MKTG90026 Chap.8 Pricing Metrics and Demand Elasticity
Pricing Metrics and Demand Elasticity
Pricing metrics compare the price actually achieved with relevant customer, order and competitor bases. This chapter distinguishes weighted from unweighted average selling price, AOV from ARPU, and price premium from several price indexes.
It then calculates price elasticity of demand, preserving both the negative direction and absolute responsiveness, before connecting volume and revenue effects with contribution margin and the limits of an observed price range.
What this chapter covers
- 01
Cost-plus pricing
- 02
Weighted and unweighted ASP
- 03
AOV and ARPU
- 04
Price premium benchmarks
- 05
Relative and competitive indexes
- 06
Price elasticity of demand
Worked example · free
Calculate price elasticity
- 1Quantity changes by negative 800 divided by 10,000, or negative 8%.
- 1Price changes by 2 divided by 20, or positive 10%.
- 1Elasticity equals negative 8% divided by 10%.
- 1The result is negative 0.8, which is inelastic in absolute value.
Key terms
- Average Selling Price
- Realised revenue per unit, normally weighted by the actual sales mix.
- Price Premium
- Own price minus benchmark price, divided by the named benchmark price.
- Demand Elasticity
- Percentage change in quantity demanded divided by percentage change in price.
Pricing Metrics and Demand Elasticity FAQ
Why can weighted and unweighted average price differ?
An unweighted average gives every product or segment equal influence, while weighted ASP reflects the units actually sold. A premium product with most of the volume can pull weighted ASP well above the simple average without any item-level price change.
What does negative elasticity mean?
It records the usual inverse relationship between price and quantity. Use the absolute value to classify responsiveness: above one is elastic and below one is inelastic over the observed range. The estimate is not a universal demand law.
Does inelastic demand guarantee a price increase is best?
No. A higher price may lift revenue within the observed range, but profit depends on contribution, competitive response, customer lifetime effects and how elasticity changes across segments or larger moves. Join the demand result to unit economics.
Why should elasticity be treated as local evidence?
The estimate reflects the observed price range, period, segments and competitive conditions. A larger move or different market can produce another response, so test revenue and contribution sensitivities before extrapolating the relationship.
Exam move
Practise identifying the denominator in ASP, AOV and ARPU, then calculate a premium against three different named benchmarks. For elasticity, label old and new price and quantity, preserve the sign and compare old with new revenue and contribution. Finish with the observed-range boundary so a local estimate is not extended without evidence. Start with a denominator card.
Average selling price divides revenue by units, average order value divides revenue by orders, and average revenue per user divides revenue by users. Construct one dataset where users place several orders and orders contain several units, then calculate all three measures. Change the product mix without changing item prices and explain why weighted ASP can move.
Compare the weighted result with a simple average to see how volume weights alter managerial meaning. For premiums and indexes, name the benchmark in words before writing numbers. A premium to a category average, a competitor and a prior-period price are different statements even when the arithmetic form is similar.
Verify whether the result should be a dollar difference, a percentage premium or an index centred on 100. Never report that a product has a premium without identifying the reference price, customer group, package size and period. Approach elasticity with an old-new table for price, quantity, revenue and contribution.
Calculate the percentage change in quantity over the percentage change in price, preserve the negative sign for direction and use the absolute value only when classifying responsiveness. Predict the sign before calculating. Then calculate old and new revenue because elastic or inelastic demand describes quantity responsiveness over the observed range, not the full profit effect.
Add unit contribution to test whether the revenue conclusion survives costs. Practise midpoint and simple percentage conventions only as specified by the question or course materials, and never switch methods silently. Use the same base consistently for price and quantity changes.
Distinguish a local historical estimate from a structural law: competitor moves, customer segments, seasonality, stock availability and the size of the price change may alter response. A positive correlation between price and quantity in observational data may reflect mix or demand shifts rather than an upward-sloping demand curve. For timed drills, write benchmark, formula, sign, unit and decision in that order.
Cover the options until you know whether the magnitude should be above or below one and whether revenue should rise or fall under the simplified case. Keep an error log for ASP versus AOV, unweighted averages, unnamed benchmarks, sign removal and revenue treated as profit. Finish each answer with the observed price range and one sensitivity or experiment that would improve confidence before a broader price move.
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