SKEW function

SKEW calculates the asymmetry (skewness) of a distribution: positive means right-skewed, negative means left-skewed, near zero means symmetric.

=SKEW(number1, ...)

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Describe what you need. The generator will reach for SKEW where SKEW is the right tool, and tell you when it is not.

How to get a better answer
  • Name your columns by letter and by header: "column F (Net Value)" beats "the amount column".
  • State every condition, including the negatives — "not cancelled" changes the formula's shape.
  • Say where the data starts if it is not row 1, and whether it will grow.
  • Check the settings above match your spreadsheet: the wrong argument separator is a syntax error on your machine.

Arguments

How SKEW reads its arguments
number1requiredSKEW →
ArgumentRequiredDescription
number1RequiredA required numeric value; subsequent arguments are optional repeating numbers or ranges to include in the skewness calculation.
...RepeatingOptional repeating numeric values or cell ranges; SKEW requires at least 3 values total to compute skewness.

Returns

Returns a number (typically between -3 and +3) representing the distribution's skewness coefficient.

Availability

Excel: All · Google Sheets: Supported

Worked examples

1. Analyze revenue distribution across customer base

CustomerPlanMRRSignup DateChurn Date
AlicePro992024-01-15
BobBasic292024-02-012024-08-15
CarolPro992024-03-10
DaveEnterprise2992024-01-20
EveBasic292024-04-05
FrankPro992024-05-122024-09-01
GraceBasic292024-06-01
HenryEnterprise2992024-02-15
IrisPro992024-07-10
JackBasic292024-08-01
KatePro992024-08-15
LiamBasic292024-09-012024-09-10
MikeBusiness1992024-09-05
=SKEW(C2:C14)

Result: 0.547

The skewness of 0.547 indicates a moderately right-skewed distribution. Most customers are concentrated in the Basic ($29) and Pro ($99) tiers, while the few Enterprise ($299) customers pull the distribution's tail rightward. This pattern is typical for SaaS businesses where budget-conscious customers outnumber high-value accounts.

2. Compare skewness: active versus churned customers

CustomerPlanMRRStatus
AlicePro99Active
CarolPro99Active
DaveEnterprise299Active
EveBasic29Active
FrankPro99Churned
GraceBasic29Active
HenryEnterprise299Active
IrisPro99Active
JackBasic29Active
KatePro99Active
LiamBasic29Churned
MikeBusiness199Active
=SKEW(C2:C5,C7:C11,C13:C14)

Result: 0.618

Active customers show skewness of 0.618 versus 0.547 for all customers, revealing that churned accounts (Bob and Liam) were both in the low-cost Basic tier. Higher-value customers are stickier, making the active base slightly more right-skewed. This insight helps identify which customer segments need retention focus.

3. Detect when a customer segment has no variation

CustomerPlanMRR
AlicePro99
CarolPro99
FrankPro99
IrisPro99
KatePro99
=SKEW(99,99,99,99,99)

Result: #NUM!

All Pro customers have identical MRR ($99), creating zero variance in the dataset. SKEW returns #NUM! because skewness measures asymmetry, which cannot exist when all values are the same. This error signals that the customer segment is perfectly uniform—useful for confirming that a plan tier has consistent pricing with no variations.

Common errors

Which SKEW error are you seeing?
SKEW returned an error#NUM!
Ensure your range or argument list includes at least 3 numeric values. Expand the range or add more individual number arguments.
#VALUE!
Filter or clean your data to exclude text, empty strings, and errors. Use IFERROR to handle problematic cells, or reference only numeric columns.
#REF!
Check that all referenced cells and ranges are valid. Correct the cell references or re-select the data range using the range picker.
ErrorWhy it happensHow to fix it
#NUM!Fewer than 3 numeric values provided to SKEW. Skewness calculation requires a minimum of 3 data points to measure distribution asymmetry.Ensure your range or argument list includes at least 3 numeric values. Expand the range or add more individual number arguments.
#VALUE!The range or arguments contain non-numeric values such as text, dates, or error cells that cannot be included in the skewness calculation.Filter or clean your data to exclude text, empty strings, and errors. Use IFERROR to handle problematic cells, or reference only numeric columns.
#REF!One or more cell references in the SKEW formula point to deleted cells, invalid ranges, or sheets that no longer exist.Check that all referenced cells and ranges are valid. Correct the cell references or re-select the data range using the range picker.

Tips and when to use something else

  • Use MEDIAN or MODE.SNGL instead of SKEW if you need the center of the distribution; SKEW only describes shape, not location.
  • Positive skew (>0) indicates a right tail; negative skew (<0) indicates a left tail; values near 0 mean the distribution is roughly symmetric.
  • SKEW ignores empty cells but stops immediately if it encounters text or error values—clean your data before calculating.
  • Compare SKEW values across time periods or customer cohorts to track whether your distribution shape is becoming more or less balanced; this signals strategic shifts (e.g., moving upmarket or downmarket).

Related functions

AVERAGEAVERAGE calculates the arithmetic mean of numbers, ignoring text and logical values; returns a single numeric value representing the average.MEDIANMEDIAN returns the middle value in a sorted list of numbers, or the average of the two middle values if the count is even.MODE.SNGLReturns the most commonly appearing numeric value in a dataset, ideal for identifying the most frequent priority level, rating, or resolution time.LARGELARGE returns the k-th largest value in an array, where k=1 is the maximum, k=2 is the second-largest, and so on.SMALLSMALL returns the k-th smallest value from an array, where k=1 gives the minimum and higher k values give progressively larger results.

Frequently asked questions

What does a skewness value of 0.547 actually mean for my SaaS business?
A skew of 0.547 means your customer base is right-skewed: most customers cluster at lower price points (Basic/Pro), but a few high-value customers (Enterprise) pull the average revenue upward. This concentration pattern affects pricing, cash-flow predictability, and retention strategy. Left-skewed would suggest the opposite: most customers on premium plans with few bargain accounts.
How is SKEW different from AVERAGE or MEDIAN?
AVERAGE and MEDIAN tell you the central value of your data, while SKEW describes the shape—whether it's balanced or lopsided. A dataset with average MRR of $110 could have skew of 0.5 (right-skewed), -0.3 (left-skewed), or 0 (symmetric). All three metrics together paint the complete picture: where data clusters, how much it varies, and which direction it leans.
Why do I get #NUM! when I try SKEW(C2:C3)?
SKEW requires at least 3 values to calculate skewness meaningfully. With only 2 values, there's no distribution shape to measure—the formula returns #NUM!. Expand your range to include at least 3 rows of numeric data.
Can I use SKEW to monitor whether my customer base is becoming more or less balanced over time?
Yes. Calculate SKEW for different time periods (quarters, cohorts) and compare the values. Increasing skew means concentration is growing (fewer customers at each price point, wider gaps between tiers). Decreasing skew means your base is becoming more uniform. This metric helps you track whether your go-to-market strategy is shifting the customer composition intentionally or as an unintended side effect.

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Reviewed 2026-09-17