyalty program. Regular Buyers ($65–$287) get frequency-incentive emails ("buy twice more this month for 15% off"). Occasional Buyers (<$65) get reactivation campaigns with a low-barrier offer.
Real Example 2: Subscription Business Segmentation
A project management SaaS company has 3,200 active subscribers on monthly plans ranging from $15 to $149. The team wants to identify which customers are at risk of cancelling based on product engagement data.
| Engagement Metric | Retained Users (n=2,800) | Churned Users (n=400) |
|---|---|---|
| Mean monthly logins | 18.4 | 3.1 |
| Median monthly logins | 16.0 | 2.0 |
| Standard deviation of logins | 9.2 | 2.9 |
| Mean features used per month | 6.3 | 1.4 |
| Median subscription length (months) | 14 | 3 |
Using descriptive statistics to define an at-risk segment
Compare means across retained vs churned groups: Retained users log in 18.4 times per month on average; churned users averaged 3.1 logins before cancelling. The mean difference (15.3 logins) is enormous relative to the retained group's standard deviation (9.2). A customer at 3.1 mean logins is more than 1.5 standard deviations below the retained mean — a strong at-risk signal.
Set the at-risk threshold from the retained group's distribution: Mean logins (18.4) minus one standard deviation (9.2) = 9.2. Current customers averaging fewer than 9 logins per month fall below one standard deviation of the healthy range.
Cross-validate with features used: At-risk customers use 1.4 features vs. 6.3 for retained customers. Customers with both low logins (<9/month) and low feature use (<2 features) form the highest-priority retention segment.
✓ Result: The at-risk segment (below 9 logins/month and below 2 features used) receives a proactive retention sequence: a check-in call from customer success, a personalized feature walkthrough, and a 30-day plan extension offer. This segment was invisible before the statistics made it visible.
Real Example 3: Retail Store Customer Analysis
A mid-size fashion retailer wants to identify its highest-value customers, its occasional buyers, and its lapsed customers using 12 months of transaction data across 5,500 loyalty card holders.
| Segment | Customer Count | Mean Annual Spend | Mean Visit Frequency | Days Since Last Purchase |
|---|---|---|---|---|
| Champions (Top 10%) | 550 | $1,240 | 14.2 visits | 12 days avg |
| Regular Buyers | 1,650 | $387 | 5.6 visits | 38 days avg |
| Occasional Buyers | 2,200 | $118 | 2.1 visits | 74 days avg |
| At-Risk / Lapsed | 1,100 | $89 (prior year) | 0.8 visits | 142 days avg |
How the segments above were calculated from raw transaction data
Compute descriptive statistics for the full dataset: Overall mean annual spend = $289, median = $158, SD = $312, 90th percentile = $780. The large gap between mean and median confirms a right-skewed distribution with a high-spending minority.
Define "Champions" using the 90th percentile: Customers above the 90th percentile ($780+) represent the top 10% by spend. Their mean spend ($1,240) is 4.3 times the overall mean — they deserve a dedicated high-value program.
Define "At-Risk" using recency: The overall mean days since last purchase is 61. Customers more than two standard deviations above this (61 + 2×41 = 143 days) are the lapsed group. The 142-day average for At-Risk customers confirms the threshold is calibrated correctly.
Use frequency distributions to confirm segment separation: A histogram of annual spend shows three visible peaks at approximately $100, $375, and $1,200 — exactly where the segment boundaries were drawn. The data confirmed the intuition.
✓ Result: Champions receive exclusive early access and personal shopping events. Regular Buyers receive a frequency loyalty card. Occasional Buyers receive seasonal "we miss you" promotions with a small incentive. At-Risk / Lapsed customers receive a targeted win-back campaign with a time-limited offer. Each message fits the segment's actual behavior rather than a generalization.
RFM Analysis and Descriptive Statistics
RFM is one of the oldest and most durable customer segmentation methods in direct marketing, with origins traced to database marketing in the 1960s and still used by direct-to-consumer brands, e-commerce platforms, and CRM teams worldwide. Its durability comes from its simplicity: three numbers, grounded entirely in transaction data, produce an actionable customer ranking without requiring machine learning or predictive modelling.
The statistics that power RFM are not complicated. Each dimension is scored by dividing the customer base into quintiles (five equal groups) using percentile ranks. A customer in the top 20% for recency scores a 5; the bottom 20% scores a 1. The same logic applies to frequency and monetary value. The three scores combine into a composite RFM score, and combinations of scores map to segment labels.
| Score | Recency (Days Since Last Purchase) | Frequency (Purchases per Year) | Monetary (Annual Spend USD) |
|---|---|---|---|
| 5 (Top 20%) | 0–14 days | 12+ purchases | $500+ |
| 4 (60th–80th pct) | 15–30 days | 7–11 purchases | $250–$499 |
| 3 (40th–60th pct) | 31–60 days | 4–6 purchases | $100–$249 |
| 2 (20th–40th pct) | 61–90 days | 2–3 purchases | $40–$99 |
| 1 (Bottom 20%) | 91+ days | 1 purchase | <$40 |
| RFM Score | Segment Label | Description | Recommended Action |
|---|---|---|---|
| 555 | Champion | Bought recently, buys often, spends the most | Reward them, ask for reviews, early access |
| 554, 545, 455 | Loyal Customer | High frequency and spend, slightly less recent | Upsell, loyalty program, exclusive offers |
| 511, 512, 521 | New Customer | Bought recently but only once or twice | Onboarding sequence, second purchase incentive |
| 155, 255, 354 | At-Risk | Was a high spender but hasn't returned recently | Win-back email, personalized offer, phone call |
| 111, 112, 121 | Lost | Bought long ago, rarely, and spent little | Low-cost reactivation or retire the contact |
The percentile thresholds in the table above are calibrated to the specific business's data using the descriptive statistics percentile method. A business with a different distribution of recency, frequency, or spend will have different threshold numbers — the scoring method stays the same, but the values come from the business's own data rather than from a template. This is the core reason RFM cannot simply be copied from one business to another without recalculation.
RFM Score Calculator
Enter a customer's recency, frequency, and annual spend below to calculate their RFM score and segment using typical e-commerce thresholds. Adjust the percentile boundaries to match your own data distribution.
🧮 RFM Customer Segment Calculator
Recency
Frequency
Monetary
This calculator uses typical e-commerce RFM thresholds. For accurate scoring in your business, replace the thresholds with your own percentile boundaries calculated from your actual customer data using the percentiles guide.
Descriptive Statistics vs Predictive Analytics in Segmentation
Descriptive statistics tell you what happened and how customers currently divide. Predictive analytics uses past patterns to forecast what will happen next, such as which customers are likely to churn or make a purchase. The two approaches answer different questions and work best together rather than in competition.
| Property | 📊 Descriptive Statistics | 🔮 Predictive Analytics |
|---|---|---|
| Question answered | Who are my customers now, and how do they differ? | What will each customer do next? |
| Data required | Current and historical transaction data | Historical data plus model training and validation |
| Complexity | Mean, median, SD, percentiles — accessible to any analyst | Machine learning, regression, or survival analysis |
| Speed | Results in hours with a spreadsheet or SQL | Days to weeks to build, train, and validate a model |
| Interpretability | High — every threshold is explainable to a non-technical stakeholder | Lower — some models (deep learning) are difficult to explain |
| When to use | First-pass segmentation, audience building, campaign targeting | Churn prediction, next-best-offer, CLV forecasting |
| Typical starting point | Yes — almost every segmentation project starts here | No — requires sufficient historical data and analytical maturity |
Most businesses start with descriptive segmentation and graduate to predictive models as their data volume and analytical capability grow. The segments created through descriptive statistics also serve as labels for training predictive models: if descriptive statistics identify your "at-risk" segment, that label becomes the target variable a churn prediction model learns to forecast.
Tools for Customer Segmentation with Descriptive Statistics
| Tool | Best For | Key Statistics Available | Consideration |
|---|---|---|---|
| Excel / Google Sheets | Small teams, initial analysis, ad hoc projects | AVERAGE, MEDIAN, STDEV, QUARTILE, PERCENTILE, COUNTIF, pivot tables | No automation; manual refresh required; limited at scale |
| SQL (any database) | Mid-to-large datasets in a data warehouse | AVG, MEDIAN, STDDEV, NTILE, PERCENTILE_CONT, GROUP BY | Requires SQL fluency; results must be exported for visualization |
| Python (pandas) | Data teams, automation, custom scoring | describe(), quantile(), std(), value_counts(), crosstab() | Requires programming; most flexible option for custom RFM |
| R (tidyverse) | Statistical teams, academic rigor | summary(), sd(), quantile(), table(), ggplot2 histograms | Strong visualization; steeper learning curve than Python for beginners |
| Tableau / Power BI | Visual exploration, stakeholder dashboards | Calculated fields for mean, median, SD; built-in percentile bins | Best for presenting results; less suited for raw statistical calculation |
| HubSpot / Salesforce | CRM-based segmentation, marketing automation | Segment filters by spend, frequency, last contact date | Limited statistical depth; suitable for applying pre-built segments |
| Google Analytics 4 | Web behavior segmentation, cohort analysis | Averages, distributions for session and revenue data | Behavioral data only; no CRM or purchase data by default |
For hands-on calculation of the statistics in this guide, the descriptive statistics calculator at Statistics Fundamentals handles mean, median, mode, standard deviation, variance, quartiles, and percentiles in one tool. The mean calculator and standard deviation calculator handle those individual measures separately if you need one at a time.
Common Mistakes in Customer Segmentation Statistics
| Mistake | What Goes Wrong | What To Do Instead |
|---|---|---|
| Relying on the mean alone | In skewed customer data, the mean misrepresents most customers. A $50 mean spend in a dataset with a $200 outlier misleads segment design. | Always compute mean and median together. If they differ by more than 20%, the distribution skews and the median is the better central measure for segmentation. |
| Ignoring variability within segments | Two customers in the same segment can have very different behavior if the segment's standard deviation is high. The segment looks homogeneous on paper but isn't. | Check standard deviation within each proposed segment. A high CV (SD / mean > 0.5) is a signal the segment is too broad and should be split. |
| Over-segmentation | Creating 15 segments when you only have resources to run 3 differentiated campaigns means most segments are unused. Statistical rigor without operational follow-through wastes the analysis. | Match the number of segments to the number of distinct actions you can execute. Three well-executed segment strategies beat twelve ignored ones. |
| Setting thresholds arbitrarily | Drawing segment lines at round numbers ($100, $500, $1,000) ignores the actual distribution and may cut through natural customer clusters. | Use quartiles, percentiles, or frequency distribution peaks to find natural boundaries in your data. Where the data breaks is where segments should break. |
| Small sample sizes | A segment of 30 customers produces unreliable statistics. The mean and standard deviation fluctuate too much to trust, and outliers have an outsized effect. | As a practical floor, aim for at least 100 customers per segment before treating the descriptive statistics as stable. For tightly targeted campaigns, 200–500 is safer. |
| Never refreshing segments | A customer who was in the "at-risk" segment six months ago might now be your top buyer — or truly lapsed. Static segments become inaccurate as customer behavior changes. | Rebuild or refresh segment assignments on a regular cadence: monthly for behavioral segments, quarterly for demographic ones. The statistics that define segments should be recalculated from current data. |
Step-by-Step Segmentation Framework
The following workflow converts raw customer data into actionable segments using descriptive statistics at each stage. It applies to any industry, any dataset size, and any segmentation type.
Phase 1: Data Collection
- Identify which customer variables are available and reliable
- Confirm data covers a meaningful time window (at least 6–12 months)
- Remove duplicate records and test for completeness
- Define which metric will be the primary segmentation variable
Phase 2: Statistical Analysis
- Calculate mean, median, and mode for primary variables
- Compute standard deviation and variance to measure spread
- Generate quartiles and key percentiles (25th, 50th, 75th, 90th)
- Build a frequency distribution to identify natural clusters
- Use cross-tabulation for multi-variable comparisons
Phase 3: Segment Design and Validation
- Set segment boundaries at percentile breaks or distribution peaks
- Verify each segment has sufficient size (>100 customers minimum)
- Check within-segment standard deviation to confirm homogeneity
- Name segments using behavior, not internal codes
- Map each segment to a distinct marketing or product action
- Schedule a refresh cadence (monthly or quarterly)
Marketing Analytics Glossary
| Term | Plain-English Definition | Role in Customer Segmentation |
|---|---|---|
| Customer Segmentation | Dividing a customer base into groups based on shared characteristics | The core goal — every statistical method in this guide serves it |
| Descriptive Statistics | Summary numbers that describe a dataset's center and spread | The toolkit that identifies where segment boundaries belong |
| Mean | The arithmetic average: sum of all values divided by count | Baseline spend or frequency benchmark; sensitive to outliers |
| Median | The middle value in a ranked dataset | Better central measure than mean for skewed customer data |
| Mode | The most frequently occurring value | Reveals the most common customer behavior or product choice |
| Standard Deviation | How much individual values spread around the mean | Measures within-segment homogeneity; high SD = segment too broad |
| Variance | Standard deviation squared | Diagnostic for segment quality; used in advanced analytics |
| Quartiles | Values that split ranked data into four equal groups | Natural four-tier segment boundaries grounded in actual data |
| Percentiles | Values that split ranked data at any percentage point | Foundation of RFM scoring and VIP tier identification |
| Frequency Distribution | Count of customers falling into each value range | Reveals natural clusters that define organic segment structure |
| Customer Lifetime Value (CLV) | Total revenue a customer is expected to generate over their relationship with a business | Key variable for value-based segmentation |
| Average Order Value (AOV) | Total revenue divided by number of orders in a period | Segment variable separating basket-size tiers |
| RFM Analysis | Scoring customers on Recency, Frequency, and Monetary value | Most widely used descriptive segmentation framework in direct marketing |
| Behavioral Segmentation | Grouping customers by what they do: purchases, visits, logins | Often the most actionable segmentation type for e-commerce and SaaS |
| Cohort Analysis | Tracking behavior of customers acquired in the same time period over time | Reveals whether newer customer cohorts retain at different rates |
| Marketing Analytics | Using data and statistical methods to measure and improve marketing performance | The broader discipline that customer segmentation serves |
Frequently Asked Questions
Key sources and further reading: Hughes, A. (1994). Strategic Database Marketing — foundational text on RFM analysis · Harvard Business Review — "The Value of Keeping the Right Customers" · Khan Academy — Summarizing Quantitative Data (free educational resource) · OpenIntro Statistics — open-access textbook covering descriptive statistics · Google Analytics — behavioral segmentation documentation