Pricing Psychology & Revenue Models

Dynamic Discounting: Real-Time Offer Engines

Real‑time offer engines tune price or perks per context but need guardrails to avoid unfairness. This quiz covers eligibility rules, exploration vs. exploitation, and profit‑based stop points.

Which design keeps real‑time discounts compliant and defensible?

Pre‑declared eligibility rules and audit logs of offers

Charging different prices by surname

Unlimited price swings per user

Hidden criteria and no recordkeeping

Transparent criteria plus traceability protects fairness and supports regulatory review.

Multi‑armed bandits add value over A/B tests mainly by ______.

avoiding any statistical trade‑offs

eliminating control groups entirely

shifting traffic to better options while still exploring

guaranteeing optimal prices instantly

Bandits balance learning and earning, allocating more to stronger performers over time.

A safe stopping rule for discount depth is when marginal profit of extra discounting approaches ______.

CPA

session count

zero

inventory age

Stop where additional discounting no longer adds profit; beyond that, value is destroyed.

To avoid training customers to wait for discounts, engines should enforce ______.

cadence and frequency guardrails with randomness

guaranteed daily flash sales

stackable codes without limits

permanent site‑wide coupons

Guardrails and some randomness keep timing unpredictable and protect reference prices.

A defensible personalization signal for first‑order discounts is ______.

device brand alone

scroll depth alone

new‑customer status verified at checkout

ZIP code alone

Status‑based and verifiable signals are more defensible than proxies that risk bias.

Real‑time promo budgets should be governed on the objective of ______.

incremental profit after cannibalization

gross revenue only

impressions served

app sessions

Evaluate net profit, not volume; cannibalization can make discounts value‑destructive.

Exploration temperature in an engine primarily controls ______.

cookie lifespan

how often the system tries alternatives vs. exploiting winners

ad viewability

database replication

Higher exploration increases learning; too much hurts earnings, too little stalls learning.

A fairness‑preserving practice when offers vary is to ______.

publish the types of offers and the conditions that qualify users

rotate prices minute‑by‑minute without notice

use dark patterns to hide comparisons

refuse to disclose any logic

Disclosure builds trust and reduces perceived price discrimination risk.

Inventory‑aware discounting should ______ when stock is scarce.

widen discounts automatically

tighten discounts or switch to value‑add perks

ignore inventory completely

freeze the site

Scarcity raises opportunity cost; deep discounts during scarcity erode margin unnecessarily.

To prevent bias creep, engines should run routine ______ checks on segments and outcomes.

fairness and disparate‑impact

session replay heatmaps only

font kerning

image alt‑text only

Bias audits test whether certain groups are unfairly impacted by offer allocation.

Starter

You get the engine basics—add profit‑based stop rules and documented eligibility.

Solid

Nice—dial exploration, inventory awareness, and cadence guardrails for durable gains.

Expert!

Excellent—you’re balancing learning, fairness, and profit like a mature offer platform.

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