The questions to answer:

What's the elasticity of signups to price?
What's the LTV of a premium user at $40 vs. hypothetically $25 or $60?
What's the share cannibalization — premium users who would have paid for other services (Sales Navigator, Recruiter) if they'd been pushed toward them.

Without A/B testing pricing (high-risk at LinkedIn's scale), you rely on:

Geographic pricing variation. LinkedIn already charges different prices in different countries. Analyze: how does conversion vary with price (in PPP-adjusted terms)? This gives you a demand curve.
Cohort / vintage analysis. When prices have changed historically, how did conversion / retention respond? Careful to control for product improvements at the same time.
Survey-based willingness-to-pay. Ask current subscribers (and lapsed / non-subscribers) at what price they'd sign up / cancel. Stated preference is noisy but directional.
Competitor benchmark. Indeed Resume, Monster, industry-specific premium tools — what do comparable services charge?

The decision framework:

If price is too high: conversion is low, churn among new sign-ups is high (they joined, realized value didn't match, left). LTV per acquirable user is low.
If price is too low: leaving money on the table — willing-to-pay users pay less than they would.
If price is right: elasticity-weighted profit is maximized. Pricing power is roughly proportional to service differentiation.

A likely finding:

"Geographic pricing elasticity suggests LinkedIn Premium is priced near the revenue-maximizing point in developed markets. In emerging markets, a 30-50% discount would lift conversions faster than it would erode margin, net-positive on revenue. Segmented pricing is the next move."

Second-order considerations:

Bundle dynamics. Should Premium and Sales Navigator be bundled? Unbundled?
Tier structure. Does a cheaper tier ($15/month with fewer features) bring in users who wouldn't pay $40 but would pay $15? Or does it cannibalize $40-tier?
Annual vs. monthly. Annual commits stabilize revenue but at a discount; monthly has higher churn but higher flexibility.

Constraints that prevent easy experimentation:

Customer uproar. A/B testing price publicly generates PR risk. Users talk.
Fairness concerns. Same user seeing different prices is legally fraught in some regions.
Competitor visibility. Testing a higher price tips off competitors to reduce their price.

Therefore: pricing decisions at scale lean on quasi-experimental, market research, and geographic variation rather than direct A/B.

What senior candidates add:

Reference Netflix's price A/B tests — they've done it publicly and it's become industry reference for how to run them carefully. Geographic randomization, pre-announced tier changes, clear communication.
Consider freemium psychology. LinkedIn Premium lives alongside a robust free tier. The delta — what's paid-only — matters as much as the price.
Discuss pricing as a signal. A low price can signal low quality; a higher price can signal premium positioning. Optimal price isn't always revenue-maximizing; can be strategy-defining.

Is LinkedIn Premium worth the price?

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