3. Prescriptive Analytics

Pricing and Revenue Management

Connect price, causal demand response, contribution, capacity and customer impact

Pricing and Revenue Management

Revenue is not the objective by default

At price £10, HarborMart sells 1,000 units with variable cost £6:

\text{contribution}=(10-6)(1{,}000)=£4{,}000.

At price £11, expected sales are 900:

(11-6)(900)=£4{,}500.

Revenue falls from £10,000 to £9,900 while contribution rises. The preferred metric depends on cost, capacity, repeat behaviour and strategic constraints.

Elasticity needs a causal interpretation

Point price elasticity is

\varepsilon=\frac{\partial Q}{\partial P}\frac{P}{Q}.

Historical correlation between high price and low quantity can mix season, stock, competitor activity and targeted markdowns. Pricing needs the demand response to an intervention, not only prediction conditional on past price.

Use randomised or credible quasi-experimental variation where lawful and feasible. Check spillovers: customers compare periods, stores and products; competitors react.

Capacity changes the question

With scarce delivery slots, the decision may combine:

price or fee by slot;
capacity protected for later high-value demand;
overbooking or no-show policy;
service and access constraints;
uncertainty in arrivals and cancellation.

Revenue management values the opportunity cost of consuming capacity now. A low current price can displace a higher-value later order; excessive protection can leave capacity unused.

A pricing experiment contract

Item	Example
unit	postcode cluster by week, not individual page view
treatment	£1 evening delivery-fee difference
primary outcome	contribution per eligible checkout
demand outcomes	purchase, slot, basket and substitution
guardrails	access, complaints, lateness and repeat use
interference	cross-postcode shopping and capacity spillover
horizon	immediate order plus repeat window

Price experimentation can raise fairness, consumer-protection and competition concerns. Obtain appropriate legal and ethics review; this course does not supply jurisdiction-specific legal advice.

Current organisational example

Uber’s 2024 investor update presents matching, routing, dispatch, pricing and incentives as one marketplace system. The teaching lesson is integration: evaluating one model metric cannot establish marketplace value or distributional impact.

Quick check

After a price increase, quantity falls 8% and contribution rises 5%. Can the company conclude long-run profit improved?

Answer

Not yet. Examine causal identification, customer substitution, retention, competitor response, capacity, cost changes, heterogeneity and the observation horizon. Short-run contribution is evidence, not the complete long-run objective.

Next: Causal Targeting

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Queueing and Simulation

Model congestion, variability and capacity when averages are not enough

Causal Targeting and Policy Learning

Target actions by incremental effect and value rather than untreated risk alone