A weighted average accounts for the relative importance of each value in a dataset.
Unlike a simple average, it weights each value by its proportion — so a large segment
contributes more to the result than a small one.
Weighted average formula
Weighted Average = Σ(value × weight) / Σ(weights)
Common business applications
Use case
Value
Weight
Portfolio return
Asset return %
Asset value $
Blended cost of capital
Cost %
Capital amount $
Weighted NPS (Net Promoter Score)
NPS per cohort
Cohort size
COGS by product
Unit cost
Units sold
Market share-weighted price
Price $
Market share %
Weighted average vs. simple average
If you sell 100 units at $10 and 10 units at $50, the simple average price is
($10 + $50) / 2 = $30. But the weighted average (by units) is (100×$10 + 10×$50)
/ 110 = $13.64 — a far more accurate representation of your actual revenue per unit.
When to Use a Weighted Average (vs. Simple Average)
A guide to when weighted averages give more accurate results than simple averages — with examples from finance, school grades, and business metrics.
A weighted average is appropriate whenever observations have different levels of
importance or represent different-sized groups. Here is when it matters.
When weighted average ≠ simple average
Exam grades: a final exam worth 40% of your grade should not count the same as
a quiz worth 5%. Weighted average: (Quiz × 5% + Final × 40% + ...) / total weight.
Portfolio returns: if 80% of your portfolio is in fund A (returned 12%) and 20%
in fund B (returned 5%), your portfolio return is 0.8×12% + 0.2×5% = 10.6%,
not (12% + 5%) / 2 = 8.5%.
Customer satisfaction scores: if enterprise customers (30% of revenue) rate you
8/10 and SMB customers (70% of revenue) rate you 6/10, the revenue-weighted NPS
is 0.3×8 + 0.7×6 = 6.6, not (8+6)/2 = 7.
Average selling price across segments: a product sold at $200 to 100 customers
and $800 to 10 customers has an average ASP of (100×$200 + 10×$800) / 110 = $254,
not ($200 + $800) / 2 = $500.
When simple average is fine
Use a simple average when every observation carries equal importance and represents
an equivalent "unit" — e.g. the average of 5 A/B test conversion rates where each
test ran on the same sample size.
Weighted Average Cost in Accounting: Inventory and COGS Calculation
How to use the weighted average cost method for inventory valuation — formula, example, comparison to FIFO and LIFO, and when each method applies.
The weighted average cost (WAC) method is one of three accepted inventory valuation
methods under both GAAP and IFRS. Here is how it works in practice.
The weighted average cost formula
WAC per unit = Total cost of inventory ÷ Total units available
Applied perpetually (after each purchase) or periodically (at end of period).
Example
Beginning inventory: 100 units at $10 = $1,000
Purchase 1: 200 units at $12 = $2,400
Purchase 2: 150 units at $15 = $2,250
Total: 450 units, $5,650 total cost
WAC = $5,650 ÷ 450 = $12.56 per unit
If you sell 200 units: COGS = 200 × $12.56 = $2,511
Ending inventory: 250 × $12.56 = $3,139
WAC vs FIFO vs LIFO
Method
COGS in rising prices
Ending inventory
Taxes
FIFO
Lower (older cost)
Higher (recent cost)
Higher
LIFO
Higher (recent cost)
Lower (older cost)
Lower (US GAAP only)
WAC
Middle
Middle
Middle
Note: LIFO is not permitted under IFRS. US companies can use it for tax deferral.
When to use WAC
WAC is preferred when: inventory is homogeneous (all units are interchangeable),
purchase prices fluctuate, and you want to smooth COGS rather than show peaks/troughs.